Facing the economic challenges of significant frequency regulation wear and tear on thermal power units and short energy storage lifespan in thermal-energy storage combined systems participating in grid primary freque...Facing the economic challenges of significant frequency regulation wear and tear on thermal power units and short energy storage lifespan in thermal-energy storage combined systems participating in grid primary frequency regulation(PFR),this paper proposes a novel hybrid energy storage system(HESS)control strategy based on Newton-Raphson optimization algorithm(NRBO)-VMD and a fuzzy neural network(FNN)for PFR.In the primary power allocation stage,the high inertia and slow response of thermal power units prevent them from promptly responding to the high-frequency components of PFR signals,leading to increased mechanical stress.To address the distinct response characteristics of thermal units and HESS,an NRBO-VMD based decomposition method for PFR signals is proposed,enabling a flexible system response to grid frequency deviations.Within the HESS,an adaptive coordinated control strategy and a State of Charge(SOC)self-recovery strategy are introduced.These strategies autonomously adjust the virtual inertia and droop coefficients based on the depth of frequency regulation and the real-time SOC.Furthermore,a FNN is constructed to perform secondary refinement of the internal power distribution within the HESS.Finally,simulations under various operational conditions demonstrate that the proposed strategy effectively mitigates frequent power adjustments of the thermal unit during PFR,adaptively achieves optimal power decomposition and distribution,maintains the flywheel energy storage’s SOC within an optimal range,and ensures the long-term stable operation of the HESS.展开更多
Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles,reducing system reliability and posing significant challenges to driving safety.To enhance the sys...Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles,reducing system reliability and posing significant challenges to driving safety.To enhance the system resilience,this paper proposes a novel robust fuzzy fault-tolerant control strategy that incorporates the adaptive event-trigger(AET)mechanism to realize stable,reliable,and precise lane-keeping control in the presence of multiple system uncertainties and probabilistic faults.First,to capture the uncertain and time-varying nature of tire cornering stiffness,an effective Takagi-Sugeno(T-S)fuzzy tire model is developed.Then,by employing the distribution-based probabilistic approach,two sets of unrelated random variables,random sensor and actuator faults in the control system,are modeled.Next,to improve communication efficiency and address ineluctable network-induced delays,an AET control framework with a well-designed triggering condition is established.Subsequently,a robust fuzzy output feedback fault-tolerant lane-keeping controller that satisfies the H∞per-formance is designed by using the Lyapunov-Krasovski functional method.Furthermore,the mean-square ex-ponential stability of the closed-loop system is rigorously guaranteed.Finally,real-time simulations based on Carsim/Simulink co-simulation platform under dynamic driving conditions demonstrate the feasibility and ef-fectiveness of the proposed control strategy.展开更多
To address the finite-time tracking control problem for fractional-order nonlinear systems(FONSs) with actuator faults and external disturbance,a novel strategy of the finite-time adaptive fuzzy fault-tolerant control...To address the finite-time tracking control problem for fractional-order nonlinear systems(FONSs) with actuator faults and external disturbance,a novel strategy of the finite-time adaptive fuzzy fault-tolerant controller is presented in this paper by utilizing the finite-time stability theory and fractional-order dynamic surface control scheme combined with backstepping method.A new lemma is developed for analyzing the finite-time stability of FONSs in terms of fractional differential inequality,which modifies some existing results.Fuzzy logic systems are adopted to identify unknown nonlinear characteristics in FONS.In order to compensate for the influence of unknown external disturbance and estimation error for fuzzy logic systems,an auxiliary function is employed to estimate the upper bound of parameters online.Furthermore,a global coordinate transformation is first introduced initially to decouple the fractional-order dynamic system of a specific class of underactuated single-link flexible manipulator systems,thereby transforming it into lower triangular systems.Simulation analyses and experimental results verify the feasibility and effectiveness of finite-time tracking control algorithm.展开更多
The proton exchange membrane fuel cell(PEMFC)and the hydrogen hybrid power system are studied by the fuzzy-PID(FPID)controlmethod and the fuzzy-PID controlmethod by Artificial Bee Colony algorithm(ABCFPID),respectivel...The proton exchange membrane fuel cell(PEMFC)and the hydrogen hybrid power system are studied by the fuzzy-PID(FPID)controlmethod and the fuzzy-PID controlmethod by Artificial Bee Colony algorithm(ABCFPID),respectively.The results reveal that compared with the FPID control method,the temperature overshoot of the PEMFC stack under the ABC-FPID control method is decreased by 0.6%.Moreover,the circulating water flow rate within the full operating envelope(about 3 min)is reduced by 19.46 L,which means the ABC-FPID control method is more effective in regulating the stack temperature.Then,the ABC-FPID control method is proposed to study the hydrogen hybrid power system,and the system output power matching,operating characteristic curve of the fuel cell,state of charge(SOC)of the lithium battery,system efficiency and hydrogen demand are obtained.The results indicate that the maximum system efficiency reaches 46.3%,the average system efficiency is 33.8%,and the average hydrogen demand is 0.192 kg/s.Overall,the ABC-FPID control method can efficiently ensure the stability of the fuel cell’s output power,and actively prompt the lithium battery to fulfill the function of“peak shaving and valley filling”under variable load power conditions.展开更多
