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Synchronization Learning Scheme of Hybrid Order adaptive dynamic Optimizations for Secure Communication

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 4110-4120页

作者： Zhang, Kun Zhang, Huaguang Zhao, Yanlong Wu, Huai-Ning Su, Rong Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China Beihang Univ Hangzhou Int Innovat Inst Hangzhou 311115 Peoples R China Nanyang Technol Univ Ctr Adv Robot Technol Innovat CARTIN Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, a novel synchronization learning scheme is proposed for secure communication, where the signal transmission architecture with a chaotic encryption process is considered. Firstly, to realize the information security in communication, the original signals are encrypted by fractional order dynamics from the sender, and decrypted by receiver to achieve synchronization. For the process, a hybrid order dynamic optimization is constructed, where the fractional order and the integer order systems are modeled as constraints. Secondly, a transformation formula is developed to convert these constraints into new integer order dynamics, and the equivalence between two dynamic optimizations is obtained. Thirdly, to obtain the synchronization solution, a new iterative learning algorithm is designed, and the adaptive dynamic programming is successfully embedded into the solving process. Finally, we apply the proposed synchronization scheme into the secure image transmission, and the simulation results demonstrate the effectiveness and practicality successfully.

关键词： Vectors Synchronization Heuristic algorithms Receivers Oscillators Optimization Chaotic communication Information security Encryption Privacy Synchronization learning hybrid-order system optimal tracking scheme adaptive dynamic programming secure communication

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Fault-tolerant control for nonlinear time-delay systems using neural network observers

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INTERNATIONAL JOURNAL OF dynamicS AND CONTROL 2025年第1期13卷 1-12页

作者： Rahimi, Farshad Sahand Univ Technol Dept Elect Engn 85 Noori Alley St Nowsud *** Iran

This article addresses the problem of fault-tolerant control in nonlinear time-delay systems using adaptive dynamic programming. An adaptive neural network observer is developed to estimate unknown dynamics, system states, and actuator faults. This observer is then transformed into an augmented structure for optimal fault-tolerant control problem. The gains of this observer are determined by solving a linear matrix inequality. A new value function index is introduced to account for time-delay states, and control law is derived associated with this novel value function. The Hamilton-Jacobi-Bellman equation for this value function is solved via a critic neural network. Lyapunov functional analysis demonstrates that the closed-loop system remains uniformly ultimately bounded. Simulation results validate the proposed fault tolerant approach. The key contribution of this paper lies in incorporating time-delay states into the adaptive dynamic programming value function in the presence of actuator faults.

关键词： Neural network observer adaptive dynamic programming Fault tolerant Time-delay systems

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Output Regulation Based on Zero-Sum Game for Discrete-Time System Driven by Exogenous Signal

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年

作者： Song, Ruizhuo Yang, Gaofu Lewis, Frank L. Univ Sci & Technol Beijing Beijing Engn Res Ctr Ind Spectrum Imaging Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Texas Arlington UTA Res Inst Ft Worth TX 76118 USA

This article proposes a novel Q-learning algorithm that relies solely on input-output data to address the output regulation control problem of complex discrete-time systems affected by exogenous signals. Unlike traditional methods, this algorithm does not require detailed system information, state knowledge, or data about external systems or exogenous signals. Additionally, the control strategy does not depend on state information, but on input-output data processed by a set of filters. We provide upper and lower bounds on the discount factor, eliminating the need to solve the Riccati equation. These bounds ensure that the value function remains finite, and we prove the stability of the system when using control inputs derived from the value function with the given discount factor. Furthermore, the Q-learning algorithm, when applied with input data containing probing noise, is shown to yield Q-function estimates that are independent of the probing noise. Finally, a simulation involving a grid-connected inverter is presented, demonstrating the effectiveness of the proposed algorithm in a practical setting.

关键词： Regulation Q-learning Noise Optimal control Riccati equations Heuristic algorithms Game theory Output feedback Games Asymptotic stability adaptive dynamic programming discrete-time systems game theory output feedback reinforcement learning

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Firefly optimized neural network-based trajectory tracking control of partially unknown multiplayer nonlinear systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第18期34卷 12187-12206页

作者： Wu, Qiuye Zhao, Bo Liu, Derong Guangdong Police Coll Sch Publ Secur & Traff Management Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL USA

In this paper, we develop an integral reinforcement learning (IRL)-based trajectory tracking control (TTC) scheme via firefly optimized neural networks for partially unknown multiplayer nonlinear systems. Under the developed TTC scheme, IRL is proved to be equivalent to the classical policy iteration, which guarantees the convergence of the IRL algorithm. By implementing the IRL method, the requirement of the drift dynamics is obviated. The TTC policy for each player is obtained by solving the coupled Hamilton-Jacobi equation with a critic neural network, whose weight vector is tuned by the firefly algorithm. The tracking error of the closed-loop system is guaranteed to be stable via the Lyapunov's direct method. Simulation results illustrate the effectiveness and superiority of the present IRL-based TTC scheme, and show that the success rate of system operation is increased by introducing the firefly algorithm.

