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Distributed optimal control for continuous-time nonaffine nonlinear interconnected systems

INTERNATIONAL JOURNAL OF CONTROL 2022年第12期95卷 3462-3476页

作者： Farzanegan, Behzad Suratgar, Amir Abolfazl Menhaj, Mohammad Bagher Zamani, Mohsen Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran Univ Newcastle Sch Elect Engn & Comp Sci Newcastle NSW Australia

In this paper, a new distributed optimal control strategy for continuous-time nonaffine nonlinear systems with unknown dynamics is presented. We exploit a paradigm based on an Extended adaptive dynamic programming (EADP), which will be fully stated in the paper, to provide a distributed optimal control strategy in the presence of subsystem interactions. A proper design of optimal control law is further fully investigated through the framework of Hamilton-Jacobi-Bellman (HJB). The proposed EADP algorithm can now be employed in order to solve iteratively the HJB equation associated with each subsystem. The proposed distributed optimal control system mainly consisting of three weighted basis functions for each subsystem is fully developed to handle the issue associated with the availability of unknown dynamics. The convergence analysis of the proposed algorithms is also established. Finally, two numerical examples are given to confirm the effectiveness of the proposed control scheme.

关键词： adaptive dynamic programming nonlinear interconnected systems distributed control systems data-driven optimal control reinforcement learning

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Neurodynamic programming and Tracking Control for Nonlinear Stochastic Systems by PI Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第6期69卷 2892-2896页

作者： Ming, Zhongyang Zhang, Huaguang Li, Weihua Luo, Yanhong Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Liaoning Peoples R China

In this paper, for nonlinear stochastic systems, the optimal tracking control problem (OTCP) is solved by adaptive dynamic programming (ADP) techniques. Firstly, the complex OTCP is transformed into a stable control optimization problem by reconstructing a new stochastic augmented system. Then, simplifying the actor-critic architecture and reducing the computational load, critic neural networks (NNs) is used in iterative learning. And by using Lyapunov method, the ultimate uniform boundedness (UUB) of the tracking system is proved. To be precise, no literature has been published on the OTCP for nonlinear Ito type stochastic systems via the ADP method. This work is the first attempt in this field. Finally, in simulation, the method is applied to sinusoidal waveform and periodic rectangular step signal, and even unbounded exponential waveform.

关键词： Stochastic systems adaptive systems Optimal control Trajectory Stability analysis Nonlinear dynamical systems Indium tin oxide Stochastic systems adaptive dynamic programming optimal tracking control problem neural network

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Error-based adaptive optimal tracking control of nonlinear discrete-time systems

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Science China(Information Sciences) 2024年第1期67卷 154-167页

作者： Chun LI Jinliang DING Frank L.LEWIS Tianyou CHAI State Key Laboratory of Synthetical Automation for Process Industries Northeastern University UTA Research Institute The University of Texas at Arlington

In this paper,for the output tracking problem of nonlinear discrete-time systems,a performance index is newly defined using the adaptive dynamic programming(ADP) technique to completely eliminate tracking errors in *** contrast to traditional definitions of performance indices in other ADP-based methods,the proposed performance index is not only designed from the perspective of output tracking errors but also introduced errors of system states and control inputs at adjacent stages,which is suitable for practical situations in many industrial applications,such as the alumina production,the flotation process,and the mineral grinding *** proved that the obtained controller can make the system output fully track the given reference trajectory by applying the iterative criteria of the ADP *** addition,the proposed algorithm was implemented using a data-driven technique and neural networks to avoid analyzing and deducing the complicated dynamics of actual industrial ***,using historical data for a forced-circulation evaporation system in the alumina production process,the effect of the proposed approach was verified through a numerical simulation and compared with that of the proportional-integral controller.

关键词： adaptive dynamic programming iterative criteria performance index tracking errors tracking problem

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Finite-Horizon Robust Optimal Trajectory Tracking Control for QUAV with Input Constraints

Finite-Horizon Robust Optimal Trajectory Tracking Control fo...