In order to solve the problems of slow dynamic response and difficult multi-source coordination of solar electric vehicle charging stations under intermittent renewable energy,this paper proposes a hardware-algorithm ...In order to solve the problems of slow dynamic response and difficult multi-source coordination of solar electric vehicle charging stations under intermittent renewable energy,this paper proposes a hardware-algorithm co-design framework:the T-type three-level bidirectional converter(100 kHz switching frequency)based on silicon carbide(SiC)MOSFET is deeply integrated with fuzzy model predictive control(Fuzzy-MPC).At the hardware level,the switching trajectory and resonance suppression circuit(attenuation resonance peak 18 dB)are optimized,and the total loss is reduced by 23%compared with the traditional silicon-based IGBT.At the algorithm level,the adaptive parameter update mechanism and multi-objective rolling optimization are adopted,and the 5 ms level dynamic power allocation is realized by relying on edge computing.Experiments on 800 V DC microgrid(including 600 kW photovoltaic and 150 A·h energy storage)built based on MATLAB/Simulink hardware-in-the-loop(HIL)platform show that the system shortens the battery charging time from 42 to 28 min(the charging speed is increased by 33%).Through the 78%valley power utilization rate,the power purchase cost of high-priced power grids was significantly reduced,and the levelized electricity price decreased by 10.3%;Under the irradiation fluctuation,the renewable energy consumption rate increases by 10.1%,and the DC bus voltage fluctuation is stable within±10 V when the load step is±30%.The co-design provides an economically feasible and dynamically robust solution for the efficient integration of PV-ESG-EV in the smart grid.展开更多
The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade tempe...The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.展开更多
Dividing wall batch distillation with middle vessel(DWBDM)is a new type of batch distillation column,with outstanding advantages of low capital cost,energy saving and flexible operation.However,temperature control of ...Dividing wall batch distillation with middle vessel(DWBDM)is a new type of batch distillation column,with outstanding advantages of low capital cost,energy saving and flexible operation.However,temperature control of DWBDM process is challenging,since inherently dynamic and highly nonlinear,which make it difficult to give the controller reasonable set value or optimal temperature profile for temperature control scheme.To overcome this obstacle,this study proposes a new strategy to develop temperature control scheme for DWBDM combining neural network soft-sensor with fuzzy control.Dynamic model of DWBDM was firstly developed and numerically solved by Python,with three control schemes:composition control by PID and fuzzy control respectively,and temperature control by fuzzy control with neural network soft-sensor.For dynamic process,the neural networks with memory functions,such as RNN,LSTM and GRU,are used to handle with time-series data.The results from a case example show that the new control scheme can perform a good temperature control of DWBDM with the same or even better product purities as traditional PID or fuzzy control,and fuzzy control could reduce the effect of prediction error from neural network,indicating that it is a highly feasible and effective control approach for DWBDM,and could even be extended to other dynamic processes.展开更多
In this paper,the problem of non-singular fixed-time control with prescribed performance is studied for multi-agent systems characterized by uncertain states,nonlinearities,and nonstrict feedback.To mitigate the nonli...In this paper,the problem of non-singular fixed-time control with prescribed performance is studied for multi-agent systems characterized by uncertain states,nonlinearities,and nonstrict feedback.To mitigate the nonlinearity,a fuzzy logic algorithm is applied to approximate the intrinsic dynamics of the system.Furthermore,a fuzzy logic system state observer based on leader state information is designed to address the partial unob-servability of followers.Subsequently,the power integral method is incorporated into the backstepping approach to avoid singularities in the fixed-time controller.A command filter method is introduced into the standard backstepping approach to reduce the computational complexity of controller design.Then,a non-singular fixed-time adaptive control strategy with prescribed performance is proposed by constraining the tracking error within a prescribed range.Rigorous theoretical analysis ensures the convergence of consensus error in the multi-agent system to the prescribed performance region within a fixed time.Finally,the practicality of the algorithm is validated through numerical simulations.展开更多
