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Data-Driven Predictive Control for Continuous-Time Nonlinear Systems:A Nonzero-Sum Game Approach 认领 引用
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作者 Juan Liu Hao Zhang +1 位作者 Yifan Xie Frank Allgöwer 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期495-497,共3页
Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over p... Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution. 展开更多
关键词 predictive control nonzero sum game observation loss predictive control input sequencesderiving continuous time nonlinear systems optimal predictive control input sequences reinforcement learning
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Adaptive-length data-driven predictive control for post-operation of space robot non-cooperative target capture with disturbances 认领 引用 被引量：1
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作者 Peiji WANG Bicheng CAI +2 位作者 Chengfei YUE Yong ZHAO Weiren WU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期485-498,共14页
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
关键词 Combined control Data-driven predictive control Post operation Predictive control systems Space non-cooperative target capture
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Distributed Iterative Learning Model Predictive Control for Unmanned Surface Vehicles 认领 引用
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作者 Xinyuan Huang Lihao Ma Huiping Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1512-1514,共3页
Dear Editor,This letter proposes a distributed iterative learning model predictive control(LMPC)strategy for coordinated trajectory tracking of multiple unmanned surface vehicles(USVs).By learning from previously feas... Dear Editor,This letter proposes a distributed iterative learning model predictive control(LMPC)strategy for coordinated trajectory tracking of multiple unmanned surface vehicles(USVs).By learning from previously feasible control and state trajectories,each USV iteratively refines its input sequence to improve the accuracy of trajectory tracking and formation control.To tackle challenges such as system coupling,limited onboard computational resources,and communication constraints,the method integrates the alternating direction method of multipliers(ADMM)with iterative learning.The effectiveness and advantages of the proposed approach are demonstrated through comparison results. 展开更多
关键词 Distributed Control learning previously feasible control state trajectorieseach distributed iterative learning model predictive control lmpc strategy unmanned surface vehicles usvs coordinated trajectory tracking Iterative Learning Control Model Predictive Control Unmanned Surface Vehicles
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Model predictive control based on LSTM neural network for maglev vehicle’suspension system 认领 引用
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作者 Mengjuan Liu Han Wu +3 位作者 Xin Liang Jiali Liu Xiaohui Zeng Kaixuan Hu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期504-517,共14页
To improve the suspension performance of high-speed maglev vehicles under complex external disturbance,a composite model predictive control(MPC)algorithm based on a neural network is proposed.Firstly,the nonlinear dyn... To improve the suspension performance of high-speed maglev vehicles under complex external disturbance,a composite model predictive control(MPC)algorithm based on a neural network is proposed.Firstly,the nonlinear dynamic response prediction model is constructed utilizing the long short-term memory(LSTM)neural network,and this model is trained by machine learning.Subsequently,a rolling optimization controller of the MPC algorithm is designed according to the vehicle suspension system’s prediction model and the suspension target.To compensate for the error of the prediction model resulting from changes in the control algorithm,a composite MPC algorithm is devised by combining both the proportionalintegral-derivative(PID)algorithm and the MPC algorithm.This composite approach enables the suspension system to switch the selection of control algorithms in the suspension system according to the prediction error.Finally,the effectiveness of the composite MPC algorithm is verified by simulation and experiment.The results show that the prediction model based on the LSTM neural network can effectively predict the future dynamic response of the vehicle.Moreover,the proposed MPC algorithm can effectively suppress the suspension gap fluctuation in the high-speed maglev vehicle,thereby fostering improved stability in the suspension system. 展开更多
关键词 Maglev vehicle Suspension system Model predictive control Long short-term memory neural network
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Networked Predictive Control:A Survey 认领 引用
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作者 Zhong-Hua Pang Tong Mu +3 位作者 Yi Yu Haibin Guo Guo-Ping Liu Qing-Long Han 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期3-20,共18页
Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induc... Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts. 展开更多
