Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a nove...Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a novel control strategy to improve the controllability of unmanned aircraft in challenging wind conditions.First,the equations of motion for the aircraft are reformulated as a system of stochastic differential equations,which are subsequently transformed into a deterministic form.By modeling turbulence as a Gaussian random process and incorporating it directly into the control system,the proposed method proactively compensates for the adverse effects of turbulence.The transformation is achieved using semi-invariant techniques.Second,the control problem is formulated as an optimization task,aiming to minimize the deviation between the actual and desired turn characteristics,specifically the angular velocity.Finally,a new numerical method with proven global convergence is employed to compute the optimal autopilot parameters.Simulation results using a medium-range unmanned aircraft model under continuous turbulent gusts demonstrate that the proposed method significantly outperforms existing approaches,ensuring both stability and precision in turbulent wind conditions.展开更多
Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those co...Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those conditions by(i)unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT(metaheuristics and learning-based trackers)and to advanced inverter controls(adaptive/MPC and grid-forming),(ii)standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair,reproducible comparisons,and(iii)framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios.We identify persistent gaps—fragmented partial-shading benchmarks,limited low-SCR testing,and scarce field-grade validation—and compile a quantitative synthesis:global soiling typically reduces annual production by≈3%–5%,and hybrid/learning MPPT frequently report≈99%tracking efficiency under PSC in simulation/HIL studies.To demonstrate practical relevance,we validate the framework on a seeded scenario library:DRL trackers achieve medianηMPPT≈0.996 with t95≈0.19 s and Hybrid trackers≈0.992/0.26 s,outperforming Metaheuristics(≈0.984/0.42 s);at SCR=2.5,grid-forming control raises VRI from~0.78(tuned GFL)to~0.95 while keeping THD within 2.5%–3.2%,with all stacks meeting IEEE-1547.1 Category-II ride-through.The resulting taxonomy,standards-aligned reporting,and open seeds provide a replicable basis for comparable,grid-relevant benchmarking and clear guidance for real-world design and operations.展开更多
Photovoltaic(PV)systems in the field operate under complex,uncertain conditions rapid irradiance ramps,partial shading,temperature swings,surface soiling,and weak-grid disturbances including off-nominal frequency and ...Photovoltaic(PV)systems in the field operate under complex,uncertain conditions rapid irradiance ramps,partial shading,temperature swings,surface soiling,and weak-grid disturbances including off-nominal frequency and voltage distortion that degrade energy yield and power quality.We propose a drift-aware,power-quality-constrained MPPT framework that co-optimizes MPPT,PLL,and current-loop gains under stochastic frequency drift,while enforcing IEEE-519 limits(per-order Ih/IL and TDD)during optimization.Unlike energy-only or THD-only methods,the design target integrates PQ constraints into the objective and is validated across calibrated drift scenarios with explicit per-order and TDD reporting.Operating scenarios are calibrated to Cameroon’s Southern Interconnected Grid and city-specific profiles(Douala/Yaoundé),combining measured-style irradiance/temperature traces,partial-shading patterns,and stochastic frequency drift up to±0.8 Hz with synthetic contingencies.Across a 30-scenario campaign,the proposed controller achievesηMPPT=99.3%–99.6%(vs.98.6%Incremental Conductance and 97.8%Perturb-and-Observe),lowers DC-link ripple by 35%–48%,reduces oscillatory PCC power by≈41%,maintains THD≤2.5%(5%limit)and PF≥0.99,and shortens irradiance-step settling from 85–110 ms to 50–65 ms.Sensitivity to PLL bandwidth shows a broad optimum(≈60–90 Hz)with minimum THD/ripple,and ablations confirm that explicit drift weighting is pivotal to ripple and THD suppression without sacrificing yield.The approach is controller-agnostic,firmware-deployable,and generalizes to other converter-interfaced renewables;we outline a short hardware-/HIL-validation path for adoption in Sub-Saharan grids.展开更多
In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
In active noise control,the optimal deployment of secondary sources is a critical factor influencing the noise reduction performance due to the spatial inhomogeneity of the sound field.Traditional methods,which rely o...In active noise control,the optimal deployment of secondary sources is a critical factor influencing the noise reduction performance due to the spatial inhomogeneity of the sound field.Traditional methods,which rely on finite element analysis to model the sound field,are accurate but computationally intensive,leading to high costs in solving the deployment optimization problem.To address this issue,this paper proposes an expensive optimization method for secondary source deployment based on Interior Point Method-assisted Differential Evolution with Weibull distribution(IPMDEW).During the optimization process,a