In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of ...In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of pneumatic systems can significantly impact the force control accuracy of pneumatic polishing system end-effectors.To enhance responsiveness and control precision during the flexible polishing process,this study proposes an observer-based fuzzy adaptive control(OBFAC)scheme.To ensure control accuracy under an uncertain dynamic contact model,a fuzzy state observer is designed to estimate unmeasured states,while fuzzy logic approximates the uncertain nonlinear functions in the model to improve control performance.Additionally,the integral barrier Lyapunov function is employed to ensure that all states remain within predefined constraints.The stability of the proposed control scheme is analyzed using the Lyapunov function,and a pneumatic polishing experimental platform is constructed to conduct polishing contact force control experiments under multiple scenarios.Experimental results demonstrate that the proposed OBFAC scheme achieves superior tracking control performance compared to existing control schemes.展开更多
This paper investigates the problem of fuzzy adaptive finite-time inverse optimal control for active suspension systems(ASSs).The fuzzy logic systems(FLSs)are utilized to learn the unknown non-linear dynamics and an a...This paper investigates the problem of fuzzy adaptive finite-time inverse optimal control for active suspension systems(ASSs).The fuzzy logic systems(FLSs)are utilized to learn the unknown non-linear dynamics and an auxiliary system is established.Based on the finite-time stability theory and inverse optimal theory,a fuzzy adaptive inverse finite-time inverse optimal control method is proposed.It is proven that the formulated control approach guarantees the stability of the controlled systems,while ensuring that errors converge to a small neighborhood of zero within finite time.Moreover,the optimized control performance can be achieved.Eventually,the simulation results demonstrate the effectiveness of the proposed fuzzy adaptive finite-time inverse optimal control scheme.展开更多
In response to the need for a supportive on-orbit platform for future Mars exploration missions,this paper proposes the design and implementation of an autonomous spacecraft formation flying system near the Martian sy...In response to the need for a supportive on-orbit platform for future Mars exploration missions,this paper proposes the design and implementation of an autonomous spacecraft formation flying system near the Martian synchronous orbit using fuzzy learning-based intelligent control.A detailed analysis of spacecraft relative motion in the Mars environment is conducted,deducing the necessary conditions to reach the Martian synchronous orbit constraints.The modified Clohessy-Wiltshire(C-W)equation with Martian J_(2)(Oblateness index)perturbation is used as a reference to design a fuzzy learning-based intelligent and robust nonlinear control approach,which helps to autonomously track the desired formation configuration and stabilizes it.An introduction to spacecraft propulsion mechanisms is provided to analyze the feasibility of using electrical thrusters for spacecraft formation configuration tracking and stabilization in Martian synchronous orbits.The simulations show the effectiveness of the proposed control system for long-term on-orbit operations and reveal its reliability for designing intelligent deep-space formation flying configurations,such as an autonomous Mars observatory,a Martian telescope,or an interferometer.展开更多
Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of cha...Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.展开更多
In order to solve the problem of double motor synchronous error in the hydraulic lifting system of large crane,fuzzy control andneural network control are combined to realize the dynamic correction of PID parameters.W...In order to solve the problem of double motor synchronous error in the hydraulic lifting system of large crane,fuzzy control andneural network control are combined to realize the dynamic correction of PID parameters.With the use of cross-coupling control method in the control process based on the dynamic characteristics of the hydraulic system,both the pressure difference of hydraulic motor outlet and displacement of steel wire rope are regard as control index on the simulation and experimental research toimprove the accuracy of synchronous control.The results show that this control strategy has strong ability of anti-interference,and effectively improving the synchronization control precision of the two motors.展开更多
An Interval Type-2(IT-2)fuzzy controller design approach is proposed in this research to simultaneously achievemultiple control objectives inNonlinearMulti-Agent Systems(NMASs),including formation,containment,and coll...An Interval Type-2(IT-2)fuzzy controller design approach is proposed in this research to simultaneously achievemultiple control objectives inNonlinearMulti-Agent Systems(NMASs),including formation,containment,and collision avoidance.However,inherent nonlinearities and uncertainties present in practical control systems contribute to the challenge of achieving precise control performance.Based on the IT-2 Takagi-Sugeno Fuzzy Model(T-SFM),the fuzzy control approach can offer a more effective solution for NMASs facing uncertainties.Unlike existing control methods for NMASs,the Formation and Containment(F-and-C)control problem with collision avoidance capability under uncertainties based on the IT-2 T-SFM is discussed for the first time.Moreover,an IT-2 fuzzy tracking control approach is proposed to solve the formation task for leaders in NMASs without requiring communication.This control scheme makes the design process of the IT-2 fuzzy Formation Controller(FC)more straightforward and effective.According to the communication interaction protocol,the IT-2 Containment Controller(CC)design approach is proposed for followers to ensure convergence into the region defined by the leaders.Leveraging the IT-2 T-SFM representation,the analysis methods developed for linear Multi-Agent Systems(MASs)are successfully extended to perform containment analysis without requiring the additional assumptions imposed in existing research.Notably,the IT-2 fuzzy tracking controller can also be applied in collision avoidance situations to track the desired trajectories calculated by the avoidance algorithm under the Artificial Potential Field(APF).Benefiting from the combination of vortex and source APFs,the leaders can properly adjust the system dynamics to prevent potential collision risk.Integrating the fuzzy theory and APFs avoidance algorithm,an IT-2 fuzzy controller design approach is proposed to achieve the F-and-C purposewhile ensuring collision avoidance capability.Finally,amulti-ship simulation is conducted to validate the feasibility and effectiveness of the designed IT-2 fuzzy controller.展开更多
Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,s...Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,so it can handle optimal control problems effectively.This paper proposes an inverse reinforcement learning optimal control method for Takagi-Sugeno(T-S)fuzzy systems.Based on learner systems,an expert system is constructed,where the learner system only knows the expert system's optimal control policy.To reconstruct the unknown cost function,we firstly develop a model-based inverse reinforcement learning algorithm for the case that systems dynamics are known.The developed model-based learning algorithm is consists of two learning stages:an inner reinforcement learning loop and an outer inverse optimal control loop.The inner loop desires to obtain optimal control policy via learner's cost function and the outer loop aims to update learner's state-penalty matrices via only using expert's optimal control policy.Then,to eliminate the requirement that the system dynamics must be known,a data-driven integral learning algorithm is presented.It is proved that the presented two algorithms are convergent and the developed inverse reinforcement learning optimal control scheme can ensure the controlled fuzzy learner systems to be asymptotically stable.Finally,we apply the proposed fuzzy optimal control to the truck-trailer system,and the computer simulation results verify the effectiveness of the presented approach.展开更多
A fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments.Firstly,the mechanical structure of the robot is designed...A fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments.Firstly,the mechanical structure of the robot is designed,and a control system framework is proposed according to the different motion environments.To address the adaptability issue of the robot foot contact with the ground,a position-based admittance control method is proposed.Secondly,to improve the tracking performance of the robot foot contact force when the ground environment changes,a fuzzy adaptive admittance parameter adjustment method is proposed.Furthermore,to address the problem of sudden changes in the tracking difference of the foot contact force when the ground environment changes,a real-time estimation method is proposed to estimate the dynamic foot contact force.Finally,a simulation experiment is conducted in MATLAB and Simscape to verify the effectiveness of the robot motion control system,admittance control,fuzzy adaptive admittance parameters adjustment,and the realtime estimation method.Through multi-scenario experiments with the robot prototype,the control method demonstrates its effectiveness and adaptability in various environments.展开更多
Dear Editor,This letter deals with the controller synthesis problem of networked Takagi-Sugeno(T-S)fuzzy systems.Due to the introduction of network communications,the same premise is no longer shared by fuzzy plants a...Dear Editor,This letter deals with the controller synthesis problem of networked Takagi-Sugeno(T-S)fuzzy systems.Due to the introduction of network communications,the same premise is no longer shared by fuzzy plants and fuzzy controllers.This makes the classic parallel distribution compensation(PDC)control infeasible.To overcome this situation,a novel method for reconstructing the membership functions'grades is proposed,which synchronizes the time scales.Then,the membership function dependent method is adopted to introduce asynchronous errors and detailed membership function information.For the event-triggered control strategy,a series of robust H∞stable conditions in LMI form are derived.Finally,a simulation of a practical system is used to demonstrate the method proposed in this letter can reduce conservatism.展开更多
This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates senso...This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.展开更多
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
During flight operations,quadrotor UAVs are susceptible to interference from environmental factors such as wind gusts,battery depletion,and obstacles,which may compromise flight stability.This study proposes a fuzzy a...During flight operations,quadrotor UAVs are susceptible to interference from environmental factors such as wind gusts,battery depletion,and obstacles,which may compromise flight stability.This study proposes a fuzzy adaptive PID controller(Fuzzy PID)combining PID control with fuzzy logic to achieve self-adaptive adjustment of PID parameters in UAV flight control systems,thereby enhancing system robustness.A quadrotor UAV control model was developed in Simulink,and a Fuzzy PID control system was constructed by integrating fuzzy control logic for simulation and experimental validation.Test results demonstrate that UAVs governed by Fuzzy PID control exhibit faster regulation speed and improved stability when subjected to disturbances.展开更多
基金supported by the Lanzhou Science and Technology Plan Project(XM1753694781389).
