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dynamic event-triggered finite-horizon robust suboptimal control of multi with disturbances

NEUROCOMPUTING 2025年 611卷

作者： Zou, Haoming Zhang, Guoshan Yan, Zhiguo Liu, Wanquan Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Elect Engn & Automat Jinan 250353 Shandong Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

This paper presents an adaptive dynamic event-triggered robust finite-horizon suboptimal control scheme for multi-player nonzero-sum game of nonlinear systems with input disturbances based on dynamic programming (ADP) and integral sliding mode (ISM) control techniques. Firstly, dynamic event-triggered adaptive ISM controllers are designed to handle input disturbances that can force the system state to remain on the sliding manifold and relax the known upper bounds condition of input disturbances. Corresponding event-triggered condition is employed for ISM controller of each player to reduce the update frequency. Then, utilizing the obtained ISM dynamics, we employ ADP-based single critic neural networks to approximate the time-varying solution of the Hamilton-Jacobi equations. Thus, the event-triggered finite-horizon suboptimal controllers can be obtained, and corresponding state-based dynamic event-triggered condition is designed to improve the resource utilization. The stability property of the closed-loop system is demonstrated through the Lyapunov technique. Finally, the effectiveness of the proposed control scheme is confirmed through two simulation examples.

关键词： Integral sliding mode control Multi-player nonlinear systems adaptive dynamic programming Finite-horizon suboptimal control

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Extended Sequential Truncation Technique for adaptive dynamic programming Based Security-Constrained Unit Commitment with Optimal Power Flow Constraints

Extended Sequential Truncation Technique for Adaptive Dynami...

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第六届亚太电力与能源国际学术会议

作者： Danli Long Hua Wei Guangxi key Laboratory of Power System Optimization and Energy Technology Guangxi University

Considering the economics and securities for the operation of a power system, this paper presents a new adaptive dynamic programming approach for security-constrained unit commitment(SCUC) problems. In response to the "curse of dimension" problem of dynamic programming, the approach solves the Bellman's equation of SCUC approximately by solving a sequence of simplified single stage optimization problems. An extended sequential truncation technique is proposed to explore the state space of the approach, which is superior to traditional sequential truncation in daily cost for unit commitment. Different test cases from 30 to 300 buses over a 24 h horizon are analyzed. Extensive numerical comparisons show that the proposed approach is capable of obtaining the optimal unit commitment schedules without any network and bus voltage violations, and minimizing the operation cost as well.

关键词： Power System Operation and Planning Priority Order adaptive dynamic programming Unit Commitment

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adaptive learning-based optimal tracking control system design and analysis of a disturbed nonlinear hypersonic vehicle model

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Science China(Technological Sciences) 2024年第6期67卷 1893-1906页

作者： AN Kai WANG ZhenGuo HUANG Wei Hypersonic Technology Laboratory National University of Defense TechnologyChangsha410073China

We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model uncertainties and external disturbances of hypersonic winged-cone *** longitudinal nonlinear model is first established and transformed into the control-oriented error equations,and the control scheme is organized by a steady-compensation *** overcome and eliminate the impact of model uncertainties and external disturbances,an adaptive radial basis function neural network(RBFNN)is designed by a q-gradient *** the height-velocity error system with estimated uncertainties into account,the adaptive learning-based optimal tracking control(ALOTC)scheme is proposed by combining the critic-only adaptive dynamic programming(ADP)framework and parameter optimization of system settling ***,a novel weight update law is proposed to satisfy the online iteration requirements,and the algorithm convergence and closedloop stability are discussed by the Lyapunov ***,four simulation cases are provided to prove the effectiveness,accuracy,and robustness of the proposed scheme for the hypersonic longitudinal control system.