Kriging model is employed to construct a response surface,i.e.,a surrogate model,of the objective function.The surrogate model is used for the initial evaluation of the population,while the finite element model is utilized to verify promising individuals.A surrogate model update algorithm based on k-means clustering is designed to iteratively refine the model and enhance its accuracy.The IPMDEW algorithm utilizes the Weibull distribution-based weighted differential evolution for global exploration and switches to the gradient-based interior point method for refined local optimization when the population approaches convergence.The results demonstrate Kriging surrogate-assisted optimization method for secondary source deployment reduces the optimization time by 85.79%,i.e.,by 347.64 h,significantly improving optimization efficiency.Furthermore,the accuracy of the Kriging model continuously improves during the optimization process.The proposed method achieves a noise reduction of 58.32 dB,ensuring high optimization accuracy while substantially increasing efficiency.展开更多
The augmented evolution equation is established under the framework of the Variation Evolving Method(VEM)that seeks optimal solutions by solving the transformed Initial-Value Problems(IVPs).To improve the numerical pe...The augmented evolution equation is established under the framework of the Variation Evolving Method(VEM)that seeks optimal solutions by solving the transformed Initial-Value Problems(IVPs).To improve the numerical performance,its compact form is developed herein.Through replacing the states and costates variation evolution with that of the controls,the dimension-reduced Evolution Partial Differential Equation(EPDE)only solves the control variables along the variation time to get the optimal solution,and the initial conditions for the definite solution may be arbitrary.With this equation,the scale of the resulting IVPs,obtained via the semi-discrete method,is significantly reduced and they may be solved with common Ordinary Differential Equation(ODE)integration methods conveniently.Meanwhile,the state and the costate dynamics share consistent stability in the numerical computation and this avoids the intrinsic numerical difficulty as in the indirect methods.Numerical examples are solved and it is shown that the compact form evolution equation outperforms the primary form in the precision,and the efficiency may be higher for the dense discretization.Actually,it is uncovered that the compact form of the augmented evolution equation is a continuous realization of the Newton type iteration mechanism.展开更多
Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering,particularly when both precision and efficiency must be ensured.Conventional control methods are o...Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering,particularly when both precision and efficiency must be ensured.Conventional control methods are often effective for stabilization but may not directly optimize long-term performance.To address this limitation,this study develops an integrated framework that combines optimal control principles with reinforcement learning for a single-link robotic manipulator.The proposed scheme adopts an actor–critic structure,where the critic network approximates the value function associated with the Hamilton–Jacobi–Bellman equation,and the actor network generates near-optimal control signals in real time.This dual adaptation enables the controller to refine its policy online without explicit system knowledge.Stability of the closed-loop system is analyzed through Lyapunov theory,ensuring boundedness of the tracking error.Numerical simulations on the single-link manipulator demonstrate that themethod achieves accurate trajectory followingwhile maintaining lowcontrol effort.The results further showthat the actor–critic learning mechanism accelerates convergence of the control policy compared with conventional optimization-based strategies.This work highlights the potential of reinforcement learning integrated with optimal control for robotic manipulators and provides a foundation for future extensions to more complex multi-degree-of-freedom systems.The proposed controller is further validated in a physics-based virtual Gazebo environment,demonstrating stable adaptation and real-time feasibility.展开更多
With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power fl...With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.展开更多
Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft conti...Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.展开更多
Dear Editor,In this letter,we focus on the algebraic relationship between the coefficient matrices and the solution of the stochastic algebraic Riccati equation.It is revealed that,if the coefficient matrices are in a...Dear Editor,In this letter,we focus on the algebraic relationship between the coefficient matrices and the solution of the stochastic algebraic Riccati equation.It is revealed that,if the coefficient matrices are in an algebra,then the solution(and also the control gain in many cases)is also in the same algebra.The main result is verified by a numerical simulation.展开更多
The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the exis...The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.展开更多
Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,th...Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.展开更多