文摘Facing the economic challenges of significant frequency regulation wear and tear on thermal power units and short energy storage lifespan in thermal-energy storage combined systems participating in grid primary frequency regulation(PFR),this paper proposes a novel hybrid energy storage system(HESS)control strategy based on Newton-Raphson optimization algorithm(NRBO)-VMD and a fuzzy neural network(FNN)for PFR.In the primary power allocation stage,the high inertia and slow response of thermal power units prevent them from promptly responding to the high-frequency components of PFR signals,leading to increased mechanical stress.To address the distinct response characteristics of thermal units and HESS,an NRBO-VMD based decomposition method for PFR signals is proposed,enabling a flexible system response to grid frequency deviations.Within the HESS,an adaptive coordinated control strategy and a State of Charge(SOC)self-recovery strategy are introduced.These strategies autonomously adjust the virtual inertia and droop coefficients based on the depth of frequency regulation and the real-time SOC.Furthermore,a FNN is constructed to perform secondary refinement of the internal power distribution within the HESS.Finally,simulations under various operational conditions demonstrate that the proposed strategy effectively mitigates frequent power adjustments of the thermal unit during PFR,adaptively achieves optimal power decomposition and distribution,maintains the flywheel energy storage’s SOC within an optimal range,and ensures the long-term stable operation of the HESS.
基金Supported by National Natural Science Foundation of China(Grant Nos.52025121,52394263)National Key R&D Plan of China(Grant No.2023YFD2000301)+2 种基金Foundation of State Key Laboratory of Automobile Safety and Energy Saving of China(Grant No.KFZ2201)Jiangsu Provincial Scientific Research Center of Applied Mathematics(Grant No.BK20233002)Special Fund of Jiangsu Province for the Transformation of Scientific and Technological Achievements(Grant No.BA2021023).
文摘Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles,reducing system reliability and posing significant challenges to driving safety.To enhance the system resilience,this paper proposes a novel robust fuzzy fault-tolerant control strategy that incorporates the adaptive event-trigger(AET)mechanism to realize stable,reliable,and precise lane-keeping control in the presence of multiple system uncertainties and probabilistic faults.First,to capture the uncertain and time-varying nature of tire cornering stiffness,an effective Takagi-Sugeno(T-S)fuzzy tire model is developed.Then,by employing the distribution-based probabilistic approach,two sets of unrelated random variables,random sensor and actuator faults in the control system,are modeled.Next,to improve communication efficiency and address ineluctable network-induced delays,an AET control framework with a well-designed triggering condition is established.Subsequently,a robust fuzzy output feedback fault-tolerant lane-keeping controller that satisfies the H∞per-formance is designed by using the Lyapunov-Krasovski functional method.Furthermore,the mean-square ex-ponential stability of the closed-loop system is rigorously guaranteed.Finally,real-time simulations based on Carsim/Simulink co-simulation platform under dynamic driving conditions demonstrate the feasibility and ef-fectiveness of the proposed control strategy.
基金supported by the National Natural Science Foundation of China(62403340,62303339)Sichuan Science and Technology Program(2026NSFSC1518)+2 种基金China Postdoctoral Science Foundation(CPSF)(2025T180940,2024M762208)Postdoctoral Fellowship Program of CPSF(GZC20231783)Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips(BCIC-24-K2)。
文摘To address the finite-time tracking control problem for fractional-order nonlinear systems(FONSs) with actuator faults and external disturbance,a novel strategy of the finite-time adaptive fuzzy fault-tolerant controller is presented in this paper by utilizing the finite-time stability theory and fractional-order dynamic surface control scheme combined with backstepping method.A new lemma is developed for analyzing the finite-time stability of FONSs in terms of fractional differential inequality,which modifies some existing results.Fuzzy logic systems are adopted to identify unknown nonlinear characteristics in FONS.In order to compensate for the influence of unknown external disturbance and estimation error for fuzzy logic systems,an auxiliary function is employed to estimate the upper bound of parameters online.Furthermore,a global coordinate transformation is first introduced initially to decouple the fractional-order dynamic system of a specific class of underactuated single-link flexible manipulator systems,thereby transforming it into lower triangular systems.Simulation analyses and experimental results verify the feasibility and effectiveness of finite-time tracking control algorithm.
基金supported by the Natural Science Foundation of Jiangsu Province(BK20231445)Aeronautical Science Foundation of China(20230028052001).
文摘The proton exchange membrane fuel cell(PEMFC)and the hydrogen hybrid power system are studied by the fuzzy-PID(FPID)controlmethod and the fuzzy-PID controlmethod by Artificial Bee Colony algorithm(ABCFPID),respectively.The results reveal that compared with the FPID control method,the temperature overshoot of the PEMFC stack under the ABC-FPID control method is decreased by 0.6%.Moreover,the circulating water flow rate within the full operating envelope(about 3 min)is reduced by 19.46 L,which means the ABC-FPID control method is more effective in regulating the stack temperature.Then,the ABC-FPID control method is proposed to study the hydrogen hybrid power system,and the system output power matching,operating characteristic curve of the fuel cell,state of charge(SOC)of the lithium battery,system efficiency and hydrogen demand are obtained.The results indicate that the maximum system efficiency reaches 46.3%,the average system efficiency is 33.8%,and the average hydrogen demand is 0.192 kg/s.Overall,the ABC-FPID control method can efficiently ensure the stability of the fuel cell’s output power,and actively prompt the lithium battery to fulfill the function of“peak shaving and valley filling”under variable load power conditions.