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2024 International Conference on Guidance, Navigation and Control

作者： Xu, Jicai Bu, Chunlei Xia, Rongsheng Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Anhui Peoples R China

ISBN: (纸本)9789819622306;9789819622283;9789819622276

This paper studies an adaptive dynamic programming (ADP) based finite-horizon robust optimal trajectory tracking control problem for quadrotor unmanned aerial vehicle (QUAV) with external disturbance, input saturation and input delay. Firstly, the disturbance observer technique is used to address the external disturbance. Subsequently, the auxiliary system method and Pade approximation technique are used to handle the input saturation and input delay problems, respectively. Then, an ADP algorithm based finite-horizon robust optimal trajectory tracking controller is designed to meet some suitable performance requirements in a finite time. Finally, the Lyapunov method is applied for stability analysis of the whole closed-loop system, simulation tests is carried out for the QUAV to verify the reliability of the proposed robust optimal control policy.

关键词： Quadrotor unmanned aerial vehicle Finite-horizon optimal control adaptive dynamic programming Input saturation Input delay

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Degradation Tolerant Control Learning for Discrete-Time Affine Nonlinear Systems 22

Degradation Tolerant Control Learning for Discrete-Time Affi...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Kanso, Soha Jha, Mayank Shekhar Theilliol, Didier Univ Lorraine CRAN UMR 7039 CNRS F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9781713872344

This paper develops a degradation tolerant optimal control in the framework of Reinforcement Learning (RL). Safety-critical and mission-critical systems require the development of new control designs that maintain system stability and performance specifications but also address incipient degradation. The aim of this work is to decelerate the speed of degradation by minimizing a cost function that includes the rate of evolution of degradation and the performance requirements. The controller is developed for discrete-time nonlinear systems affine in control, where the system's states are affected by a nonlinear degradation. Value iteration (VI) algorithm based approach is developed to find suitable approximations of both optimal control policy and optimal cost, while guaranteeing closed-loop stability and minimization of degradation rate. Offline model-based adaptive dynamic programming (ADP) algorithm is developed and implemented using actor-critic structure which involves training of both actor and critic neural networks (NN). After training the actor NN with the optimal policy, the NN is implemented in real time to generate the input of the system. Simulation example shows the efficiency and feasibility of the algorithm.

关键词： adaptive dynamic programming Reinforcement Learning Actor-Critic Structure Optimal Control Fault Tolerant Control Safety-Critical Systems Affine Nonlinear Systems

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Direct adaptive Control for Stochastic Systems with Risk-Sensitive Indices * 22

Direct Adaptive Control for Stochastic Systems with Risk-Sen...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Qiao, Nan Li, Tao East China Normal Univ Shanghai Key Lab Pure Math & Math Practice Sch Math Sci 5005 South Lianhua Rd Shanghai 200241 Peoples R China NYU Shanghai NYU ECNU Inst Math Sci 3663 Zhongshan Rd North Shanghai 200062 Peoples R China

ISBN: (纸本)9781713872344

We propose a 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 indices. A control design methodology is employed to iteratively solve the generalized algebraic Riccati equation by using the online information of the state and input, and to directly learn the optimal control law. We prove the convergence of the online ADP algorithm and show that the direct adaptive control law approximates the optimal control law as time goes on. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our algorithm. Copyright (c) 2023 The Authors.

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

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Neural network-based event-triggered integral reinforcement learning for constrained H1 tracking control with experience replay

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NEUROCOMPUTING 2022年 513卷 25-35页

作者： Xue, Shan Luo, Biao Liu, Derong Gao, Ying South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