关键词 Communication constraints cyber attacks networked control systems networked multi-agent systems networked predictive control
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Dynamic Neural-Model-Based Predictive Control for Autonomous Wheel-Legged Robot System 认领 引用
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作者 Jiehao Li Junzheng Wang +2 位作者 Hongbo Gao Xiwen Luo C.L.Philip Chen 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期83-97,共15页
Mobile wheel-legged robots exhibiting mobility,stability and reliability have garnered heightened research attention in demanding real-world scenarios,especially in material transport,emergency response and space expl... Mobile wheel-legged robots exhibiting mobility,stability and reliability have garnered heightened research attention in demanding real-world scenarios,especially in material transport,emergency response and space exploration.The kinematics model merely delineates the geometric relationship of the controlled objective,disregarding force feedback.This study investigates model predictive trajectory tracking control utilising the robot dynamic model(DRMPC)in the context of unpredictable interactions.The predictive tracking controller for the wheel-legged robot is introduced in the context of position tracking.A dynamic approximator is employed to address the uncertain interactions in the tracking process.Ultimately,cosimulation and empirical tests are conducted to demonstrate the efficacy of the devised control methodology,which achieves high precision and dependable robustness.This work can elucidate the technical and practical oversight of autonomous movement in complicated environments and enhance the manoeuverability and flexibility. 展开更多
关键词 intelligent control predictive control robotics
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Model predictive control:past,present,and future 认领 引用
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作者 Nan Li Hong Chen +2 位作者 Meng Li Shuyou Yu Yanjun Huang 《Control Theory and Technology》 EI CSCD 2026年第2期173-193,共21页
Model predictive control(MPC)has evolved from an industry-originated heuristic to a rigorously grounded and widely adopted control framework.This survey provides a structured account of MPC's trajectory across thr... Model predictive control(MPC)has evolved from an industry-originated heuristic to a rigorously grounded and widely adopted control framework.This survey provides a structured account of MPC's trajectory across three interrelated dimensions:theoretical foundations,practical deployments,and future outlook.We first trace the emergence of core principles especially around stability and robustness and highlight pivotal contributions that have shaped linear,nonlinear,robust,stochastic,and adaptive MPC variants.The second part reviews computational advances,data-driven innovations,and widespread deployments,with a particular emphasis on automotive applications.Finally,we articulate a forward-looking vision of MPC as a general and unified paradigm for embodied intelligence.Throughout,we underscore contributions from international and Chinese research communities and point to emerging research directions at the intersection of control theory,machine learning,and intelligent systems. 展开更多
关键词 Model predictive control Autonomous systems Embodied intelligence
Tuning nonlinear model predictive control via Bayesian optimization:a comparative performance analysis 认领 引用
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作者 Maria Alice de F.Marques Juarez dos Santos Azevedo +1 位作者 Julio Elias Normey-Rico Marcus V.Americano da Costa 《Control Theory and Technology》 EI CSCD 2026年第3期374-387,共14页
This paper presents a new approach to tuning the cost function weights of a nonlinear model predictive controller(NMPC)using offline Bayesian optimization(BO).We propose a recursive weight selection method that integr... This paper presents a new approach to tuning the cost function weights of a nonlinear model predictive controller(NMPC)using offline Bayesian optimization(BO).We propose a recursive weight selection method that integrates BO directly into the NMPC simulation loop and targets an economic cost function.This approach identifies weights that optimize an economic cost function,ensuring that controller performance is aligned with the operational economics of the process.A case study involving an interconnected tank system with nonlinear level and temperature dynamics illustrates the method.Two operating conditions are tested:an undisturbed scenario and a disturbed one,incorporating sensor noise and model–plant mismatch.In both scenarios,the BO-tuned NMPC outperforms Traditional and Satisficing-based weight strategies,yielding smoother control actions.In the disturbed case,cost reductions of up to 4.5%are achieved.The results confirm that offline BO-based tuning offers a viable and robust alternative to manual or online adaptive strategies,especially in systems where online retraining is impractical.Sensitivity tests further highlight the importance of informed search interval selection to ensure convergence and performance. 展开更多