With the growing demand for offshore energy,deepwater drilling has become a vital technology in petroleum engineering.However,conventional drilling systems often face limitations such as delayed bottomhole pressure re...With the growing demand for offshore energy,deepwater drilling has become a vital technology in petroleum engineering.However,conventional drilling systems often face limitations such as delayed bottomhole pressure response and low control precision,particularly under narrow pressure window and complex formation conditions.To address these challenges,Dual-layer Pipe dual-gradient drilling(DGD)technology has been introduced,utilizing a dual-pipe structure and downhole lift pumps to extend the pressure control range.Despite these advantages,current DGD systems lack fast and precise bottomhole pressure control due to their reliance on indirect flow-based methods.This study proposes a bottomhole pressure control method based on backpressure regulation using a hybrid fuzzy-PID control strategy.A dynamic pressure calculation model is developed for the Dual-layer Pipe DGD system,incorporating coupling among choke valve opening,surface backpressure,and bottomhole pressure.The fuzzy-PID controller adjusts valve operation in real-time based on pressure deviation and its rate of change,improving response speed and control accuracy.Simulink-based simulations demonstrate that the proposed system achieves rapid pressure regulation with an overshoot below 5%and steady-state error under 0.12%.Compared to conventional PID control,the fuzzy-PID system shows superior adaptability to pressure variations.This research enhances the theoretical foundation of backpressure control in deepwater DGD operations and provides a practical approach for improving safety and efficiency in complex drilling environments.展开更多
Antarctic telescopes,especially those located at Dome A,face significant reliability challenges owing to the extremely harsh working environment,among which the reliability of the control system is critical in ensurin...Antarctic telescopes,especially those located at Dome A,face significant reliability challenges owing to the extremely harsh working environment,among which the reliability of the control system is critical in ensuring stable operation.This paper describes various factors affecting the reliability of Antarctic telescopes,as well as the challenges of reliability improvement.Combined with the development of Antarctic telescopes and the experience of Antarctic scientific expeditions,we introduce,in detail,the optimization strategy for reliability enhancement,including the hardware layer,software layer,modular design to facilitate maintenance,and reliability management.The current status of the Antarctic Survey Telescope(AST3)is also briefly introduced,along with future development plans.We aim to provide ideas for the reliability design of Antarctic telescopes and provide technical support for the development of future Antarctic telescopes.展开更多
Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimi...Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimization sub-problems within the MPC framework,eliminating the dynamic constraints in solving the optimal control problem and enhancing the convergence performance of the algorithm.Moreover,this method can repeatedly perform trajectory optimization calculations at a high frequency,achieving timely correction of the optimal control command.Numerical simulations demonstrate that the method can satisfy the requirements of rapid computation and reliability for the MAV system when considering uncertainties and perturbations.展开更多
This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certifie...This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.展开更多
For optimization algorithms,the most important consideration is their global optimization performance.Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target pro...For optimization algorithms,the most important consideration is their global optimization performance.Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster speed.For stochastic optimization algorithms based on population search methods,the search speed and solution quality are always contradictory.Suppose that the random range of the group search is larger;in that case,the probability of the algorithm converging to the global optimal solution is also greater,but the search speed will inevitably slow.The smaller the random range of the group search is,the faster the search speed will be,but the algorithm will easily fall into local optima.Therefore,our method is intended to utilize heuristic strategies to guide the search direction and extract as much effective information as possible from the search process to guide an optimized search.This method is not only conducive to global search,but also avoids excessive randomness,thereby improving search efficiency.To effectively avoid premature convergence problems,the diversity of the group must be monitored and regulated.In fact,in natural bird flocking systems,the distribution density and diversity of groups are often key factors affecting individual behavior.For example,flying birds can adjust their speed in time to avoid collisions based on the crowding level of the group,while foraging birds will judge the possibility of sharing food based on the density of the group and choose to speed up or escape.The aim of this work was to verify that the proposed optimization method is effective.We compared and analyzed the performances of five algorithms,namely,self-organized particle swarm optimization(PSO)-diversity controlled inertia weight(SOPSO-DCIW),self-organized PSO-diversity controlled acceleration coefficient(SOPSO-DCAC),standard PSO(SPSO),the PSO algorithm with a linear decreasing inertia weight(SPSO-LDIW),and the modified PSO algorithm with a time-varying acceleration constant(MPSO-TVAC).展开更多
