Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres...Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.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
The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynam...The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.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
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
Traditional dynamic analysis of mechanical structures,often limited to individual beams or plates,fails to fully capture their dynamic behaviors.In systems where space and mass are constrained,such as the battery supp...Traditional dynamic analysis of mechanical structures,often limited to individual beams or plates,fails to fully capture their dynamic behaviors.In systems where space and mass are constrained,such as the battery support structures in electric aircraft,conventional absorbers and isolators are insufficient for effective vibration control.This study simplifies the battery support structure of electric aircraft as an integrated composite beam consisting of three interconnected beams,and investigated its structural dynamics properties and nonlinear vibration control under thermal conditions caused by battery heat.The nonlinear vibration control is performed using the Nitinol steel wire ropes(Ni Ti-ST),with nonlinear damping properties.The natural frequencies of system are determined using the Rayleigh-Ritz technique.Theoretical results are validated through both Finite Element Method(FEM)and hammer tests.Moreover,the dynamic equations are derived using the Lagrange method and discretized via the Galerkin Truncation Method(GTM).The Harmonic Balance Method(HBM)is used to evaluate the vibration responses of the integrated model,with further verification through the Runge-Kutta Method(RKM).The experiments are conducted to corroborate the theoretical analysis.The results show that the system frequency changes in stages with the increase of the stiffness of the integrated composite beam connection.Especially in the case of varying environments,as the temperature increases,the frequency of system will first increase to a certain maximum value and then gradually decrease.Furthermore,the NiTi-ST effectively reduces vibration in the integrated composite beam,particularly under varying temperatures and external excitations.展开更多
This review article provides a comprehensive analysis of the latest advancements and persistent challenges in Software-Defined Wide Area Networks(SD-WANs),with a particular emphasis on the multi-objective Controller P...This review article provides a comprehensive analysis of the latest advancements and persistent challenges in Software-Defined Wide Area Networks(SD-WANs),with a particular emphasis on the multi-objective Controller Placement Problem(CPP).As SD-WAN technology continues to gain prominence for its capacity to offer flexible and efficient network management,the task of 36optimally placing controllers—responsible for orchestrating and managing network traffic—remains a critical yet complex challenge.This review delves into recent innovations in multi-objective controller placement strategies,including clustering techniques,heuristic-based approaches,and the integration of machine learning and deep learning models.Each methodology is critically evaluated in terms of its ability to minimize network latency,enhance fault tolerance,and improve overall network performance.Furthermore,this paper discusses the inherent limitations and challenges associated with these techniques,providing a critical evaluation of their current utility and outlining potential avenues for future research.By offering a thorough overview of state-of-the-art approaches to multi-objective controller placement in SD-WANs,this review aims to inform ongoing advancements and highlight emerging research opportunities in this evolving field.展开更多
In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelli...In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.展开更多
This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satis...This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.展开更多
It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specificat...It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specifications tend to be conflicting to each other to make the control design even more challenging. This paper presents a cell mapping method for multi-objective optimal feedback control design in time domain for a nonlinear Duffing system with time delay. We first review the multi-objective optimization problem and its formulation for control design. We then introduce the cell mapping method and a hybrid algorithm for global optimal solutions. Numerical simulations of the PID control are presented to show the features of the multi-objective optimal design. @ 2013 The Chinese Society of Theoretical and Applied Mechanics. [doi：10.1063/2.1306306]展开更多
Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy...Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy to improve the opti-mization of PID controllers within nonlinear oscillatory Automatic Generation Control(AGC)systems,essential for the stability of power systems.Our approach aims to reduce the integrated time squared error,the integrated time absolute error,and the rate of change in deviation,facilitating faster convergence,diminished overshoot,and decreased oscillations.By incorporating the spiral model from the Whale Optimization Algorithm(WOA)into the Multi-Objective Marine Predator Algorithm(MOMPA),our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies.Furthermore,the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima,thereby generating optimal Pareto solutions.When applied to nonlinear AGC systems featuring governor dead zones,the PID controllers optimized by QQSMOMPA not only achieve 14%reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.展开更多
