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Boundary adaptive iterative learning control for flexible satellite system with input constraint

NONLINEAR DYNAMICS 2025年第7期113卷 6907-6921页

作者： Zhao, Zhibo Yuan, Yuan Luo, Biao Xu, Xiaodong Huang, Tingwen Gui, Weihua Cent South Univ Sch Automat Changsha Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha Peoples R China Texas A&M Univ Qatar Dept Sci Doha Qatar

This paper is concerned with the constrained vibration control problem of a flexible satellite system in the presence of unknown external disturbances. Therein, the dynamic model of satellite system is governed as a high order partial differential equation. Unknown disturbances directly result that existing approaches fail in solving the precise control problem for the considered system. To this end, this paper proposes a boundary adaptive iterative learning controller. During the design of the control law, in response to the input constraint which is described by a saturation function model, a hyperbolic tangent function is employed. Novel adaptive learning laws are proposed to learn the upper bounds of disturbances exerted at the solar panels and the centerbody. The closed-loop stability is theoretically proved through a Lyapunov-Krasovskii composite energy function. Numerical simulations are provided to represent the effectiveness of the proposed control method.

关键词： Adaptive control Iterative learning control Flexible satellite input constraint

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Event-triggered consensus tracking control of flexible manipulators with nonlinear time-varying fault-tolerant actuators and input constraint

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2024年第13期55卷 2727-2740页

作者： Zhang, Qianyi Zhang, Qingzhen Liu, Jinkun Beihang Univ Sch Automat Sci & Elect Engn Beijing 100000 Peoples R China

This paper introduces a novel event-triggered consensus control strategy for a multi-agent system composed of underactuated flexible manipulators. Most existing research on consensus tracking control assumes that the model targeted is fully actuated, the control signal is continuous, and that both time-varying actuator failures and input constraints are managed separately. These challenges were tackled in this study within the context of multi-agent systems, addressing input constraints and nonlinear time-varying actuator failures simultaneously. Moreover, an event-triggered mechanism is proposed to reduce signal communication. A double closed-loop consensus tracking method is designed based on the multi-agent topology, enabling all flexible manipulators to track the angle and angular velocity of the leader and an adaptive control law is designed to solve actuator faults. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.

关键词： Flexible manipulator consensus tracking nonlinear actuator failures input constraint event-triggered mechanism

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Integral reinforcement learning-based optimal tracking control for uncertain nonlinear systems under input constraint and specified performance constraints

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第13期34卷 8802-8824页

作者： Chang, Ru Liu, Zhi-Meng Li, Xiao-Bin Sun, Chang-Yin Shanxi Univ Sch Automat & Software Engn Taiyuan Peoples R China Shanxi Univ Sch Math Sci Taiyuan Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China

This article addresses the optimal tracking control problem with prescribed performance for uncertain nonlinear systems subject to input constraint and unknown disturbances. First, a fixed-time monotonic convergence function is introduced to restrain tracking error, and a nonlinear mapping technique is employed to transform the constrained error into an unconstrained variable, then the fixed-time output tracking issue is boiled down to the boundedness problem of the transformed variable. With the aid of a nonquadratic cost function, the input constraint is encoded into the optimization problem. To solve the unknown disturbances, an auxiliary system and an auxiliary disturbance policy are constructed, and the optimal control problem is formulated as a two-player zero-sum game. Moreover, a Hamilton-Jacobi-Isaacs (HJI) equation associated with this nonquadratic zero-sum game is established to give the optimal control and the worst-case disturbance policy solution. Subsequently, to avoid using knowledge of the system dynamics, three neural network approximators, namely, actor, critic, and disturbance, which are tuned online and simultaneously for approximating the solution of HJI, are constructed based on the integral reinforcement learning algorithm. Theoretical analysis shows that the reconstructed error system states and the weight estimation errors are semi-globally uniformly ultimately bounded. Finally, the simulation study further tests the availability of the proposed control strategy.