关键词： adaptive dynamic programming firefly algorithm integral reinforcement learning multiplayer nonlinear systems neural networks trajectory tracking control

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adaptive Optimal Control via Q-Learning for Multi-Agent Pursuit-Evasion Games

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第6期71卷 3056-3060页

作者： Dong, Xu Zhang, Huaguang Ming, Zhongyang Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China

The multi-agent pursuit-evasion (PE) games are solved in this brief to find the best possible strategic options for every participant. In these games, numerous pursuers aim to catch numerous evaders which are trying to escape arrest. In order to determine distributed control rules for each agent, a graph-theoretic technique is used to examine how the agents with restricted sensing capabilities interact with one another. Further, the online and real-time solution of Hamilton-Jacobi-Bellman (HJB) equations is necessary to achieve the optimal actions. By introducing the Q -learning technique's policy iteration, we are able to solve this issue. Once capture, the game is over. The simulation results are shown to demonstrate the viability of the suggested techniques.

关键词： Multi-agent systems Mathematical models Games Technological innovation Symmetric matrices Sensors Q-learning adaptive dynamic programming Pursuit-evasion game

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Advanced optimal tracking integrating a neural critic technique for asymmetric constrained zero-sum games ☆

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NEURAL NETWORKS 2024年 177卷 106388页

作者： Li, Menghua Wang, Ding Ren, Jin Qiao, Junfei Beijing Univ Technol Fac Informat 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

This paper investigates the optimal tracking issue for continuous -time (CT) nonlinear asymmetric constrained zero -sum games (ZSGs) by exploiting the neural critic technique. Initially, an improved algorithm is constructed to tackle the tracking control problem of nonlinear CT multiplayer ZSGs. Also, we give a novel nonquadratic function to settle the asymmetric constraints. One thing worth noting is that the method used in this paper to solve asymmetric constraints eliminates the strict restriction on the control matrix compared to the previous ones. Further, the optimal controls, the worst disturbances, and the tracking Hamilton-Jacobi-Isaacs equation are derived. Next, a single critic neural network is built to estimate the optimal cost function, thus obtaining the approximations of the optimal controls and the worst disturbances. The critic network weight is updated by the normalized steepest descent algorithm. Additionally, based on the Lyapunov method, the stability of the tracking error and the weight estimation error of the critic network is analyzed. In the end, two examples are offered to validate the theoretical results.

关键词： adaptive dynamic programming Asymmetric input constraints Multiplayer zero-sum games Neural critic technique Optimal tracking control

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Online robust self-learning terminal sliding mode control for balancing control of reaction wheel bicycle robots

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ROBOTICA 2024年第10期42卷 3416-3430页

作者： Zhu, Xianjin Xu, Wenfu Chen, Zhang Deng, Yang Zheng, Qingyuan Liang, Bin Liu, Yu Harbin Inst Technol Sch Mechatron Engn Harbin Peoples R China Harbin Inst Technol Sch Mechatron Engn & Automat Shenzhen Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

This paper proposes an online robust self-learning terminal sliding mode control (RS-TSMC) with stability guarantee for balancing control of reaction wheel bicycle robots (RWBR) under model uncertainties and disturbances, which improves the balancing control performance of RWBR by optimising the constrained output of TSMC. The TSMC is designed for a second-order mathematical model of RWBR. Then robust adaptive dynamic programming based on an actor-critic algorithm is used to optimise the TSMC only by data sampled online. The system closed- loop stability and convergence of the neural network weights are guaranteed based on the Lyapunov analysis. The effectiveness of the proposed algorithm is demonstrated through simulations and experiments.