Rollover and jack-knifing of tractor semi-trailer are serious threats for vehicle safety, and accordingly active safety technologies have been widely used to reduce or prevent the occurrence of such accidents. However...Rollover and jack-knifing of tractor semi-trailer are serious threats for vehicle safety, and accordingly active safety technologies have been widely used to reduce or prevent the occurrence of such accidents. However, currently tractor semi-trailer stability control is generally only a single hazardous condition (rollover or jack-knifing) control, it is difficult to ensure the vehicle comprehensive stability of various dangerous conditions. The main objective of this study is to introduce a multi-objective stability control algorithm which can improve the vehicle stability of a tractor semi-trailer by using differential braking. A vehicle controller is designed to minimize the likelihood of rollover and jack-knifing. First a linear vehicle model of tractor semi-trailer is constructed. Then an optimal yaw control for tractor using differential braking is applied to minimize the yaw rate and lateral acceleration deviation of tractor, as well as the hitch articulation angle of tractor semi-trailer, so as to improve the vehicle stability. Second a braking scheme and variable structure control with sliding mode control are introduced in order to achieve the best braking effect. Last Fishhook maneuver is introduced to the active safety simulation and the active control system effect verification. The simulation results show that multi-objective stability control algorithm of semi-trailer could improve the vehicle stability significantly during the transient maneuvers. The proposed multi-objective stability control algorithm is effective to prevent the vehicle rollover and jackknifing.展开更多
For different flight phases in an overall flight mission,different control and allocation preferences should be pursued considering lift,drag or maneuverability characteristics.The multi-objective flight control alloc...For different flight phases in an overall flight mission,different control and allocation preferences should be pursued considering lift,drag or maneuverability characteristics.The multi-objective flight control allocation problem for a multi-phase flight mission is studied.For an overall flight mission,different flight phases namely climbing,cruise,maneuver and gliding phases are defined.Firstly,a multi-objective control allocation problem considering drag,lift or control energy preference is constructed.Secondly,considering different control preferences at different flight phases,the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences.The active set based dynamic programming optimization method is used to solve the real-time optimization problem.For the validation,the Innovative Control Effector(ICE)tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping(IBKS)are used to construct the validation platform.Finally,an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method.The results show that the comprehensive performance index for different phases,which are determined from the Analytic Hierarchy Process(AHP)method,can suitably satisfy the preference requirements for different flight phases.展开更多
This paper investigates a multiplayer Pareto game for affine nonlinear stochastic systems disturbed by both external and the internal multiplicative noises.The Pareto cooperative optimal strategies with the H_(∞) con...This paper investigates a multiplayer Pareto game for affine nonlinear stochastic systems disturbed by both external and the internal multiplicative noises.The Pareto cooperative optimal strategies with the H_(∞) constraint are resolved by integrating H_(2)/H_(∞) theory with Pareto game theory.First,a nonlinear stochastic bounded real lemma(SBRL)is derived,explicitly accounting for non-zero initial conditions.Through the analysis of four cross-coupled Hamilton-Jacobi equations(HJEs),we establish necessary and sufficient conditions for the existence of Pareto optimal strategies with the H_(∞) constraint.Secondly,to address the complexity of solving these nonlinear partial differential HJEs,we propose a neural network(NN)framework with synchronous tuning rules for the actor,critic,and disturbance components,based on a reinforcement learning(RL)approach.The designed tuning rules ensure convergence of the actor-critic-disturbance components to the desired values,enabling the realization of robust Pareto control strategies.The convergence of the proposed algorithm is rigorously analyzed using a constructed Lyapunov function for the NN weight errors.Finally,a numerical simulation example is provided to demonstrate the effectiveness of the proposed methods and main results.展开更多
For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainab...For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainability.In addition to comfort and fuel-economy,automated vehicles also have the basic requirements of safety and car-following.For this purpose,an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework.In the proposed ACC algorithm,safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy.The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments.The results show that not only are safety and car-following objectives satisfied,but also driving comfort and fuel-economy are improved significantly.展开更多
This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sli...This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sliding mode control is applied to the vibration control of a simplified landing gear model with uncertainty. A two-stage generalized cell mapping algorithm is applied to search the Pareto set with gradient-free scheme. Drop test simulations over uneven runway show that the vibration and force interaction can be considerably reduced, and the Pareto optimum form a tight range in time domain.展开更多
A model-assistant extended state observer(MESO)-based decoupling control strategy is proposed for boiler-turbine units in the presence of unknown external disturbance and model-plant mismatch. For ease of implementati...A model-assistant extended state observer(MESO)-based decoupling control strategy is proposed for boiler-turbine units in the presence of unknown external disturbance and model-plant mismatch. For ease of implementation, the decoupling compensator is reduced to the proportion integration(PI) decoupler with the frequency domain analysis, where the decoupling error in collusion of uncertainties and disturbances can be estimated by the proposed MESO and then compensated. To decrease the sensitivity of the dynamic error for the decoupling control and fulfill various requirements of constraints, such as safety operation, energy conservation, emission reduction, etc., the plant is transmitted through a scheduled steady state region which is achieved from the optimized reference governor in advance. Simulation results show that the proposed control strategy can well suppress various disturbances including a decoupling error, and multi-objective optimization can meet multiple requirements with the premise of safety production.展开更多