This study proposes a system for biometric access control utilising the improved Cultural Chicken Swarm Optimization(CCSO)technique.This approach mitigates the limitations of conventional Chicken Swarm Optimization(CS...This study proposes a system for biometric access control utilising the improved Cultural Chicken Swarm Optimization(CCSO)technique.This approach mitigates the limitations of conventional Chicken Swarm Optimization(CSO),especially in dealing with larger dimensions due to diversity loss during solution space exploration.Our experimentation involved 600 sample images encompassing facial,iris,and fingerprint data,collected from 200 students at Ladoke Akintola University of Technology(LAUTECH),Ogbomoso.The results demonstrate the remarkable effectiveness of CCSO,yielding accuracy rates of 90.42%,91.67%,and 91.25%within 54.77,27.35,and 113.92 s for facial,fingerprint,and iris biometrics,respectively.These outcomes significantly outperform those achieved by the conventional CSO technique,which produced accuracy rates of 82.92%,86.25%,and 84.58%at 92.57,63.96,and 163.94 s for the same biometric modalities.The study’s findings reveal that CCSO,through its integration of Cultural Algorithm(CA)Operators into CSO,not only enhances algorithm performance,exhibiting computational efficiency and superior accuracy,but also carries broader implications beyond biometric systems.This innovation offers practical benefits in terms of security enhancement,operational efficiency,and adaptability across diverse user populations,shaping more effective and resource-efficient access control systems with real-world applicability.展开更多
Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal ene...Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal energy source for future deep space exploration.A whole system model of the space nuclear reactor consisting of the reactor neutron kinetics,reactivity control,reactor heat transfer,heat exchanger,and thermoelectric converter was developed.In addition,an electrical power control system was designed based on the developed dynamic model.The GRS method was used to quantitatively calculate the uncertainty of coupling parameters of the neutronics,thermal-hydraulics,and control system for the space reactor.The Spearman correlation coefficient was applied in the sensitivity analysis of system input parameters to output parameters.The calculation results showed that the uncertainty of the output parameters caused by coupling parameters had the most considerable variation,with a relative standard deviation<2.01%.Effective delayed neutron fraction was most sensitive to electrical power.To obtain optimal control performance,the non-dominated sorting genetic algorithm method was employed to optimize the controller parameters based on the uncertainty quantification calculation.Two typical transient simulations were conducted to test the adaptive ability of the optimized controller in the uncertainty dynamic system,including 100%full power(FP)to 90%FP step load reduction transient and 5%FP/min linear variable load transient.The results showed that,considering the influence of system uncertainty,the optimized controller could improve the response speed and load following accuracy of electrical power control,in which the effectiveness and superiority have been verified.展开更多
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金co-supported by the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province(No.22kftk01)the Key Research and Development Program of Heilongjiang,China(No.2024ZXJ07B05)the National Natural Science Foundation of China(No.92471103)。
文摘Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a novel control strategy to improve the controllability of unmanned aircraft in challenging wind conditions.First,the equations of motion for the aircraft are reformulated as a system of stochastic differential equations,which are subsequently transformed into a deterministic form.By modeling turbulence as a Gaussian random process and incorporating it directly into the control system,the proposed method proactively compensates for the adverse effects of turbulence.The transformation is achieved using semi-invariant techniques.Second,the control problem is formulated as an optimization task,aiming to minimize the deviation between the actual and desired turn characteristics,specifically the angular velocity.Finally,a new numerical method with proven global convergence is employed to compute the optimal autopilot parameters.Simulation results using a medium-range unmanned aircraft model under continuous turbulent gusts demonstrate that the proposed method significantly outperforms existing approaches,ensuring both stability and precision in turbulent wind conditions.