关键词 Nonlinear predictive control Bayesian optimization Tuning parameters Cost function
Research on Coordinated Operation Strategies for Wind Power Hybrid Energy Storage Systems Based on Model Predictive Control 认领 引用
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作者 Jiguang Wu Qing Zhi +4 位作者 Jin Guan Ruopeng Zhang Lixia Wu Shuhui Zhang Caifeng Wen 《Energy Engineering》 EI 2026年第7期424-444,共21页
This paper proposes a hybrid energy storage control method that coordinates the minimum output of the wind-storage system and the SOC self-recovery capability,applied to stand-alone energy storage stations.Under the p... This paper proposes a hybrid energy storage control method that coordinates the minimum output of the wind-storage system and the SOC self-recovery capability,applied to stand-alone energy storage stations.Under the premise of meeting the wind power smoothing requirements,model predictive control(MPC)is employed to rapidly regulate the SOC and output of the energy storage system during the smoothing process,thereby enhancing its sustained and stable operation capability,and decomposing the original wind power into a direct grid-connected component and a hybrid energy storage smoothing component.Subsequently,the Northern Goshawk AlgorithmImproved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(NGO-ICEEMDAN)method is employed to decompose and reconstruct the hybrid energy storage power obtained from MPC rolling optimization by determining the optimal combination of white noise amplitude weight Nstd and the number of noise additions(NA),and to allocate the reconstructed power between the supercapacitor and the battery.Finally,simulation verification is conducted using actual 10o MW wind power data from a site in Inner Mongolia.The results demonstrate that the proposed strategy can coordinate the relationship among the minimum output of the hybrid energy storage system(HESS),SOC balancing,and grid-connected power fluctuations.The NGO-ICEEMDAN method enables more precise power allocation,thereby improving the rationality and efficiency of energy management in wind power hybrid energy storage systems. 展开更多
关键词 Standalone energy storage power station hybrid energy storage system model predictive control empirical mode decomposition multi-objective optimization
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Human-Robot Interaction-Based Model Predictive Control for Exoskeleton Robots Driven by Series Elastic Actuators 认领 引用
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作者 Changxian Xu Keping Liu Zhongbo Sun 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期486-488,共3页
Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charg... Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject. 展开更多
关键词 human robot interaction model predictive assistance support series elastic actuator model predictive control series elastic actuator sea exoskeleton robot robot charge mode
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Improved Whale Optimization-Based Neural Network Predictive Control for Industrial Refrigeration Systems 认领 引用
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作者 Qi Li Menghan Yang +1 位作者 Shifa Cui Kun Han 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第3期1-14,共14页
The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such ... The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such systems a significant and challenging research problem.To address this,a novel Neural Network Predictive Control(NNPC)algorithm is proposed,which integrates an Improved Deep Belief Network(IDBN)with an Improved Whale Optimization Algorithm(IWOA).First,the IDBN acts as a high-precision nonlinear prediction model,significantly improving multi-step prediction accuracy.Second,the IWOA is employed to optimize the predictive controller,featuring three major improvements:an improved population initialization,a modified convergence factor update mechanism,and an added disturbance strategy,which collectively accelerate convergence and enhance global search capability.Finally,simulation results demonstrate that the proposed NNPC algorithm achieves superior set-point tracking performance and strong robustness against external disturbances. 展开更多
关键词 industrial refrigeration systems model predictive control time⁃delay whale optimization algorithm deep belief network
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Nonlinear model predictive control strategy for vehicle path tracking with steering actuator’s dynamic characteristics 认领 引用
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作者 Bin Wang Zhiguo Zhao +1 位作者 Xiwen Yuan Yuyi Wang 《Control Theory and Technology》 EI CSCD 2026年第3期359-373,共15页