关键词： input constraint optimal tracking control prescribed performance control reinforcement learning uncertain nonlinear systems zero-sum game

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A Class of Optimal Control Problems of Forward-Backward Systems with input constraint

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2023年第3期199卷 1050-1084页

作者： Huang, Jianhui Li, Wenqiang Zhao, Hanyu Hong Kong Polytech Univ Dept Appl Math Kowloon Hong Kong Peoples R China Yantai Univ Sch Math & Informat Sci Yantai 264005 Peoples R China

In this paper, we consider a new class of optimal control problems with admissibility constraint, where the state is driven by a fully coupled forward backward stochastic differential equation (FBSDE) with mixed initial-terminal condition. Different from the classical control problems, both dynamic process control and static initial-terminal perturbations are considered. Moreover, all control/perturbation components are subject to input constraint in terms of closed convex sets and partial information in terms of some sub-filtration for randomness evolution. We first study the nonlinear case of aforementioned FBSDE optimal control by deriving stochastic maximum principle. Next, we consider the linear quadratic case with explicit representation of the optimal admissible controls. More specifically, a new Hamiltonian system involving three projection operators and conditional expectation is derived. Finally, we apply obtained maximum principle to study a general class of large-population system and provide a unified framework to analyze related mean-field game (MFG). Our result includes considerable existing MFG results as its special cases and provides some new features such as recursive functional or input delay average.

关键词： Mixed forward backward stochastic differential equation Initial-terminal perturbation input constraint Stochastic maximum principle Large-population problem Mean-field game

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Integrated guidance and control of hypersonic flight vehicle with coordinated mission requirement and input constraint

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第7期33卷 4262-4280页

作者： Shou, Yingxin Yan, Tian Xu, Bin Sun, Fuchun Northwestern Polytech Univ Sch Automat Xian 710072 Shannxi Peoples R China Northwestern Polytech Univ Unmanned Syst Res Inst Xian Shannxi Peoples R China Tsinghua Univ State Key Lab Intelligent Technol & Syst Comp Sci & Technol Beijing Peoples R China

An integrated adaptive guidance and control algorithm is investigated for the hypersonic flight vehicle (HFV) under the objection of coordinated for mission requirement and control capability. The design pressure to avoid input saturation is placed in the guidance loop by analyzing the hard restriction on the fuel-air equivalence ratio (FER) and the acceleration mission objective. Using the concept of model self-shaping, a loss function is established to achieve the self-optimization of the reference model bandwidth. The reference trajectory of the velocity slows down as the FER approaches its maximum value and becomes faster as the FER demand decreases. The backstepping frame is applied in the attitude control system, and the dynamics uncertainty with the unknown aerodynamic parameters is constructed using neural network. The Karush-Kuhn-Tucker multiplier is used as a link parameter of the guidance and control integrated framework and is introduced into the adaptive update law. An adaptive algorithm is proposed with the estimating evaluation derived from the serial-parallel estimation model. A HFV simulation test with FER constraint shows that the adaptive integrated guidance and control can follow the desired trajectory with high tracking accuracy, and the control input can be kept within a feasible value.

关键词： integrated guidance and control hypersonic flight vehicle input constraint Karush-Kuhn-Tucker condition neural network

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Internal-Model-Principle-Based Fast Adaptive Iterative Learning Trajectory Tracking Control for Autonomous Farming Vehicle Under Alignment Condition and input constraint

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第6期53卷 3588-3599页

作者： Zhang, Ting Jiao, Xiaohong Zhang, Yahui Yanshan Univ Minist Educ Intelligent Control Syst & Intelligent Engn Res Ctr Qinhuangdao Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao Peoples R China Yanshan Univ Sch Mech Engn Qinhuangdao 066004 Peoples R China

A novel adaptive iterative learning control (NAILC) strategy is proposed to enhance static and dynamic control performances for the autonomous farming vehicle tracking repetitive trajectories of alternating parallel straight and large curvature. The method integrates an exponential decay function, an internal model principle (IMP), and an input constraint auxiliary variable design system in an adaptive iterative learning control framework. The introduction of exponential decay improves the convergence rate of the iterative process and ensures the control system's dynamic performance. The IMP enhances the static performance of the trajectory tracking control system and guarantees the alignment condition setting. The auxiliary variable design system reduces the adverse effects of input constraints on the control system performance during the iterative process. Moreover, the adaptive iterative learning updating law estimates the total time-varying disturbance to improve the robustness of the tracking process against the actual disturbance. Simulation comparisons with existing results verify the effectiveness and advantages of the designed NAILC strategy under the MATLAB/Simulink environment.

