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optimal tracking control for Autonomous Vehicle With Prescribed Performance via Adaptive Dynamic Programming

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第9期25卷 12437-12449页

作者： Hu, Chuan Wang, Ziao Bu, Xiangwei Zhao, Jun Na, Jing Gao, Hongbo Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China Shandong Univ Sci & Technol Coll Transportat Qingdao 266590 Peoples R China Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650093 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Dept Automat Hefei 230026 Peoples R China

The path tracking control problem for autonomous vehicle with uncertain dynamics requires simultaneous consideration of control optimality and safety-based performance constraints. In this paper, an adaptive optimal control method with prescribed performance is proposed to solve this problem, which contains two contributions: 1) by introducing a prescribed performance function (PPF) into adaptive dynamic programming (ADP), the controller can constrain the tracking error of the system within a specified performance boundary while optimizing the control cost;2) the critic-only ADP is used for the controller design, which simplifies the commonly used actor-critic ADP scheme, and the convergence of the estimation error is guaranteed under FE conditions. On this basis, the neural network identification technique is introduced to deal with the unknown dynamic parameters of the vehicle system. The control scheme is able to strictly guarantee user-defined vehicle performance specifications with approximately optimal control performance. The stability of the closed-loop system is rigorously demonstrated by the Lyapunov method. In addition, the controller also embeds a radial basis function neural network (RBFNN) compensator to approximate the nonlinear external disturbances of the autonomous vehicle. Finally, the efficiency of the controller to achieve autonomous vehicle path tracking is verified by CarSim-Simulink simulation.

关键词： Autonomous vehicles Vehicle dynamics Dynamic programming Adaptive systems Neural networks Safety optimal control Adaptive dynamic programming autonomous vehicles optimal tracking control system identification prescribed performance control

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optimal tracking control of Blast Furnace Molten Iron Quality Based on Krotov's Method and Nonlinear Subspace Identification

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第8期71卷 9610-9619页

作者： Liu, Yue Zhou, Ping Sun, Xiaoyang Chai, Tianyou Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

In order to address the high-performance control problem of multivariate molten iron quality (MIQ) in the blast furnace (BF) ironmaking process, specifically molten iron silicon content ([Si]) and molten iron temperature, this article proposes a novel optimal tracking control algorithm based on Krotov's method and nonlinear subspace identification. To begin with, a nonlinear Hammerstein model for MIQ is established to represent the relationship between the key process variables and MIQ indexes using nonlinear subspace identification technology. Then, to reduce the computational burden, an augmented system is established such that the optimal tracking control problem of MIQ is transformed into an optimal regulation problem. Furthermore, the candidate optimal controller for the augmented system is derived by utilizing the iterative optimization characteristic of Krotov's method and designing an improving function. Finally, the numerical solution of the optimal controller is obtained by solving nonlinear equations through the quasi-Newton method, resulting in the optimal output trajectory of MIQ. Theoretical analysis and experiments based on actual BF production data verify the effectiveness and advancement of the proposed method.

关键词： Iron Process control optimal control Trajectory Silicon Optimization Computational modeling Blast furnace (BF) hammerstein subspace identification Krotov's method optimal tracking control

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optimal tracking control of Second-Order Multiagent Systems With Input Delay via Data-Driven Forward Reward Q -Learning Framework

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 1858-1869页

作者： Rao, Kai Yan, Huaicheng Liu, Qiwei Dang, Qingmei Shi, Kaibo East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Weifang Univ Coll Machinery & Automat Weifang 261061 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Chengdu Univ Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China

In this article, an optimal tracking control algorithm is derived for second-order discrete-time multiagent systems (MASs) with unknown system dynamics and input delay. First, the optimal tracking problem of MASs with input delay is constructed by the tracking error and a local performance index function. By designing a new variable, the original model is converted into a model without delay while guaranteeing the equivalence of performance index and control law of each agent. Subsequently, the transformed model and reinforcement learning (RL) theory are integrated to obtain a novel data-driven distributed learning framework. This framework enables online learning of the optimal control law and ensures tracking consensus of all followers' position and velocity states. Compared to the traditional actor-critic framework, an additional neural network (NN) is utilized to approximate the forward reward information (FRI) to improve the information learning capability of the MASs. Furthermore, the convergence analysis of system states and three NNs structures are conducted by Lyapunov theory. Finally, the proposed framework is verified to have better convergence and require fewer iteration steps than classical actor-critic framework by numerical simulation comparison experiments.