The dynamic characteristics of the steering actuator,including response time-delay and calibration deviation,are crucial to a vehicle’s path tracking performance.This study proposes a novel vehicle path tracking cont... The dynamic characteristics of the steering actuator,including response time-delay and calibration deviation,are crucial to a vehicle’s path tracking performance.This study proposes a novel vehicle path tracking control strategy by combining nonlinear model predictive control(NMPC),time-delay model control,and calibration deviation compensation control.In the first stage,the path tracking NMPC strategy is designed without considering the steering actuator’s dynamic characteristics.In the second stage,by combining polynomial fitting with linear matrix inequality(LMI)techniques,a nonlinear time-delay model control algorithm is designed to address the response time-delay.In the third stage,based on the inverse model of the calibration deviation,a nonlinear compensation algorithm is designed for steering actuator’s calibration deviation.Finally,simulation and real vehicle experiment results are provided to illustrate the effectiveness of the proposed vehicle path tracking control strategy. 展开更多
关键词 Nonlinear model predictive control(NMPC) Path tracking Nonlinear time-delay model control Calibration deviation compensation control
Enforcing spatio-temporal collaborations in distributed predictive control under temporally relaxed STL specifications 认领 引用
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作者 Hongbo Yang Yuanyuan Zou Shaoyuan Li 《Control Theory and Technology》 EI CSCD 2026年第2期203-213,共11页
This paper introduces spatio-temporal collaboration(STC),a novel formalism for coordinating multi-agent systems under signal temporal logic(STL).STC defines critical inter-agent dependencies that may be violated by th... This paper introduces spatio-temporal collaboration(STC),a novel formalism for coordinating multi-agent systems under signal temporal logic(STL).STC defines critical inter-agent dependencies that may be violated by the cascading delays resulting from temporal relaxation,a common method for resolving local task conflicts.To address this issue,we first analyze the propagation of task delays through dependent tasks.A time interval refinement strategy is then proposed to maintain the required collaborations.This strategy is integrated into a distributed predictive control algorithm,ensuring simultaneous satisfaction of both STL specifications and STC relations while preserving recursive feasibility and closed-loop stability.Validation via a case study demonstrates the effectiveness of proposed strategy in preventing collaboration failures. 展开更多
关键词 Collaborative control Signal temporal logic Multi-agent system Temporal relaxation Distributed predictive control
Switched model predictive control with non-uniform penalty for switched systems 认领 引用
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作者 Feiyue Wu Jie Lian Wei Wang 《Control Theory and Technology》 EI CSCD 2026年第2期260-270,共11页
This paper presents a switched model predictive control(MPC)with non-uniform penalty for discrete-time constrained switched linear systems.A non-uniform time-varying stage cost penalty is incorporated to reduce the re... This paper presents a switched model predictive control(MPC)with non-uniform penalty for discrete-time constrained switched linear systems.A non-uniform time-varying stage cost penalty is incorporated to reduce the required prediction horizon while guaranteeing closed-loop stability.To address feasibility challenges arising from mode-dependent state constraints,switching invariant sets are constructed to ensure persistent feasibility under minimal dwell-time constraints.Explicit conditions on dwell time,weighting parameters,and penalty rates are derived to guarantee asymptotic stability of the closedloop system.Furthermore,an algorithm for computing the maximal switching invariant sets is developed.The proposed method is validated through a vehicle longitudinal control case study,demonstrating its effectiveness and computational efficiency. 展开更多
关键词 Switched system Model predictive control Switching invariant set Dwell-time constraint
Structural control performance of a force-constrained tuned inertial mass electromagnetic transducer using model predictive control with acceleration feedback 认领 引用
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作者 Takehiko Asai Luis Nunez 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第2期537-553,共17页
The tuned viscous mass damper(TVMD),composed of an inerter,a tuning spring,and a damping component,has emerged in recent years as one of the most successful structural control devices.The TVMD efficiently absorbs vibr... The tuned viscous mass damper(TVMD),composed of an inerter,a tuning spring,and a damping component,has emerged in recent years as one of the most successful structural control devices.The TVMD efficiently absorbs vibration energy through the damping component by resonating the inerter with the tuning spring.However,concerns have been raised regarding the excessive reaction forces that are exerted on the primary structure by the TVMD,potentially leading to structural damage.To address this issue,this study proposes an active control strategy that employs a tuned inertial mass electromagnetic transducer(TIMET).The TIMET has the same configuration as the TVMD but replaces the damping component with an electromagnetic motor,which is controlled by using a model predictive control(MPC)algorithm with acceleration feedback.To evaluate the structural control performance and the control force generated by the proposed strategy,a numerical example involving a five-story shear building model with TIMETs installed between floors is presented.Comparative analyses are conducted using models lacking control devices but including TVMDs,subjected to four earthquake records.The results demonstrate that the force-constrained MPC strategy effectively limits the control forces of the TIMETs,while simultaneously reducing the response displacements and accelerations of the primary structure. 展开更多