文摘Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those conditions by(i)unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT(metaheuristics and learning-based trackers)and to advanced inverter controls(adaptive/MPC and grid-forming),(ii)standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair,reproducible comparisons,and(iii)framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios.We identify persistent gaps—fragmented partial-shading benchmarks,limited low-SCR testing,and scarce field-grade validation—and compile a quantitative synthesis:global soiling typically reduces annual production by≈3%–5%,and hybrid/learning MPPT frequently report≈99%tracking efficiency under PSC in simulation/HIL studies.To demonstrate practical relevance,we validate the framework on a seeded scenario library:DRL trackers achieve medianηMPPT≈0.996 with t95≈0.19 s and Hybrid trackers≈0.992/0.26 s,outperforming Metaheuristics(≈0.984/0.42 s);at SCR=2.5,grid-forming control raises VRI from~0.78(tuned GFL)to~0.95 while keeping THD within 2.5%–3.2%,with all stacks meeting IEEE-1547.1 Category-II ride-through.The resulting taxonomy,standards-aligned reporting,and open seeds provide a replicable basis for comparable,grid-relevant benchmarking and clear guidance for real-world design and operations.
基金Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project(Grant No.RGP2/587/46).
文摘Photovoltaic(PV)systems in the field operate under complex,uncertain conditions rapid irradiance ramps,partial shading,temperature swings,surface soiling,and weak-grid disturbances including off-nominal frequency and voltage distortion that degrade energy yield and power quality.We propose a drift-aware,power-quality-constrained MPPT framework that co-optimizes MPPT,PLL,and current-loop gains under stochastic frequency drift,while enforcing IEEE-519 limits(per-order Ih/IL and TDD)during optimization.Unlike energy-only or THD-only methods,the design target integrates PQ constraints into the objective and is validated across calibrated drift scenarios with explicit per-order and TDD reporting.Operating scenarios are calibrated to Cameroon’s Southern Interconnected Grid and city-specific profiles(Douala/Yaoundé),combining measured-style irradiance/temperature traces,partial-shading patterns,and stochastic frequency drift up to±0.8 Hz with synthetic contingencies.Across a 30-scenario campaign,the proposed controller achievesηMPPT=99.3%–99.6%(vs.98.6%Incremental Conductance and 97.8%Perturb-and-Observe),lowers DC-link ripple by 35%–48%,reduces oscillatory PCC power by≈41%,maintains THD≤2.5%(5%limit)and PF≥0.99,and shortens irradiance-step settling from 85–110 ms to 50–65 ms.Sensitivity to PLL bandwidth shows a broad optimum(≈60–90 Hz)with minimum THD/ripple,and ablations confirm that explicit drift weighting is pivotal to ripple and THD suppression without sacrificing yield.The approach is controller-agnostic,firmware-deployable,and generalizes to other converter-interfaced renewables;we outline a short hardware-/HIL-validation path for adoption in Sub-Saharan grids.