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
This paper proposes a new approach for multi-objective robust control. The approach extends the standard generalized l2 (Gl2) and generalized H2 (GH2) conditions to a set of new linear matrix inequality (LMI) constra...This paper proposes a new approach for multi-objective robust control. The approach extends the standard generalized l2 (Gl2) and generalized H2 (GH2) conditions to a set of new linear matrix inequality (LMI) constraints based on a new stability condition. A technique for variable parameterization is introduced to the multi-objective control problem to preserve the linearity of the synthesis variables. Consequently, the multi-channel multi-objective mixed Gl2/GH2 control problem can be solved less conservatively using computationally tractable algorithms developed in the paper.展开更多
CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities...CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities in its control and design.Dynamic performance is compassionate to change in system parameterswhich need more effort for planning a significant controller for CSTR.The reactor temperature changes in either direction from the defined reference value.It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature,and deviation from reference values may cause degradation of biomass quality.Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential.In this paper,a conventional Proportional Integral Derivative(PID)controller is designed.The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters.Hence,A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s limitation.In the proposed technique,PID parameters are tuned by Particle Swarm Optimization(PSO).It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response.In this article,a multi-objective function is proposed for PSO based controller design of CSTR.展开更多
基金supported by the National Natural Science Foundation of China(No.62173107).
文摘Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.
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.
基金National Natural Science Foundation of China(No.12071370)。
文摘The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.
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 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 National Natural Science Foundation of China(No.12272240)the Liaoning Revitalization Talents Program,China(No.XLYC2203197)。
文摘Traditional dynamic analysis of mechanical structures,often limited to individual beams or plates,fails to fully capture their dynamic behaviors.In systems where space and mass are constrained,such as the battery support structures in electric aircraft,conventional absorbers and isolators are insufficient for effective vibration control.This study simplifies the battery support structure of electric aircraft as an integrated composite beam consisting of three interconnected beams,and investigated its structural dynamics properties and nonlinear vibration control under thermal conditions caused by battery heat.The nonlinear vibration control is performed using the Nitinol steel wire ropes(Ni Ti-ST),with nonlinear damping properties.The natural frequencies of system are determined using the Rayleigh-Ritz technique.Theoretical results are validated through both Finite Element Method(FEM)and hammer tests.Moreover,the dynamic equations are derived using the Lagrange method and discretized via the Galerkin Truncation Method(GTM).The Harmonic Balance Method(HBM)is used to evaluate the vibration responses of the integrated model,with further verification through the Runge-Kutta Method(RKM).The experiments are conducted to corroborate the theoretical analysis.The results show that the system frequency changes in stages with the increase of the stiffness of the integrated composite beam connection.Especially in the case of varying environments,as the temperature increases,the frequency of system will first increase to a certain maximum value and then gradually decrease.Furthermore,the NiTi-ST effectively reduces vibration in the integrated composite beam,particularly under varying temperatures and external excitations.
文摘This review article provides a comprehensive analysis of the latest advancements and persistent challenges in Software-Defined Wide Area Networks(SD-WANs),with a particular emphasis on the multi-objective Controller Placement Problem(CPP).As SD-WAN technology continues to gain prominence for its capacity to offer flexible and efficient network management,the task of 36optimally placing controllers—responsible for orchestrating and managing network traffic—remains a critical yet complex challenge.This review delves into recent innovations in multi-objective controller placement strategies,including clustering techniques,heuristic-based approaches,and the integration of machine learning and deep learning models.Each methodology is critically evaluated in terms of its ability to minimize network latency,enhance fault tolerance,and improve overall network performance.Furthermore,this paper discusses the inherent limitations and challenges associated with these techniques,providing a critical evaluation of their current utility and outlining potential avenues for future research.By offering a thorough overview of state-of-the-art approaches to multi-objective controller placement in SD-WANs,this review aims to inform ongoing advancements and highlight emerging research opportunities in this evolving field.
基金supported by the National Natural Science Foundation of China(Grant No.52179105)China Postdoctoral Science Foundation(Grant No.2024M762193)。
文摘In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.
基金Supported by the National Natural Science Foundation of China(61374111)the Natural Science Foundation of Zhejiang Province(LY13F030006)Agricultural Key Program of Ningbo City(2014C10068)
文摘This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.