关键词 structural control model predictive control inerter tuned viscous mass damper tuned inertial mass electromagnetic transducer
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A data-driven modeling approach for predictive control of mixed traffic flow 认领 引用
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作者 Yue Zuo Xudong Qi +2 位作者 Huifeng Hu Tao Zou Ping Wang 《Control Theory and Technology》 EI CSCD 2026年第2期293-306,共14页
Effective control of mixed traffic flow remains challenging due to vehicle behavior uncertainty and complex interactions.This paper proposes a data-driven control strategy for connected and autonomous vehicles(CAVs)in... Effective control of mixed traffic flow remains challenging due to vehicle behavior uncertainty and complex interactions.This paper proposes a data-driven control strategy for connected and autonomous vehicles(CAVs)in mixed traffic flow,implemented through variable speed limits and lane-changing guidance.First,a cellular automata model of mixed traffic flow is developed,with adjustable CAVs'maximum speed limit and lane-changing probability,thereby linking microscopic CAV operating rules to macroscopic traffic flow dynamics.Second,a recurrent neural network(RNN)is employed to capture the temporal dynamics of the traffic system and predict the evolution of traffic flow states.The RNN is then linearized via the Koopman operator,transforming the complex nonlinear model into a linear representation for the design of a computationally efficient model predictive controller.Finally,simulation results demonstrate that the strategy increases the average traffic speed by 14.2% across 12 traffic scenarios.Specifically,under the challenging conditions of high traffic density with low CAV penetration,it achieves a 5.22% improvement and promotes a more uniform vehicle distribution.These findings highlight the potential of the proposed approach for mixed traffic flow regulation. 展开更多
关键词 Mixed traffic flow Model predictive control Cellular automata model Koopman operator
Analysis of Metaheuristic,Sampling-Based,Potential Field,and Predictive Control Methods for Path Planning in Simulated Underwater Settings 认领 引用
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作者 Rubina Castro Bruno Silva +1 位作者 Luiz Guerreiro Lopes Fábio Mendonça 《Computers, Materials & Continua》 SCIE EI 2026年第8期498-523,共26页
Path planning for autonomous underwater vehicles requires reliable and computationally efficient methods,particularly in cluttered environments.This work presents a comparative evaluation of representative approaches,... Path planning for autonomous underwater vehicles requires reliable and computationally efficient methods,particularly in cluttered environments.This work presents a comparative evaluation of representative approaches,including metaheuristic optimization methods(continuous genetic algorithm,particle swarm optimization,gray wolf optimizer,and Jaya),a sampling-based method(probabilistic roadmap with genetic refinement),a reactive strategy(artificial potential fields),and a control-based approach(model predictive control with control barrier functions).The algorithms are assessed in a controlled two-dimensional simulated workspace with randomly generated obstacles and systematically increasing obstacle density.Each configuration is evaluated across multiple independent trials using metrics such as success rate,path length,and convergence behavior.The effect of environmental disturbances is examined by analyzing particle swarm optimization under Gauss–Markov current models.The results show that performance depends strongly on the ability to preserve feasibility as obstacle density increases.The probabilistic roadmap with genetic refinement demonstrated the highest robustness,maintaining feasibility across all scenarios,while particle swarm optimization provided a strong balance between path quality and reliability in low-to-moderate clutter.The introduction of current disturbances led to reduced efficiency and consistency.Statistical analysis confirmed significant differences among methods,highlighting that rank-based superiority does not necessarily reflect practical robustness in constrained environments. 展开更多
关键词 Autonomous underwater vehicles path planning collision avoidance metaheuristic optimization potential fields sampling-based planning predictive control
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Input Mapping-Based Model Predictive Control with Event-Triggered Adaptive Strategy for Rigid-Soft Hybrid Manipulator 认领 引用
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作者 LI Guolin CHEN Tong +1 位作者 He Shaoying Yin Debin 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第1期36-47,共12页