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
文摘In active noise control,the optimal deployment of secondary sources is a critical factor influencing the noise reduction performance due to the spatial inhomogeneity of the sound field.Traditional methods,which rely on finite element analysis to model the sound field,are accurate but computationally intensive,leading to high costs in solving the deployment optimization problem.To address this issue,this paper proposes an expensive optimization method for secondary source deployment based on Interior Point Method-assisted Differential Evolution with Weibull distribution(IPMDEW).During the optimization process,a Kriging model is employed to construct a response surface,i.e.,a surrogate model,of the objective function.The surrogate model is used for the initial evaluation of the population,while the finite element model is utilized to verify promising individuals.A surrogate model update algorithm based on k-means clustering is designed to iteratively refine the model and enhance its accuracy.The IPMDEW algorithm utilizes the Weibull distribution-based weighted differential evolution for global exploration and switches to the gradient-based interior point method for refined local optimization when the population approaches convergence.The results demonstrate Kriging surrogate-assisted optimization method for secondary source deployment reduces the optimization time by 85.79%,i.e.,by 347.64 h,significantly improving optimization efficiency.Furthermore,the accuracy of the Kriging model continuously improves during the optimization process.The proposed method achieves a noise reduction of 58.32 dB,ensuring high optimization accuracy while substantially increasing efficiency.
基金supported by the National Nature Science Foundation of China under Grant No.11902332。
文摘The augmented evolution equation is established under the framework of the Variation Evolving Method(VEM)that seeks optimal solutions by solving the transformed Initial-Value Problems(IVPs).To improve the numerical performance,its compact form is developed herein.Through replacing the states and costates variation evolution with that of the controls,the dimension-reduced Evolution Partial Differential Equation(EPDE)only solves the control variables along the variation time to get the optimal solution,and the initial conditions for the definite solution may be arbitrary.With this equation,the scale of the resulting IVPs,obtained via the semi-discrete method,is significantly reduced and they may be solved with common Ordinary Differential Equation(ODE)integration methods conveniently.Meanwhile,the state and the costate dynamics share consistent stability in the numerical computation and this avoids the intrinsic numerical difficulty as in the indirect methods.Numerical examples are solved and it is shown that the compact form evolution equation outperforms the primary form in the precision,and the efficiency may be higher for the dense discretization.Actually,it is uncovered that the compact form of the augmented evolution equation is a continuous realization of the Newton type iteration mechanism.
基金supported in part by the National Science and Technology Council under Grant NSTC 114-2221-E-027-104.
文摘Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering,particularly when both precision and efficiency must be ensured.Conventional control methods are often effective for stabilization but may not directly optimize long-term performance.To address this limitation,this study develops an integrated framework that combines optimal control principles with reinforcement learning for a single-link robotic manipulator.The proposed scheme adopts an actor–critic structure,where the critic network approximates the value function associated with the Hamilton–Jacobi–Bellman equation,and the actor network generates near-optimal control signals in real time.This dual adaptation enables the controller to refine its policy online without explicit system knowledge.Stability of the closed-loop system is analyzed through Lyapunov theory,ensuring boundedness of the tracking error.Numerical simulations on the single-link manipulator demonstrate that themethod achieves accurate trajectory followingwhile maintaining lowcontrol effort.The results further showthat the actor–critic learning mechanism accelerates convergence of the control policy compared with conventional optimization-based strategies.This work highlights the potential of reinforcement learning integrated with optimal control for robotic manipulators and provides a foundation for future extensions to more complex multi-degree-of-freedom systems.The proposed controller is further validated in a physics-based virtual Gazebo environment,demonstrating stable adaptation and real-time feasibility.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(J2024162).
文摘With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.
基金supported in part by the Innovation and Technology Commission of Hong Kong,China(ITS/136/20,ITS/234/21,MHP/096/22,ITS/235/22)Multi-Scale Medical Robotics Center,InnoHK,China(8312051)+1 种基金Research Grants Council(RGC) of Hong Kong,China(CUHK 14217822,CUHK14207823,AoE/E-407/24-N)The Chinese University of Hong Kong(CUHK) Direct Grant。
文摘Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.
文摘Dear Editor,In this letter,we focus on the algebraic relationship between the coefficient matrices and the solution of the stochastic algebraic Riccati equation.It is revealed that,if the coefficient matrices are in an algebra,then the solution(and also the control gain in many cases)is also in the same algebra.The main result is verified by a numerical simulation.
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.
基金received funding from the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633)2023 University Student Innovation and Entrepreneurship Training Program(202311463009Z)+1 种基金Changzhou Science and Technology Support Project(CE20235045)Open Project of Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology(2021JSSPD12).