基金Jiangsu Provincial College Student Innovation and Entrepreneurship Program(Grant No.SJCX25_2184)—“Multi-energy Complementary Optimization and Vehicle-Storage Bidirectional Interaction Technology Driven by Novel 5E Framework”(Principal Investigator:Yuan-Yuan ShiFunding Agency:Jiangsu Provincial Education Department)+3 种基金Huaian Natural Science Research Project(Grant No.HAB2024046)—“Optimal Control of Flexible Cold-Heat-Power Integrated System with Source-Grid-Load-Storage Coordination”(Principal Investigator:Jie JiFunding Agency:Huaian Science and Technology Bureau)Huaiyin Institute of TechnologyUniversity-funded Project(GrantNo.HGYK202511)—“Data-driven CooperativeOptimization Dispatch for Source-Grid-Load Systems”(Principal Investigator:Chu-Tong ZhangFunding Agency:Huaiyin Institute of Technology).
文摘In order to solve the problems of slow dynamic response and difficult multi-source coordination of solar electric vehicle charging stations under intermittent renewable energy,this paper proposes a hardware-algorithm co-design framework:the T-type three-level bidirectional converter(100 kHz switching frequency)based on silicon carbide(SiC)MOSFET is deeply integrated with fuzzy model predictive control(Fuzzy-MPC).At the hardware level,the switching trajectory and resonance suppression circuit(attenuation resonance peak 18 dB)are optimized,and the total loss is reduced by 23%compared with the traditional silicon-based IGBT.At the algorithm level,the adaptive parameter update mechanism and multi-objective rolling optimization are adopted,and the 5 ms level dynamic power allocation is realized by relying on edge computing.Experiments on 800 V DC microgrid(including 600 kW photovoltaic and 150 A·h energy storage)built based on MATLAB/Simulink hardware-in-the-loop(HIL)platform show that the system shortens the battery charging time from 42 to 28 min(the charging speed is increased by 33%).Through the 78%valley power utilization rate,the power purchase cost of high-priced power grids was significantly reduced,and the levelized electricity price decreased by 10.3%;Under the irradiation fluctuation,the renewable energy consumption rate increases by 10.1%,and the DC bus voltage fluctuation is stable within±10 V when the load step is±30%.The co-design provides an economically feasible and dynamically robust solution for the efficient integration of PV-ESG-EV in the smart grid.
基金Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)the International Scientific and Technological Cooperation(20240402071GH).
文摘The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.
基金supported by Beijing Natural Science Foundation(2222037)the Special Educating Project of the Talent for Carbon Peak and Carbon Neutrality of University of Chinese Academy of Sciences(Innovation of talent cultivation model for“dual carbon”in chemical engineering industry,E3E56501A2).
文摘Dividing wall batch distillation with middle vessel(DWBDM)is a new type of batch distillation column,with outstanding advantages of low capital cost,energy saving and flexible operation.However,temperature control of DWBDM process is challenging,since inherently dynamic and highly nonlinear,which make it difficult to give the controller reasonable set value or optimal temperature profile for temperature control scheme.To overcome this obstacle,this study proposes a new strategy to develop temperature control scheme for DWBDM combining neural network soft-sensor with fuzzy control.Dynamic model of DWBDM was firstly developed and numerically solved by Python,with three control schemes:composition control by PID and fuzzy control respectively,and temperature control by fuzzy control with neural network soft-sensor.For dynamic process,the neural networks with memory functions,such as RNN,LSTM and GRU,are used to handle with time-series data.The results from a case example show that the new control scheme can perform a good temperature control of DWBDM with the same or even better product purities as traditional PID or fuzzy control,and fuzzy control could reduce the effect of prediction error from neural network,indicating that it is a highly feasible and effective control approach for DWBDM,and could even be extended to other dynamic processes.
基金supported by the National Natural Science Foundation of China(62203356).