Uncertain loads of the rigid-soft hybrid manipulator directly affect working configurations,which will alter the system model parameters,and thereby degrade control accuracy and efficiency.This paper introduces an eve... Uncertain loads of the rigid-soft hybrid manipulator directly affect working configurations,which will alter the system model parameters,and thereby degrade control accuracy and efficiency.This paper introduces an event-triggered adaptive model predictive control strategy,which integrates with a data-driven approach to control hybrid robots with a cable-driven soft component.In the presence of model uncertainty and mismatch,adaptive identification is employed to improve the nominal model within the controller.Meanwhile,an event-triggered scheme is utilized to reduce redundant identification frequency and improve computing efficiency.Furthermore,an online data-driven method,called input mapping,uses the relationship between the historical input and output data to compensate for the minor model error in the controller via linear combination.The optimization problem is efficiently solved by designing the attenuation coefficient in an infinite-domain situation.Comparative simulation and experimental results demonstrate that the proposed method achieves improved accuracy and faster convergence speed. 展开更多
关键词 rigid-soft hybrid robot input mapping event-triggered adaptive model predictive control recursive feasibility stability
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Koopman operator-based stable economic model predictive control for nonlinear systems 认领 引用
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作者 Jie Wu Ke Li Fei Liu 《Control Theory and Technology》 EI CSCD 2026年第2期250-259,共10页
This paper presents a data-driven economic model predictive control(EMPC)framework for nonlinear systems.Leveraging Koopman operator theory and the extended dynamic mode decomposition method,a lifted linear model in t... This paper presents a data-driven economic model predictive control(EMPC)framework for nonlinear systems.Leveraging Koopman operator theory and the extended dynamic mode decomposition method,a lifted linear model in the high-dimensional function space of the nonlinear dynamics is first identified from the collected dataset.Then,an EMPC strategy used to optimize process economics is designed in the lifted space,which employs the Koopman linear model as the predictor.To guarantee closed-loop stability,an artificial constraint is constructed by solving a convex quadratic programming problem.The recursive feasibility and closed-loop stability of the proposed approach are rigorously analyzed.Benefiting from the linear structure of the Koopman model,the online computational burden of the EMPC is substantially reduced.The effectiveness of the proposed method is demonstrated through simulations on a nonlinear chemical reactor. 展开更多
关键词 Closed-loop stability Economic model predictive control Koopman operator Recursive feasibility
Data-driven non-Gaussian stochastic dynamical system identification meets knowledge-based model predictive control 认领 引用
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作者 Yicheng Mao Xianbin Liu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期71-85,共15页
Randomness and nonlinearity are essential properties of the real world,and their interaction gives rise to highly complex phenomena.With the advancement of technology,merely observing data of the current system state ... Randomness and nonlinearity are essential properties of the real world,and their interaction gives rise to highly complex phenomena.With the advancement of technology,merely observing data of the current system state is no longer sufficient for prediction and application in various fields.Consequently,extracting the nonlinear evolution nature of the system from noisy data has become a prominent and challenging issue.To address this,we propose an integrated approach that combines data-driven stochastic model identification with a knowledge-based model predictive control strategy.By leveraging high-precision model identification,our data-driven control design is particularly effective for continuous target tracking problems that are difficult to address using traditional precise-model-based control theory.Furthermore,the central challenge in data science lies in maximizing the informational value of datasets while minimizing the effects of observation noise.In this study,we propose and rigorously demonstrate the stochastic Occam’s razor principle,a stochastic error estimation theory that evaluates and enhances the design of data-driven schemes to mitigate the effect of observation noise.Notably,our approach offers valuable insights for contemporary data-driven,end-to-end control challenges,particularly those involving uncertain governing equations and substantial non-Gaussian observation noise. 展开更多
关键词 Nonlinear stochastic dynamical system Data-driven system identification Non-Gaussian Lévy noise Data-driven model predictive control
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