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Distributed data-driven optimal synchronisation of multi-agent systems with input saturation

INTERNATIONAL JOURNAL OF CONTROL 2025年

作者： Cai, Xuan Wang, Gang Liu, Shuxin Shanghai Dianji Univ Sch Elect Engn Shanghai 201306 Peoples R China Univ Shanghai Sci & Technol Inst Machine Intelligence Shanghai Peoples R China

This paper discusses the optimal synchronisation problem of multi-agent systems with unknown system dynamics, where each agent is subject to both input saturation and external disturbances. A novel data-driven control approach is developed in this paper based on low-gain technique, output regulation, differential game theory, and adaptive dynamic programming (ADP). Unlike existing approaches to the data-driven optimal synchronisation problem, our method eliminates the need for an initially admissible stabilising control policy, and the proposed distributed control law ensures asymptotic tracking even in the presence of both modelling disturbances and unmodeled disturbances.

关键词： Multi-agent systems optimal synchronisation input saturation adaptive dynamic programming value iteration

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Integrated Online Q-Learning Design for Wastewater Treatment Processes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第2期21卷 1833-1842页

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

The efficiency and economy of the nonlinear optimal control process in wastewater treatment plants are two crucial indicators, corresponding to achieving the control objective faster and reducing the preset cost function. To accomplish this, an integrated online Q-learning (IOQL) algorithm, driven by a prior policy and an exploration policy, is proposed for nonlinear discrete-time systems characterized by nonaffine features and unknown structures. First, a prior policy based on historical or artificial experience is designed to reduce the training time of the controller and provide a more stable learning environment. By introducing a weighting factor, the impact of the prior policy on the overall learning process can be adjusted. Second, an exploration policy is trained online through new experiences collected from the real environment. By leveraging two policies, we can swiftly and smoothly adjust the critic network for approximating the cost function and the action network for approximating the exploration policy, which can gradually enhance the control outcomes. Third, a stability condition with reasonable bounds is presented for the IOQL design. Finally, experimental and comparative results pertaining to a wastewater treatment plant, specifically evaluating learning speed and cost consumption, clearly demonstrate the significant advantages and superiority of the IOQL algorithm.

关键词： adaptive critic control adaptive dynamic programming nonlinear optimal control online Q-learning wastewater treatment applications

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dynamic event-based cooperative control of virtually coupled high-speed trains: an ADP-based optimal control scheme

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VEHICLE SYSTEM dynamicS 2025年第1期63卷 50-70页

作者： Wang, Xueli Wang, Tianzhen Ding, Derui Ge, Xiaohua Zhu, Bihong Shanghai Maritime Univ Logist Engn Coll Shanghai Peoples R China Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Australia Shanghai Invest Design & Res Inst Co Ltd Shanghai Peoples R China

This paper is concerned with the cooperative control issue of virtually coupled high-speed trains (VCHSTs) under an adaptive dynamic programming (ADP) framework, where a dynamic event-triggered mechanism is employed to govern the communication between sensors and observers. A learning-based observer, equipped with a robust term, is exploited to mitigate the effect of the unknown nonlinear dynamics of the VCHSTs. Furthermore, to realise the distributed design, an auxiliary control signal is employed to simply the closed-loop system and also integrated into the cost function to handle the coupled nature of the investigated systems. The Hamilton-Jacobi-Bellman (HJB) equation is approximated by actor and critic networks in the ADP framework, which iteratively acquires the desired control strategy. By resorting to the common Lyapunov stability, some sufficient conditions are derived to achieve the desired observer gain and learning rates of NNs. Ultimately, the control scheme's efficiency is confirmed by simulations of VCHSTs.

关键词： Neural networks optimisation control dynamic event-triggered mechanisms adaptive dynamic programming

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Finite-time optimal control for a class of nonlinear systems with performance constraints via critic-only ADP: Theory and experiments

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INFORMATION SCIENCES 2025年 690卷

作者： Zhang, Haichao Huang, Haowei Xiao, Bing Yin, Shen Li, Bo Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China CSG Guangdong Power Grid Corp Guangzhou 510620 Guangdong Peoples R China Norwegian Univ Sci & Technol Trondheim Norway Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 201306 Peoples R China

This paper addresses the optimal control problem within the framework of adaptive dynamic programming (ADP) for a class of nonlinear systems subjected to performance constraints. A new finite-time optimal control scheme is developed to stabilize the system by using the critic-only neural network ADP method. Compared with the existing ADP-based optimal control methods with uniformly ultimately bounded stability, the provided control scheme ensures that the controlled system's state and neural network weight estimation error are finite-time stable. It can ensure optimality, prescribed performance, and finite-time stability of the closed-loop control system simultaneously through an integration of ADP, the prescribed performance control technique, and Lyapunov theory. The designed adaptive neural network weight update law can relax the persisting excitation condition. The proposed control scheme is implemented on a robotic experiment platform to achieve trajectory tracking and verify its effectiveness.