文摘In this paper,the problem of non-singular fixed-time control with prescribed performance is studied for multi-agent systems characterized by uncertain states,nonlinearities,and nonstrict feedback.To mitigate the nonlinearity,a fuzzy logic algorithm is applied to approximate the intrinsic dynamics of the system.Furthermore,a fuzzy logic system state observer based on leader state information is designed to address the partial unob-servability of followers.Subsequently,the power integral method is incorporated into the backstepping approach to avoid singularities in the fixed-time controller.A command filter method is introduced into the standard backstepping approach to reduce the computational complexity of controller design.Then,a non-singular fixed-time adaptive control strategy with prescribed performance is proposed by constraining the tracking error within a prescribed range.Rigorous theoretical analysis ensures the convergence of consensus error in the multi-agent system to the prescribed performance region within a fixed time.Finally,the practicality of the algorithm is validated through numerical simulations.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFB3403402)National Natural Science Foundation of China Basic Research Programme for PhD Students(Grant No.524B2049)。
文摘In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of pneumatic systems can significantly impact the force control accuracy of pneumatic polishing system end-effectors.To enhance responsiveness and control precision during the flexible polishing process,this study proposes an observer-based fuzzy adaptive control(OBFAC)scheme.To ensure control accuracy under an uncertain dynamic contact model,a fuzzy state observer is designed to estimate unmeasured states,while fuzzy logic approximates the uncertain nonlinear functions in the model to improve control performance.Additionally,the integral barrier Lyapunov function is employed to ensure that all states remain within predefined constraints.The stability of the proposed control scheme is analyzed using the Lyapunov function,and a pneumatic polishing experimental platform is constructed to conduct polishing contact force control experiments under multiple scenarios.Experimental results demonstrate that the proposed OBFAC scheme achieves superior tracking control performance compared to existing control schemes.
基金supported by the National Natural Science Foundation of China under 62173172。
文摘This paper investigates the problem of fuzzy adaptive finite-time inverse optimal control for active suspension systems(ASSs).The fuzzy logic systems(FLSs)are utilized to learn the unknown non-linear dynamics and an auxiliary system is established.Based on the finite-time stability theory and inverse optimal theory,a fuzzy adaptive inverse finite-time inverse optimal control method is proposed.It is proven that the formulated control approach guarantees the stability of the controlled systems,while ensuring that errors converge to a small neighborhood of zero within finite time.Moreover,the optimized control performance can be achieved.Eventually,the simulation results demonstrate the effectiveness of the proposed fuzzy adaptive finite-time inverse optimal control scheme.
基金supported by the National Laboratory of Space Intelligent Control(No.HTKJ2023KL502007)the Chinese Government Scholarship(CSC)。
文摘In response to the need for a supportive on-orbit platform for future Mars exploration missions,this paper proposes the design and implementation of an autonomous spacecraft formation flying system near the Martian synchronous orbit using fuzzy learning-based intelligent control.A detailed analysis of spacecraft relative motion in the Mars environment is conducted,deducing the necessary conditions to reach the Martian synchronous orbit constraints.The modified Clohessy-Wiltshire(C-W)equation with Martian J_(2)(Oblateness index)perturbation is used as a reference to design a fuzzy learning-based intelligent and robust nonlinear control approach,which helps to autonomously track the desired formation configuration and stabilizes it.An introduction to spacecraft propulsion mechanisms is provided to analyze the feasibility of using electrical thrusters for spacecraft formation configuration tracking and stabilization in Martian synchronous orbits.The simulations show the effectiveness of the proposed control system for long-term on-orbit operations and reveal its reliability for designing intelligent deep-space formation flying configurations,such as an autonomous Mars observatory,a Martian telescope,or an interferometer.
基金supported by National Key R&D Program of ChinaunderGrant,(2021YFB2601403).
文摘Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.
基金supported by the project of the Central Government Guides Local Science and Technology Development Plans of Inner Mongolia(2022ZY0013)2022 Autonomous Region"Grassland Talents"Young Innovative Talents Level 1(2023QNCXRC04)2022 Western Light Talent Training Program of the Organization Department of the CPC Central Committee"Western Young Scholars"(S24001).
文摘In order to solve the problem of double motor synchronous error in the hydraulic lifting system of large crane,fuzzy control andneural network control are combined to realize the dynamic correction of PID parameters.With the use of cross-coupling control method in the control process based on the dynamic characteristics of the hydraulic system,both the pressure difference of hydraulic motor outlet and displacement of steel wire rope are regard as control index on the simulation and experimental research toimprove the accuracy of synchronous control.The results show that this control strategy has strong ability of anti-interference,and effectively improving the synchronization control precision of the two motors.
基金founded by the National Science and Technology Council of the Republic of China under contract NSTC113-2221-E-019-032.