关键词： optimal tracking control adaptive dynamic programming RBFNN hypersonic vehicle

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Data-Driven Direct adaptive Risk-Sensitive Control of Stochastic Systems

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Journal of Systems Science & Complexity 2024年第4期37卷 1446-1469页

作者： QIAO Nan LI Tao School of Mathematical Sciences East China Normal UniversityShanghai 200241China Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice School of Mathematical SciencesEast China Normal UniversityShanghai 200241China

The authors propose a data-driven direct adaptive control law based on the adaptive dynamic programming(ADP) algorithm for continuous-time stochastic linear systems with partially unknown system dynamics and infinite horizon quadratic risk-sensitive *** authors use online data of the system to iteratively solve the generalized algebraic Riccati equation(GARE) and to learn the optimal control law *** the case with measurable system noises,the authors show that the adaptive control law approximates the optimal control law as time goes *** the case with unmeasurable system noises,the authors use the least-square solution calculated only from the measurable data instead of the real solution of the regression equation to iteratively solve the *** authors also study the influences of the intensity of the system noises,the intensity of the exploration noises,the initial iterative matrix,and the sampling period on the convergence of the ADP ***,the authors present two numerical simulation examples to demonstrate the effectiveness of the proposed algorithms.

关键词： adaptive dynamic programming direct adaptive control generalized algebraic Riccati equation risk-sensitive control

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ADP-Based Self-Triggered Optimal Control of Active Loads in DC Microgrid

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第1期72卷 193-197页

作者： Su, Hanguang Zhi, Gan Zhang, Huaguang Wang, Jiawei Strbac, Goran Ren, He Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang 110819 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Taiyuan Univ Technol Coll Data Sci Taiyuan 030606 Peoples R China

In this brief, an adaptive dynamic programming (ADP)-based self-triggered control (STC) method was proposed to address the optimization control problem of power buffer systems in DC microgrids. The optimization control problem of power buffers is addressed in the framework of non-zero sum games to ensure mutual cooperation among power buffers. In the proposed STC mechanism, the next triggering moment is determined by the current triggering information, avoiding continuous monitoring of devices under the event-triggered control (ETC) and reducing the occupation of system communication and computing resources. Besides, an experience replay (ER) method is introduced when updating the weights of the critic neural networks (NNs). The proposed method ensures the stability of the system, eliminates the Zeno phenomenon, and leads to an adjustable positive minimum triggering interval. The effectiveness of the proposed method is ultimately verified by using a DC microgrid case study.

关键词： Microgrids Optimal control Optimization Impedance Capacitors Noise Circuits adaptive dynamic programming self-triggered control power buffer DC microgrid event-triggered control

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Decentralized Robust Tracking Control of Interconnected Nonlinear-Constrained Systems: A dynamic Event-Sampled Method

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第4期35卷 1486-1499页

作者： Zheng, Wenqian Yang, Xiong Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

This paper presents a decentralized robust dynamic event-sampled tracking (EST) control law for interconnected nonlinear-constrained systems. The core of developing such a control law is to convert the original EST control problem into the event-sampled decentralized stabilization problem of augmented interconnected systems. To address the transformed decentralized stabilization problem, an indirect approach relying on the optimal control methodology is proposed. Initially, a group of cost functions are constructed for the nominal subsystems related to the augmented interconnected systems. Then, the dynamic event-sampling mechanisms are introduced for lessening the computational burden. Meanwhile, the event-sampled Hamilton-Jacobi-Bellman equations (ES-HJBEs) are proposed for the augmented interconnected systems. To approximately solve the ES-HJBEs, the critic approximators are used with their parameters tuned under the reinforcement learning framework. After that, the uniform ultimate boundedness of the tracking errors and the approximators' parameter estimation errors are assured based on the Lyapunov theorem. Finally, a nonlinear plant is provided to validate the decentralized robust dynamic EST control law.

关键词： adaptive dynamic programming asymmetric constraints decentralized control event-sampled control reinforcement learning tracking control

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Model-free tracking design for nonlinear zero-sum games with an improved utility function

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NONLINEAR dynamicS 2025年第13期113卷 16679-16694页

作者： Wang, Ding Tang, Guohan Ren, Jin Zhao, Mingming Qiao, Junfei Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

In this article, an advanced accelerated Q-learning (AQL) approach is designed to address the nonlinear discrete-time optimal tracking problem of zero-sum games with unknown dynamics. Different from conventional adaptive dynamic programming methods, the advanced Q-learning algorithm incorporates both the control input and the disturbance signal into the tracking error, which obviates the quadratic form of control and disturbance inputs directly. This innovative Q-function is used to derive the optimal tracking control policy pair that ensures the terminal tracking error asymptotically converges to zero, independent of the feedforward control input. In order to improve the convergence speed of the iterative process and reduce computational complexity, an accelerated factor is introduced. After collecting offline input-output data, a backpropagation neural network is employed to approximate the proposed Q-function, which enables model-free tracking control of zero-sum games through an off-policy learning mechanism. Furthermore, the theoretical properties of the developed algorithm are analyzed under specific preconditions. Finally, the effectiveness of the AQL algorithm is validated through a numerical simulation, which is implemented using a critic-only structure.