文摘Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.
基金the Sichuan Provincial Key R&D Program(Regional Innovation Coop-eration Project 2025YFHZ0306)Open Fund(PLN 2022-46)of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University)Special Support for Sichuan Postdoctoral Research Projects.
文摘With the growing demand for offshore energy,deepwater drilling has become a vital technology in petroleum engineering.However,conventional drilling systems often face limitations such as delayed bottomhole pressure response and low control precision,particularly under narrow pressure window and complex formation conditions.To address these challenges,Dual-layer Pipe dual-gradient drilling(DGD)technology has been introduced,utilizing a dual-pipe structure and downhole lift pumps to extend the pressure control range.Despite these advantages,current DGD systems lack fast and precise bottomhole pressure control due to their reliance on indirect flow-based methods.This study proposes a bottomhole pressure control method based on backpressure regulation using a hybrid fuzzy-PID control strategy.A dynamic pressure calculation model is developed for the Dual-layer Pipe DGD system,incorporating coupling among choke valve opening,surface backpressure,and bottomhole pressure.The fuzzy-PID controller adjusts valve operation in real-time based on pressure deviation and its rate of change,improving response speed and control accuracy.Simulink-based simulations demonstrate that the proposed system achieves rapid pressure regulation with an overshoot below 5%and steady-state error under 0.12%.Compared to conventional PID control,the fuzzy-PID system shows superior adaptability to pressure variations.This research enhances the theoretical foundation of backpressure control in deepwater DGD operations and provides a practical approach for improving safety and efficiency in complex drilling environments.
基金supported by the National Natural Science Foundation of China (12303089, 11973065)the Jiangsu Funding Program for Excellent Postdoctoral Talent (2022ZB449)the Polar Research Institute of China (PRIC) for their support and help with the Antarctic telescope project
文摘Antarctic telescopes,especially those located at Dome A,face significant reliability challenges owing to the extremely harsh working environment,among which the reliability of the control system is critical in ensuring stable operation.This paper describes various factors affecting the reliability of Antarctic telescopes,as well as the challenges of reliability improvement.Combined with the development of Antarctic telescopes and the experience of Antarctic scientific expeditions,we introduce,in detail,the optimization strategy for reliability enhancement,including the hardware layer,software layer,modular design to facilitate maintenance,and reliability management.The current status of the Antarctic Survey Telescope(AST3)is also briefly introduced,along with future development plans.We aim to provide ideas for the reliability design of Antarctic telescopes and provide technical support for the development of future Antarctic telescopes.
基金supported by the National Defense Basic Scientific Research Program(JCKY2021603B030)the National Natural Science Foundation of China(62273118,12150008)the Natural Science Foundation of Heilongjiang Province(LH2022F023).
文摘Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimization sub-problems within the MPC framework,eliminating the dynamic constraints in solving the optimal control problem and enhancing the convergence performance of the algorithm.Moreover,this method can repeatedly perform trajectory optimization calculations at a high frequency,achieving timely correction of the optimal control command.Numerical simulations demonstrate that the method can satisfy the requirements of rapid computation and reliability for the MAV system when considering uncertainties and perturbations.
基金supported in part by the National Science and Technology Major Project(2022ZD0119902)the National Natural Science Foundation of China(52471372,623B2018,62203015,62233001)+4 种基金the Liaoning Revitalization Leading Talents Program(XLYC2402054)the Key Basic Research of Dalian(2023JJ11CG008)the Fundamental Research Funds for the Central Universities(3132023508)the Collaborative Research Fund of Hong Kong Research Grants Council(C1013-24G)the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University(2023YBPY005).
文摘This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.