文摘An Interval Type-2(IT-2)fuzzy controller design approach is proposed in this research to simultaneously achievemultiple control objectives inNonlinearMulti-Agent Systems(NMASs),including formation,containment,and collision avoidance.However,inherent nonlinearities and uncertainties present in practical control systems contribute to the challenge of achieving precise control performance.Based on the IT-2 Takagi-Sugeno Fuzzy Model(T-SFM),the fuzzy control approach can offer a more effective solution for NMASs facing uncertainties.Unlike existing control methods for NMASs,the Formation and Containment(F-and-C)control problem with collision avoidance capability under uncertainties based on the IT-2 T-SFM is discussed for the first time.Moreover,an IT-2 fuzzy tracking control approach is proposed to solve the formation task for leaders in NMASs without requiring communication.This control scheme makes the design process of the IT-2 fuzzy Formation Controller(FC)more straightforward and effective.According to the communication interaction protocol,the IT-2 Containment Controller(CC)design approach is proposed for followers to ensure convergence into the region defined by the leaders.Leveraging the IT-2 T-SFM representation,the analysis methods developed for linear Multi-Agent Systems(MASs)are successfully extended to perform containment analysis without requiring the additional assumptions imposed in existing research.Notably,the IT-2 fuzzy tracking controller can also be applied in collision avoidance situations to track the desired trajectories calculated by the avoidance algorithm under the Artificial Potential Field(APF).Benefiting from the combination of vortex and source APFs,the leaders can properly adjust the system dynamics to prevent potential collision risk.Integrating the fuzzy theory and APFs avoidance algorithm,an IT-2 fuzzy controller design approach is proposed to achieve the F-and-C purposewhile ensuring collision avoidance capability.Finally,amulti-ship simulation is conducted to validate the feasibility and effectiveness of the designed IT-2 fuzzy controller.
基金The National Natural Science Foundation of China(62173172).
文摘Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,so it can handle optimal control problems effectively.This paper proposes an inverse reinforcement learning optimal control method for Takagi-Sugeno(T-S)fuzzy systems.Based on learner systems,an expert system is constructed,where the learner system only knows the expert system's optimal control policy.To reconstruct the unknown cost function,we firstly develop a model-based inverse reinforcement learning algorithm for the case that systems dynamics are known.The developed model-based learning algorithm is consists of two learning stages:an inner reinforcement learning loop and an outer inverse optimal control loop.The inner loop desires to obtain optimal control policy via learner's cost function and the outer loop aims to update learner's state-penalty matrices via only using expert's optimal control policy.Then,to eliminate the requirement that the system dynamics must be known,a data-driven integral learning algorithm is presented.It is proved that the presented two algorithms are convergent and the developed inverse reinforcement learning optimal control scheme can ensure the controlled fuzzy learner systems to be asymptotically stable.Finally,we apply the proposed fuzzy optimal control to the truck-trailer system,and the computer simulation results verify the effectiveness of the presented approach.
基金National Natural Science Foundation of China(No.U1831123)。
文摘A fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments.Firstly,the mechanical structure of the robot is designed,and a control system framework is proposed according to the different motion environments.To address the adaptability issue of the robot foot contact with the ground,a position-based admittance control method is proposed.Secondly,to improve the tracking performance of the robot foot contact force when the ground environment changes,a fuzzy adaptive admittance parameter adjustment method is proposed.Furthermore,to address the problem of sudden changes in the tracking difference of the foot contact force when the ground environment changes,a real-time estimation method is proposed to estimate the dynamic foot contact force.Finally,a simulation experiment is conducted in MATLAB and Simscape to verify the effectiveness of the robot motion control system,admittance control,fuzzy adaptive admittance parameters adjustment,and the realtime estimation method.Through multi-scenario experiments with the robot prototype,the control method demonstrates its effectiveness and adaptability in various environments.
基金supported by the National Natural Science Foundation of China(62173218,61833011)International International Cooperation Project of Shanghai Science and Technology Commission(21190780300).
文摘Dear Editor,This letter deals with the controller synthesis problem of networked Takagi-Sugeno(T-S)fuzzy systems.Due to the introduction of network communications,the same premise is no longer shared by fuzzy plants and fuzzy controllers.This makes the classic parallel distribution compensation(PDC)control infeasible.To overcome this situation,a novel method for reconstructing the membership functions'grades is proposed,which synchronizes the time scales.Then,the membership function dependent method is adopted to introduce asynchronous errors and detailed membership function information.For the event-triggered control strategy,a series of robust H∞stable conditions in LMI form are derived.Finally,a simulation of a practical system is used to demonstrate the method proposed in this letter can reduce conservatism.
文摘This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
基金The 2023 Scientific and Technological Project in Henan Province of China(232102220098)。
文摘During flight operations,quadrotor UAVs are susceptible to interference from environmental factors such as wind gusts,battery depletion,and obstacles,which may compromise flight stability.This study proposes a fuzzy adaptive PID controller(Fuzzy PID)combining PID control with fuzzy logic to achieve self-adaptive adjustment of PID parameters in UAV flight control systems,thereby enhancing system robustness.A quadrotor UAV control model was developed in Simulink,and a Fuzzy PID control system was constructed by integrating fuzzy control logic for simulation and experimental validation.Test results demonstrate that UAVs governed by Fuzzy PID control exhibit faster regulation speed and improved stability when subjected to disturbances.