关键词： Control of robotic systems adaptive dynamic programming sliding mode control reaction wheel bicycle robots balancing control

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Robust tracking control for uncertain Euler-Lagrange systems via dynamic event-triggered and self-triggered ADP

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第1期34卷 481-505页

作者： Chen, Lu Hao, Fei Beihang Univ Sch Automat Sci & Elect Engn Res Div 7 Beijing Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Res Div 7 Beijing 100191 Peoples R China

This article investigates the robust tracking control problem for a class of Euler-Lagrange systems in presence of parameter uncertainties and external disturbances. Through system transformation and theoretical analysis, an adaptive dynamic programming (ADP) algorithm with two adaptive neural networks (NNs) and a suitable triggering mechanism is proposed to attain the robust stability of the closed-loop system. A single critic NN is leveraged to implement the approximate optimal controller design. Particularly, an NN-based feedforward compensation is developed to cope with the uncertainties with unknown bounds. Two different triggering mechanisms are respectively constructed to reduce the budget of sampling, communication and computation, namely the dynamic event-triggering mechanism (DETM) and the self-triggering mechanism (STM). The DETM is utilized to decide the update of remote controller and critic NN weight, which can yield a larger inter-event interval than the static event-triggering mechanism. Also, the Zeno-free behavior is guaranteed. Moreover, it is a novel attempt to introduce the STM into ADP design, which relaxes the demand of dedicated hardware online monitoring the event-triggering condition. Then it is demonstrated that all signals in the closed-loop system are uniformly ultimately bounded (UUB) via Lyapunov-based stability analysis. Finally, a simulation example of 2-link robotic system is implemented to verify the feasibility and effectiveness of the proposed algorithm.

关键词： adaptive dynamic programming dynamic event-triggering mechanism Euler-Lagrange systems self-triggering mechanism uncertainties

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Integral sliding mode-based event-triggered nearly optimal tracking control for uncertain nonlinear systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第4期34卷 2639-2658页

作者： Zhang, Shunchao Wang, Yonghua Liu, Dacai Zhuang, Jiawei Zhang, Yongwei Guangdong Univ Finance Sch Internet Finance & Informat Engn Guangzhou Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China

In this paper, an event-triggered nearly optimal tracking control method is investigated for a class of uncertain nonlinear systems by integrating adaptive dynamic programming (ADP) and integral sliding mode (ISM) control techniques. An ISM-based discontinuous control law with a neural network (NN) adaptive term is designed to eliminate the influence of the uncertainties and obtain the sliding mode dynamics which is equivalent to the tracking error dynamics without uncertainties, and relax the known upper-bounded condition of uncertainties. In order to guarantee the stability of tracking error system and the considerable optimality, under the ADP technique, a critic NN is applied to approximate the optimal value function for solving the event-triggered Hamilton-Jacobi-Bellman equation and the event-triggered nearly optimal feedback control is obtained. The feedback control law is updated and transmitted to plant only when events occur, thus both the communication and the computational resources can be saved. Furthermore, the stability of tracking error is proven thanks to Lyapunov's direct method. Finally, we provide two simulation examples to validate the developed control scheme.

关键词： adaptive dynamic programming event-triggered mechanism integral sliding mode control neural networks uncertain systems

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Bilayer nonzero-sum differential game-based optimal control of modular robot manipulator for human-robot collaboration

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EUROPEAN JOURNAL OF CONTROL 2025年 83卷

作者： Cui, Yiming An, Tianjiao Dong, Bo Ma, Bing Zhang, Zhenguo Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China Jilin Commun Polytech Inst Rail Transit Changchun 130012 Jilin Peoples R China Changchun Univ Technol Minist Educ Key Lab Adv Struct Mat Changchun 130012 Jilin Peoples R China

This paper introduces a bilayer nonzero-sum differential game-based optimal control framework for a Modular Robot Manipulator (MRM) in Human-Robot Collaboration (HRC) tasks. The dynamic model of the MRM is obtained with the Joint Torque Feedback (JTF) technique. Consider the N-player nonzero-sum differential game within the MRM subsystems and the 2-player nonzero-sum differential game involving both the MRM and human collaborators in HRC tasks as the inner and outer layers of the bilayer nonzero-sum differential game. The Nash equilibrium solutions for the inner and outer layers of the nonzero-sum differential games are independently determined using the adaptive dynamic programming (ADP) algorithm, which is based on a fuzzy logic system. As a result, optimal control policies for MRM subsystems and the optimal interaction force in HRC tasks are derived. The trajectory tracking error of the MRM system and the outer layer physical Human-Robot Interaction (pHRI) system have both been proven to be Ultimately Uniformly Bounded (UUB) under the bilayer nonzero-sum differential game-based optimal control of MRM for HRC with the application of Lyapunov theory. Finally, experiment results are presented to validate the superiority and effectiveness of the proposed method.

关键词： Modular robot manipulator Nonzero-sum differential game adaptive dynamic programming Fuzzy logic

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