基金supported by the UC MEXUSCONACyT("Cell-to-cell Mapping for Global Multi-objective Optimization")the National Natural Science Foundation of China(11172197)+1 种基金the Natural Science Foundation of Tianjin through a key-project grantsupport from CONACyT through a scholarship to pursue graduate studies at the Computer Science Department of CINVESTAV-IPN
文摘It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specifications tend to be conflicting to each other to make the control design even more challenging. This paper presents a cell mapping method for multi-objective optimal feedback control design in time domain for a nonlinear Duffing system with time delay. We first review the multi-objective optimization problem and its formulation for control design. We then introduce the cell mapping method and a hybrid algorithm for global optimal solutions. Numerical simulations of the PID control are presented to show the features of the multi-objective optimal design. @ 2013 The Chinese Society of Theoretical and Applied Mechanics. [doi：10.1063/2.1306306]
基金supported in part by the National Natural Science Foundation of China under Grant 61873130in part by the Chunhui Program Collaborative Scientific Research Project under Grant 202202004+4 种基金in part by the Foundation of the Key Laboratory of Industrial Internet of Things and Networked Control of the Ministry of Education of China under Grant 2021FF01in part by the Natural Science Foundation of Nanjing University of Posts and Telecommunications under Grant NY221082,Grant NY222144,and Grant NY223075in part by the Huali Program for Excellent Talents in Nanjing University of Posts and Telecommunicationsin part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grantin part by the Fundamental Research Funds for the Central Universities under WUT:104972024KFYjc0072.
文摘Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy to improve the opti-mization of PID controllers within nonlinear oscillatory Automatic Generation Control(AGC)systems,essential for the stability of power systems.Our approach aims to reduce the integrated time squared error,the integrated time absolute error,and the rate of change in deviation,facilitating faster convergence,diminished overshoot,and decreased oscillations.By incorporating the spiral model from the Whale Optimization Algorithm(WOA)into the Multi-Objective Marine Predator Algorithm(MOMPA),our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies.Furthermore,the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima,thereby generating optimal Pareto solutions.When applied to nonlinear AGC systems featuring governor dead zones,the PID controllers optimized by QQSMOMPA not only achieve 14%reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.
基金supported by Open Research Fund of State Key Laboratory of Automobile Dynamics Simulation, China (Grant No. 20101103)National Natural Science Foundation of China (Grant No. 51075176)
文摘Rollover and jack-knifing of tractor semi-trailer are serious threats for vehicle safety, and accordingly active safety technologies have been widely used to reduce or prevent the occurrence of such accidents. However, currently tractor semi-trailer stability control is generally only a single hazardous condition (rollover or jack-knifing) control, it is difficult to ensure the vehicle comprehensive stability of various dangerous conditions. The main objective of this study is to introduce a multi-objective stability control algorithm which can improve the vehicle stability of a tractor semi-trailer by using differential braking. A vehicle controller is designed to minimize the likelihood of rollover and jack-knifing. First a linear vehicle model of tractor semi-trailer is constructed. Then an optimal yaw control for tractor using differential braking is applied to minimize the yaw rate and lateral acceleration deviation of tractor, as well as the hitch articulation angle of tractor semi-trailer, so as to improve the vehicle stability. Second a braking scheme and variable structure control with sliding mode control are introduced in order to achieve the best braking effect. Last Fishhook maneuver is introduced to the active safety simulation and the active control system effect verification. The simulation results show that multi-objective stability control algorithm of semi-trailer could improve the vehicle stability significantly during the transient maneuvers. The proposed multi-objective stability control algorithm is effective to prevent the vehicle rollover and jackknifing.
基金co-supported by the National Natural Science Foundation of China(No.11502008)Aeronautical Science Foundation of China(Nos.2017ZA51002,20185702003)the Fundamental Research Funds for the Central Universities of China(No.YWF-19-BJ-J-280)。
文摘For different flight phases in an overall flight mission,different control and allocation preferences should be pursued considering lift,drag or maneuverability characteristics.The multi-objective flight control allocation problem for a multi-phase flight mission is studied.For an overall flight mission,different flight phases namely climbing,cruise,maneuver and gliding phases are defined.Firstly,a multi-objective control allocation problem considering drag,lift or control energy preference is constructed.Secondly,considering different control preferences at different flight phases,the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences.The active set based dynamic programming optimization method is used to solve the real-time optimization problem.For the validation,the Innovative Control Effector(ICE)tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping(IBKS)are used to construct the validation platform.Finally,an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method.The results show that the comprehensive performance index for different phases,which are determined from the Analytic Hierarchy Process(AHP)method,can suitably satisfy the preference requirements for different flight phases.