关键词： adaptive dynamic programming Accelerated Q-learning Neural networks Nonlinear tracking control Zero-sum games

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Swarm-intelligence-based value iteration for optimal regulation of continuous-time nonlinear systems

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Wang, Ding Hu, Qinna Liu, Ao Qiao, Junfei Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

In this article, a swarm-intelligence-based value iteration (VI) algorithm is constructed to resolve the optimal control issue for continuous-time (CT) nonlinear systems. By leveraging the evolutionary concept of particle swarm optimization (PSO), the challenge of gradient vanishing is effectively overcome compared to traditional adaptive dynamic programming (ADP). Specifically, a PSO-based action network is implemented to perform policy improvement, eliminating the reliance on gradient information. Furthermore, within the ADP framework, the swarm-intelligence-based VI algorithm for CT systems is developed to address the challenges associated with constraints of initial admissible conditions and the difficulty of selecting probing signals in the traditional policy iteration method. The theoretical analysis is provided to show the convergence of the developed VI algorithm and the stability of the closed-loop system, respectively. Finally, under affine and non-affine backgrounds, two simulations are conducted to demonstrate the effectiveness and optimality of the established swarm-intelligence-based VI scheme for CT systems.

关键词： adaptive dynamic programming Continuous-time nonlinear systems Intelligent optimal control Particle swarm optimization Reinforcement learning Value iteration

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A nonlinear multi-player non-zero-sum differential game with state constraints and input delay

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NONLINEAR dynamicS 2025年第6期113卷 5383-5408页

作者： Zhou, Yinglu Li, Yinya Sheng, Andong Qi, Guoqing Nanjing Univ Sci & Technol Sch Automat Nanjing 210000 Peoples R China

This paper investigates a nonlinear multi-player non-zero-sum differential game with state constraints and input delay. To solve this problem, this paper constructs a neural network (NN) framework to approximate the solution of the Hamilton-Jacobi-Isaacs equation. The adaptive dynamic programming method is utilized where each player only needs one critic NN. To solve the issue of state saturation and input delay, this paper develops a novel state constraints mechanism for the differential game to make the states within the predetermined constraint set. Then, a future predictor is employed to compensate for the input delay. The condition that estimated states of agents having nonlinear dynamics with the predictor converge to the real state is derived. With these treatments, the system is more in line with real-world applications. Moreover, the stability of the system is also analyzed using the Lyapunov theorem. Simulation examples are given to validate the efficiency of the presented methods.

关键词： Differential game Distributed control adaptive dynamic programming State constraints Input delay

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dynamic Event-Triggered ADP Optimized Tracking Control of Marine Surface Vessels With Asymmetric Time-Varying Full-State Constraint

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第7期25卷 7352-7362页

作者： Shen, Zhipeng Zhou, Lu Yang, Yang Liu, Yingjie Yu, Haomiao Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Liaoning Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China

An optimal tracking control problem is critical for marine surface vessels (MSVs) with full state constraints, input constraints, external disturbances, and model uncertainty terms. For solving an MSV's position and velocity constraints, a barrier Lyapunov function (BLF) and control barrier function (CBF) are introduced and combined with prescribed performance functions, and then, an adaptive dynamic programming (ADP) optimized tracking control strategy is presented based on the two functions. A dynamic event-triggered mechanism is employed, which reduces the number of triggers by tracking the intermittent transmission of error signals, thus avoiding actuator wear and excessive consumption of communication resources. The experience replay method is brought in the gradient descent method for improvement of the weight learning efficiency. The Lyapunov-based stability analysis shows that the designed tracking control strategy ensures that all signals and weights in the closed-loop system are semi-globally bounded with Zeno-free behavior. Numerical comparison simulation verifies the effectiveness and superiority of the strategy.

关键词： Marine surface vessel trajectory tracking event-triggered control adaptive dynamic programming control barrier function barrier Lyapunov function

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