关键词： adaptive dynamic programming Optimal control Finite-time stability Nonlinear systems Nonholonomic robot Prescribed performance

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Reinforcement Learning-Based 3D Trajectory Tracking Control of Hypersonic Gliding Vehicles With Time-Varying Uncertainties

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 8187-8199页

作者： Luo, Biao Sun, Jingyi Tang, Rui Xu, Xiaodong Cent South Univ Sch Automat Changsha 410083 Peoples R China

In this paper, a robust three-dimensional trajectory tracking control scheme based on reinforcement learning is proposed for the glide phase of a hypersonic gliding vehicle (HGV) with time-varying uncertainties. First, the non-affine nonlinear full-state kinematics and dynamics model of the HGV glide phase is constructed. Then, without linearizing the system, the desired multiplanar reference trajectories for HGVs are planned based on the pseudo-spectral theory under the input constraints, initial conditions, and terminal conditions. Subsequently, the full-state error system is generated by subtracting the reference system state from the actual state of the HGV system with time-varying uncertainty. For the full-state HGV error system with time-varying uncertainty and input constraints, we design a reinforcement learning-based optimal control scheme for its nominal system and establish the equivalence between this optimal control and the robust control of the original HGV error system. A single-evaluation network structure is used in the concrete implementation to reduce the computational cost. A rigorous theory is given to demonstrate the uniform ultimate boundedness of the closed-loop system and the weight error. Finally, we perform simulation traces for reference trajectories with different optimization performances to verify the effectiveness of the proposed method. Note to Practitioners-There are various constraints and uncertainties in the glide phase of HGVs, which is the hinge connecting the initial descent phase and the terminal management phase. How to design robust trajectory tracking controllers for the glide phase of HGVs with complex environments and large span of flight parameters is of great significance to aerial guidance practitioners. In this paper, an RL-based three-dimensional trajectory robust tracking guidance method is proposed for the HGV glide phase system, which can resist time-varying uncertainties and satisfy flight constraints. The unifo

关键词： adaptive dynamic programming hypersonic gliding vehicle tracking control standard trajectory adaptive dynamic programming hypersonic gliding vehicle tracking control standard trajectory

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A novel multi-level hierarchy optimization algorithm for pipeline inner detector speed control

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NEUROCOMPUTING 2025年 627卷

作者： Liu, Jinze Feng, Jian Zhang, Huaguang Yang, Shengxiang Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

This paper proposes a novel nature-inspired algorithm called Multi-Level Hierarchy Optimization (MLHO) for solving optimization problems over continuous space. The MLHO algorithm is inspired by the hierarchy of nature, especially the hierarchy of biological populations. The entire algorithm structure is divided into four levels for iterative optimization, and the work of each level is global direction guidance, optimization-seeking task allocation, local optimal exploration, and broad domain exploration. Differential variation strategy and dynamic inertia factor are also designed to solve the problem of decreasing population diversity and slow convergence speed at the late stage of evolution. In order to validate and analyze the performance of MLHO, numerical experiments were conducted on benchmark problems in each dimension of CEC'20. In addition, comparisons with 4 state-of-the-art (SOTA) algorithms are executed. The results show that the performance of MLHO is significantly superior to, or at least comparable to the SOTA algorithms. At the same time, the feasibility and effectiveness of MLHO are also demonstrated for the speed control problem of the pipeline inner detector.

关键词： Multi-Level Hierarchy Optimization adaptive dynamic programming Optimum control Metaheuristic algorithm Pipeline inner detector Speed control

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An event-triggered reliable cloud control scheme based on ADP and integral sliding mode

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NEUROCOMPUTING 2025年 636卷

作者： Huang, Xin Bi, Sicheng Han, Xinyu Xiao, Shuyi Su, Qingyu Northeast Elect Power Univ Sch Automat Engn Jilin 132012 Peoples R China Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Peoples R China

This paper investigates event-triggered (ET) reliable control problems for cloud control systems under actuator faults and data injection attacks via the adaptive dynamic programming (ADP) and integral sliding mode (ISM). A mist-fog-regional cloud control architecture is first given, which can improve computing efficiency of the cloud platform. In this architecture, a fog-based fault parameter estimation method is proposed with the aid of neural networks. It is driven by the feedback of fault parameter estimation errors, so as to achieve more accurate estimations of fault parameters. A double ET reliable cloud control scheme is further presented. is composed of an ISM-based and an ADP-based regional cloud controllers. As a result, it not only saves communication resources, but also eliminates the influence of the attacks and matched uncertainties, well as ensures the stability of the equivalent sliding-mode dynamics with optimal performance. Finally, the effectiveness of the proposed method is verified by the simulation results.