基金supported by the National Natural Science Foundation of China(12426609,62203220,62373229)the Taishan Scholar Project Foundation of Shandong Province(tsqnz20230619,tsqn202408110)+2 种基金the Fundamental Research Foundation of the Central Universities(23Cx06024A)the Natural Science Foundation of Shandong Province(ZR2024QF096)the Outstanding Youth Innovation Team in Shandong Higher Education Institutions(2023KJ061).
文摘This paper investigates a multiplayer Pareto game for affine nonlinear stochastic systems disturbed by both external and the internal multiplicative noises.The Pareto cooperative optimal strategies with the H_(∞) constraint are resolved by integrating H_(2)/H_(∞) theory with Pareto game theory.First,a nonlinear stochastic bounded real lemma(SBRL)is derived,explicitly accounting for non-zero initial conditions.Through the analysis of four cross-coupled Hamilton-Jacobi equations(HJEs),we establish necessary and sufficient conditions for the existence of Pareto optimal strategies with the H_(∞) constraint.Secondly,to address the complexity of solving these nonlinear partial differential HJEs,we propose a neural network(NN)framework with synchronous tuning rules for the actor,critic,and disturbance components,based on a reinforcement learning(RL)approach.The designed tuning rules ensure convergence of the actor-critic-disturbance components to the desired values,enabling the realization of robust Pareto control strategies.The convergence of the proposed algorithm is rigorously analyzed using a constructed Lyapunov function for the NN weight errors.Finally,a numerical simulation example is provided to demonstrate the effectiveness of the proposed methods and main results.
基金Project supported by the National Hi-Tech Research and Develop-ment Program (863) of China (No. 2006AA11Z204)the Qianji-ang Program of Zhejiang Province (No. 2009R10008)
文摘For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainability.In addition to comfort and fuel-economy,automated vehicles also have the basic requirements of safety and car-following.For this purpose,an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework.In the proposed ACC algorithm,safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy.The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments.The results show that not only are safety and car-following objectives satisfied,but also driving comfort and fuel-economy are improved significantly.
基金Supported by the National Natural Science Foundation of China(No.11172197 and No.11332008)a key-project grant from the Natural Science Foundation of Tianjin(No.010413595)
文摘This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sliding mode control is applied to the vibration control of a simplified landing gear model with uncertainty. A two-stage generalized cell mapping algorithm is applied to search the Pareto set with gradient-free scheme. Drop test simulations over uneven runway show that the vibration and force interaction can be considerably reduced, and the Pareto optimum form a tight range in time domain.
基金The National Natural Science Foundation of China(No.51576041,51506029)
文摘A model-assistant extended state observer(MESO)-based decoupling control strategy is proposed for boiler-turbine units in the presence of unknown external disturbance and model-plant mismatch. For ease of implementation, the decoupling compensator is reduced to the proportion integration(PI) decoupler with the frequency domain analysis, where the decoupling error in collusion of uncertainties and disturbances can be estimated by the proposed MESO and then compensated. To decrease the sensitivity of the dynamic error for the decoupling control and fulfill various requirements of constraints, such as safety operation, energy conservation, emission reduction, etc., the plant is transmitted through a scheduled steady state region which is achieved from the optimized reference governor in advance. Simulation results show that the proposed control strategy can well suppress various disturbances including a decoupling error, and multi-objective optimization can meet multiple requirements with the premise of safety production.
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
基金Project supported by the National Natural Science Foundation ofChina (No. 60374028) and the Scientific Research Foundation forReturned Overseas Chinese Scholars Ministry of Education (No.[2004]176)
文摘This paper proposes a new approach for multi-objective robust control. The approach extends the standard generalized l2 (Gl2) and generalized H2 (GH2) conditions to a set of new linear matrix inequality (LMI) constraints based on a new stability condition. A technique for variable parameterization is introduced to the multi-objective control problem to preserve the linearity of the synthesis variables. Consequently, the multi-channel multi-objective mixed Gl2/GH2 control problem can be solved less conservatively using computationally tractable algorithms developed in the paper.
基金University Malaysia Sabah fully funds this research under the grant number F08/PGRG/1908/2019,Ag.Asri Ag.Ibrahim received the grant,sponsors’websites:https://www.u ms.edu.my.Conflicts of Interest。
文摘CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities in its control and design.Dynamic performance is compassionate to change in system parameterswhich need more effort for planning a significant controller for CSTR.The reactor temperature changes in either direction from the defined reference value.It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature,and deviation from reference values may cause degradation of biomass quality.Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential.In this paper,a conventional Proportional Integral Derivative(PID)controller is designed.The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters.Hence,A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s limitation.In the proposed technique,PID parameters are tuned by Particle Swarm Optimization(PSO).It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response.In this article,a multi-objective function is proposed for PSO based controller design of CSTR.