关键词： Farming Process control Trajectory Trajectory tracking Vehicle dynamics Control systems Convergence Adaptive iterative learning control (AILC) alignment condition autonomous farming vehicle exponential decay function input constraint internal model principle (IMP)

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Adaptive Neural Networks Command Filtered Control for MIMO Nonlinear Discrete-Time Systems With input constraint

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第2期70卷 581-585页

作者： Xu, Yumeng Liu, Jiapeng Yu, Jinpeng Wang, Qing-Guo Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai 519087 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multimodal Data Proc Zhuhai 519087 Peoples R China

An adaptive neural network command filtered tracking control method is investigated for multiple-input multiple-output (MIMO) discrete-time nonlinear systems with input constraint. Firstly, the noncausal problem in the back-stepping is eliminated by using the command filtered control (CFC) technology. Secondly, the error compensation mechanism is brought to solve the filtering error generated by the first-order filter. Thirdly, radial basis function neural networks (RBF NNs) are exploited to deal with unknown nonlinear functions in MIMO discrete-time systems. The effectiveness of the proposed method is tested through a simulation example.

关键词： MIMO nonlinear systems RBF NNs command filtered backstepping input constraint discrete-time

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Command-Filtered Neuroadaptive Output-Feedback Control for Stochastic Nonlinear Systems With input constraint

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第4期53卷 2301-2310页

作者： Yu, Jinpeng Cheng, Shuai Shi, Peng Lin, Chong Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia

In this article, an adaptive neural-network (NN) command-filtered output-feedback control strategy is proposed for a class of stochastic nonlinear systems (SNSs) with the actuator constraint. The problem of ``explosion of complexity'' existing in the conventional backstepping design procedure for SNSs is successfully resolved based on the command filter technique, and the error compensation mechanism is introduced to remove effectively the influence of filtered error. By using the NNs to identify the unknown nonlinear functions, a neural-network-based state observer is designed to estimate the unmeasurable states of the SNSs. Based on the quartic Lyapunov function, the stability of stochastic closed-loop systems is analyzed. It is proved that all signals of the closed-loop systems are bounded in probability, and the tracking error approaches a small neighborhood of the origin in probability. Finally, the effectiveness of the developed control algorithm in this article is verified by a comparison example.

关键词： Nonlinear systems Backstepping Actuators Stochastic processes Observers Artificial neural networks Explosions Adaptive neural-network (NN) control command filter input constraint neural state observer stochastic nonlinear systems (SNSs)

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Adaptive fuzzy command filtered discrete control for cascade continuous stirred tank reactors with input constraint 43

Adaptive fuzzy command filtered discrete control for cascade...

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43rd Chinese Control Conference (CCC)

作者： Xin, Li-Ping Shan, Wen-Yue Niu, Xian-Duo Liu, Jia-Shuo Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266525 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

A novel fuzzy adaptive command-filtered discrete controller is designed for cascaded continuous stirred tank reactors (CCSTRs) with input constraints. The mathematical model of CCSTRs system is discretized using the Euler method. The fuzzy logic system is used to approximate the complex nonlinear function in the CCSTRs system. The problems of "complexity of computation" and noncausal problems that result from the traditional backstepping method are avoided based on the command-filtered technique. Compared with the existing control methods for CCSTRs, the control strategy has better stability and realizability, the controller structure is simpler, and the on-line computation burden and the maximum value of control input are all strongly reduced.

关键词： Cascaded continuous stirred tank reactors (CCSTRs) discrete control command filtered control input constraint adaptive fuzzy control

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Social optima in mean field linear-quadratic-Gaussian models with control input constraint

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SYSTEMS & CONTROL LETTERS 2022年 162卷 105174-105174页

作者： Du, Kai Wu, Zhen Shandong Univ Sch Math Jinan 250100 Peoples R China

This paper studies linear quadratic social optimal problems with control input constraints, where both dynamics and costs involve the coupled term of states, and the diffusion terms contain control variables. In virtue of the variational method, we obtain the corresponding auxiliary stochastic control problem by introducing the limiting state-average process. Furthermore, we design a set of decentralized control strategies by using the stochastic maximum principle, which is given by a fully coupled non-linear mean-field forward-backward stochastic differential equation, and they are further shown to be asymptotically social optimality by perturbation analysis. As applications, we apply the theoretical results to deal with the optimal consumption problem and obtain the related optimal consumption strategy.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Mean field model Social optimum input constraint Forward-backward stochastic differential equation

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