Since input constraints and external disturbances are unavoidable in tracking control problems, how to obtain a controller in this case to save communication and data resources at the same time is very chal-lenging. Aiming at these challenges, this paper develops a novel neural network (NN)-based event -triggered integral reinforcement learning (IRL) algorithm for constrained H1 tracking control problems. First, the constrained H1 tracking control problem is transformed into a regulation problem. Second, an event-triggered optimal controller is designed to reduce network transmission burden and improve resource utilization, where a novel threshold is proposed and its non-negativity can be guaranteed. Third, for implementation purpose, a novel NN-based event-triggered IRL algorithm is developed. In order to improve data utilization, the experience replay technique with an easy-to-verify condition is employed in the learning process. Theoretical analysis proves that the tracking error and weight estima-tion error are uniformly ultimately bounded. Finally, simulation verification shows the effectiveness of the present method. (c) 2022 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Neural networks Integral reinforcement learning H 1 tracking control Event -triggered mechanism

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Compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators: theory and experimental verification

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COMPLEX & INTELLIGENT SYSTEMS 2022年第3期8卷 1913-1927页

作者： Ma, Bing Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun 130021 Peoples R China

This paper presents a novel compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators (MRMs). On the basis of subsystem dynamics under joint torque feedback (JTF) technique, the proposed tracking error fusion function, which includes position error and velocity error, is utilized to construct performance index function. By analyzing the dynamic uncertainties, a local dynamic information-based robust controller is designed to engage the model uncertainty compensation. Based on adaptive dynamic programming (ADP) algorithm and the event-triggered mechanism, the decentralized tracking control is obtained by solving the event-triggered Hamilton-Jacobi-Bellman equation (HJBE) with the critic neural network (NN). The tracking error of the closed-loop manipulators system is proved to be ultimately uniformly bounded (UUB) using the Lyapunov stability theorem. Finally, experimental results illustrate the effectiveness of the developed control method.

关键词： Modular robot manipulators adaptive dynamic programming Event-triggered mechanism Optimal control Neural networks

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Data-Based Approximate Optimal Control for Unknown Nonaffine Systems via dynamic Feedback 12

Data-Based Approximate Optimal Control for Unknown Nonaffine...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Lin, Jinquan Zhao, Bo Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350321050

In this paper, an integral reinforcement learning (IRL)-based approximate optimal control (AOC) method for unknown nonaffine systems is developed by using dynamic feedback. For optimal control problems of nonaffine systems, optimal control policy cannot be expressed explicitly since the input gain matrix is unknown. Therefore, the nonaffine system is transformed into an augmented affine system by introducing a dynamic feedback signal as the virtual control input. Moreover, by designing an appropriate value function for the augmented affine system, the optimal control of augmented affine system is formulated as the AOC for unknown nonaffine systems. Moreover, the IRL method is adopted to derive the approximate solution of Hamilton-Jacobi-Bellman equation via the critic-only structure. Theoretical analysis concludes that the closed-loop system is uniformly ultimately bounded by using the developed IRL-based AOC scheme. An example is utilized to demonstrate the effectiveness of the present approach.

关键词： adaptive dynamic programming reinforcement learning dynamic feedback data-based control unknown non-affine systems

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Event-Triggered Near Optimal Output Feedback Control for Constrained Discrete-Time Systems Via an Iterative adaptive Algorithm

Event-Triggered Near Optimal Output Feedback Control for Con...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Hou, Jiaxu Zhu, Liao Guo, Ping School of Systems Science Beijing Normal University Beijing100875 China International Academic Center of Complex Systems Beijing Normal University at Zhuhai Zhuhai519087 China

ISBN: (纸本)9798350337020

For event-triggered optimal control problems of general nonlinear systems, it is very difficult to obtain the optimal analytical solution. In this paper, an adaptive near optimal output feedback control method is presented for discrete-time (DT) nonlinear systems. The event-triggered mechanism is introduced to significantly reduce the execution costs through aperiodic control updating intervals without affecting system responses. Furthermore, an integral term is presented in the performance index function to achieve the optimal constrained control. Consequently, iterative dual heuristic dynamic programming algorithm (DHP) is adopted to learn the optimal control law and the costate function. Finally, two examples are provided to illustrate the effectiveness of the proposed approach. © 2023 IEEE.

关键词： adaptive dynamic programming Discrete-time nonlinear system Event-triggered control Near optimal control Neural network

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