关键词： Cloud control systems Event-triggered reliable control Neural networks adaptive dynamic programming Integral sliding mode

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General multi-step value iteration for optimal learning control

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AUTOMATICA 2025年 175卷

作者： Wang, Ding Wang, Jiangyu Liu, Derong 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 Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Southern Univ Sci & Technol Sch Automat & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

Learning control methods have been widely enhanced by reinforcement learning, but it is challenging to analyze the effects of incorporating extra system information. This paper presents a novel multi-step framework that utilizes extra multi-step system information to solve optimal control problems. Within this framework, we establish and classify general multi-step value iteration (MsVI) algorithms based on the uniformity between policy evaluation and improvement stages. According to this uniformity concept, the convergence condition and the acceleration conclusion are analyzed for different kinds of MsVI algorithms. Besides, we introduce a swarm policy optimizer to relieve limitations of the traditional gradient optimizer. Specifically, we implement general MsVI using an actor-critic scheme, where the swarm optimizer and neural networks are employed for policy improvement and evaluation, respectively. Furthermore, the approximation error caused by the approximator is also considered to verify the advantage of using multi-step system information. Finally, we apply the proposed method to a nonlinear benchmark system, demonstrating superior learning ability and control performance compared to traditional methods. (c) 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： adaptive dynamic programming Approximation error Multi-step learning Neural networks Optimal control Particle swarm optimization Value iteration

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Neurocontrol for fixed-length trajectories in environments with soft barriers

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NEURAL NETWORKS 2025年 184卷 107034页

作者： Fairbank, Michael Prokhorov, Danil Barragan-Alcantar, David Samothrakis, Spyridon Li, Shuhui Univ Essex Sch Comp Sci & Elect Engn Colchester England Toyota Res Inst NA Ann Arbor MI USA Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35401 USA

In this paper we present three neurocontrol problems where the analytic policy gradient via back-propagation through time is used to train a simulated agent to maximise a polynomial reward function in a simulated environment. If the environment includes terminal barriers (e.g. solid walls) which terminate the episode whenever the agent touches them, then we show learning can get stuck in oscillating limit cycles, or local minima. Hence we propose to use fixed-length trajectories, and change these barriers into soft barriers, which the agent may pass through, while incurring a significant penalty cost. We demonstrate that the presence of soft barriers can have the drawback of causing exploding learning gradients. Furthermore, the strongest learning gradients often appear at inappropriate parts of the trajectory, where control of the system has already been lost. When combined with modern adaptive optimisers, this combination of exploding gradients and inappropriate learning often causes learning to grind to a halt. We propose ways to avoid these difficulties;either by careful gradient clipping, or by smoothly truncating the gradients of the soft barriers' polynomial cost functions. We argue that this enables the learning algorithm to avoid exploding gradients, and also to concentrate on the most important parts of the trajectory, as opposed to parts of the trajectory where control has already been irreversibly lost.

关键词： Neurocontrol adaptive dynamic programming Reinforcement learning Exploding gradients Soft barriers Back-propagation through time Analytic policy gradient

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Game-based fault-tolerant formation containment control for fixed-wing UAVs under the fully actuated system framework

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AEROSPACE SCIENCE AND TECHNOLOGY 2025年 160卷

作者： Hu, Jiao Xu, Yuhang Jiang, Bin Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China

This paper addresses a fault-tolerant formation tracking-containment control problem for fixed-wing unmanned aerial vehicles (UAVs), where leader UAVs follow a target in a desired formation, and faulty follower UAVs converge into the convex hull spanned by leader UAVs. To solve this problem, a differential game-based approach is proposed, leading to the distributed formation control of leader UAVs and fault-tolerant containment control of follower UAVs simultaneously. Moreover, the adaptive dynamic programming (ADP) technology is incorporated to derive the solutions of the Hamilton-Jacobi-Isaacs (HJI) equations and further approximate the game strategies. It is rigorously proved that the states of UAVs and weight estimation errors of neural networks (NNs) are asymptotically stable rather than uniform ultimate bounded (UUB) under the proposed adaptive weight updating laws. Finally, a comparative simulation is conducted on a fixed-wing UAV system to verify the effectiveness of the proposed game-based method.

关键词： Fault-tolerant formation tracking-containment control Fixed-wing unmanned aerial vehicle Differential games adaptive dynamic programming

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