文摘For optimization algorithms,the most important consideration is their global optimization performance.Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster speed.For stochastic optimization algorithms based on population search methods,the search speed and solution quality are always contradictory.Suppose that the random range of the group search is larger;in that case,the probability of the algorithm converging to the global optimal solution is also greater,but the search speed will inevitably slow.The smaller the random range of the group search is,the faster the search speed will be,but the algorithm will easily fall into local optima.Therefore,our method is intended to utilize heuristic strategies to guide the search direction and extract as much effective information as possible from the search process to guide an optimized search.This method is not only conducive to global search,but also avoids excessive randomness,thereby improving search efficiency.To effectively avoid premature convergence problems,the diversity of the group must be monitored and regulated.In fact,in natural bird flocking systems,the distribution density and diversity of groups are often key factors affecting individual behavior.For example,flying birds can adjust their speed in time to avoid collisions based on the crowding level of the group,while foraging birds will judge the possibility of sharing food based on the density of the group and choose to speed up or escape.The aim of this work was to verify that the proposed optimization method is effective.We compared and analyzed the performances of five algorithms,namely,self-organized particle swarm optimization(PSO)-diversity controlled inertia weight(SOPSO-DCIW),self-organized PSO-diversity controlled acceleration coefficient(SOPSO-DCAC),standard PSO(SPSO),the PSO algorithm with a linear decreasing inertia weight(SPSO-LDIW),and the modified PSO algorithm with a time-varying acceleration constant(MPSO-TVAC).
基金supported by Ladoke Akintola University of Technology,Ogbomoso,Nigeria and the University of Zululand,South Africa.
文摘This study proposes a system for biometric access control utilising the improved Cultural Chicken Swarm Optimization(CCSO)technique.This approach mitigates the limitations of conventional Chicken Swarm Optimization(CSO),especially in dealing with larger dimensions due to diversity loss during solution space exploration.Our experimentation involved 600 sample images encompassing facial,iris,and fingerprint data,collected from 200 students at Ladoke Akintola University of Technology(LAUTECH),Ogbomoso.The results demonstrate the remarkable effectiveness of CCSO,yielding accuracy rates of 90.42%,91.67%,and 91.25%within 54.77,27.35,and 113.92 s for facial,fingerprint,and iris biometrics,respectively.These outcomes significantly outperform those achieved by the conventional CSO technique,which produced accuracy rates of 82.92%,86.25%,and 84.58%at 92.57,63.96,and 163.94 s for the same biometric modalities.The study’s findings reveal that CCSO,through its integration of Cultural Algorithm(CA)Operators into CSO,not only enhances algorithm performance,exhibiting computational efficiency and superior accuracy,but also carries broader implications beyond biometric systems.This innovation offers practical benefits in terms of security enhancement,operational efficiency,and adaptability across diverse user populations,shaping more effective and resource-efficient access control systems with real-world applicability.
基金supported by the National Natural Science Foundation of China(12305185)Natural Science Foundation of Hunan Province,China(No.2023JJ50122)+1 种基金International Cooperative Research Project of the Ministry of Education,China(No.HZKY20220355)Scientific Research Foundation of the Education Department of Hunan Province,China(No.22A0307).
文摘Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal energy source for future deep space exploration.A whole system model of the space nuclear reactor consisting of the reactor neutron kinetics,reactivity control,reactor heat transfer,heat exchanger,and thermoelectric converter was developed.In addition,an electrical power control system was designed based on the developed dynamic model.The GRS method was used to quantitatively calculate the uncertainty of coupling parameters of the neutronics,thermal-hydraulics,and control system for the space reactor.The Spearman correlation coefficient was applied in the sensitivity analysis of system input parameters to output parameters.The calculation results showed that the uncertainty of the output parameters caused by coupling parameters had the most considerable variation,with a relative standard deviation<2.01%.Effective delayed neutron fraction was most sensitive to electrical power.To obtain optimal control performance,the non-dominated sorting genetic algorithm method was employed to optimize the controller parameters based on the uncertainty quantification calculation.Two typical transient simulations were conducted to test the adaptive ability of the optimized controller in the uncertainty dynamic system,including 100%full power(FP)to 90%FP step load reduction transient and 5%FP/min linear variable load transient.The results showed that,considering the influence of system uncertainty,the optimized controller could improve the response speed and load following accuracy of electrical power control,in which the effectiveness and superiority have been verified.