关键词： Delays Heuristic algorithms Performance analysis Convergence Q-learning optimal control Artificial neural networks Approximation algorithms Delay effects System dynamics Data-driven input delays optimal tracking control reinforcement learning (RL) second-order multiagent systems (MASs)

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optimal tracking control of an Autonomous Underwater Vehicle: A PMP approach

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ISA TRANSACTIONS 2024年 145卷 298-314页

作者： Anil, B. Gajbhiye, Sneha Indian Inst Technol Palakkad Dept Elect Engn Kanjikkode 678623 Kerala India

This paper addresses the optimal tracking control problem of an Autonomous Underwater Vehicle (AUV) using Pontryagin's Minimum Principle (PMP). The formulation uses a choice of Hamiltonian function using the desired control objective with AUV dynamics acting as dynamic constraints. We first develop PMP based on AUV kinematics and then extend it to the dynamics to arrive at optimal thrusts and moments. Necessary conditions for optimality are derived for both the models using PMP, which results in optimal trajectories that simultaneously minimize the tracking error as well as the control cost, thereby arriving at energy optimality. It was observed that the adjoint variables (costates) are indeed the momenta in the inertial and body -fixed frames. At the kinematic level, this forms a stable solution. The developed methodology is applied to both 2D and 3D AUV model with disturbances due to inputs and ocean currents. Numerical simulations are carried out with the derived control laws for a given trajectory tracking target. Quantitative evaluation of the performance and comparison of the controller is done using Mean Square Error(MSE) and Total Variation(TV) measures. The proposed control laws are found to achieve the desired control objectives.

关键词： Autonomous Underwater Vehicle optimal tracking control Pontryagin's Minimum Principle

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optimal tracking control of mechatronic servo system using integral reinforcement learning

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INTERNATIONAL JOURNAL OF control 2023年第12期96卷 3072-3082页

作者： Chen, Wei Hu, Jian Xu, Chenchen Zhou, Haibo Yao, Jianyong Nie, Weirong Nanjing Univ Sci & Technol Sch Mech Engn Nanjing Peoples R China Cent South Univ State Key Lab High Performance Complex Mfg Changsha Peoples R China

Recent years, there has been an increasing interest in the application of reinforcement learning (RL) to nonlinear system control. And many published studies have shown that reusing historical data can improve the learning efficiency of intelligent agents. However, reusing historical data leads to a large increase in the data to be processed. This paper proposes a novel adaptive law for the critic network in the RL framework to address this problem. The innovation of the proposed method is the introduction of an auxiliary matrix containing information about the historical estimation errors instead of the historical data itself. Moreover, the addition of the auxiliary matrix allows the persistent excitation (PE) conditions to be checked online. As a validation, the proposed approach is applied to a motor servo system containing unknown dynamics. Simulation and experimental results demonstrate the effectiveness of the proposed controller in the end.

关键词： Integral reinforcement learning mechatronic servo system optimal tracking control adaptive optimal control

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optimal tracking control for blast furnace molten iron quality based on subspace identification and Krotov's method

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optimal control APPLICATIONS & METHODS 2023年第5期44卷 2532-2550页

作者： Liu, Yue Zhou, Ping Northeastern Univ State Key Lab Synthet Automat Proc Ind 11 Lane 3 Wenhua Rd Shenyang Liaoning Peoples R China

Blast furnace ironmaking is a complex nonlinear dynamic process, which has strict requirements on the quality of final molten iron. To improve the control effect of molten iron quality, this paper proposes an optimal tracking control method for molten iron quality based on the bilinear subspace identification and Krotov's method. First, a state space model between key process variables and blast furnace molten iron quality, using the bilinear subspace identification method, is established based on actual industrial data. Then, an augmented system is established combining the state vector and the reference trajectory, and the improving feedback controller is designed by solving a set of Riccati equations, which are established based on Krotov's method. In combination with the designed controller and the established system, the specific implementation steps are proposed. Finally, the effectiveness and advancement of the proposed method are verified based on actual industrial data of a large blast furnace.

关键词： bilinear subspace identification data-driven control Krotov's method molten iron quality optimal tracking control

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optimal tracking control of hydraulic support initial support force based on adaptive dynamic programming 43

Optimal tracking control of hydraulic support initial suppor...

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43rd Chinese control Conference (CCC)

作者： Zeng, Qingliang Jia, Bingyi Zhao, Jun Wan, Lirong Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Transportat Qingdao 266590 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Due to the complex coal mining environment and dynamic pressure characteristics in hydraulic supply systems, setting and controlling the initial support force of hydraulic supports leads to the significant challenges. This paper proposes an optimal tracking control method for the initial support force of hydraulic supports base on adaptive dynamic programming (ADP). First, a mathematical model for the fluid supply system is developed, and an augmented system is constructed to transform the tracking problem into a regulation control problem. Accordingly, the optimal control cost function is estimated by applying a neural network (NN), where a discount factor is introduced to ensure boundedness. Finally, an online adaptive learning method based on adaptive parameter estimation technique is designed to online update the unknown NN weights, thus the Hamilton-Jacobi-Bellman (HJB) equation can be online resolved. Simulation results demonstrate high control accuracy, with the tracking error converging to zero, enabling adaptive adjustment of the initial support force in complex coal mine working environments.

关键词： Initial support force Fluid supply system Adaptive dynamic programming optimal tracking control

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optimal tracking control for Uncertain Nonlinear Systems With Prescribed Performance via Critic-Only ADP

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第1期52卷 561-573页

作者： Dong, Hongyang Zhao, Xiaowei Luo, Biao Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England Cent South Univ Sch Automat Changsha 410083 Peoples R China

This article addresses the tracking control problem for a class of nonlinear systems described by Euler-Lagrange equations with uncertain system parameters. The proposed control scheme is capable of guaranteeing prescribed performance from two aspects: 1) a special parameter estimator with prescribed-performance properties is embedded in the control scheme. The estimator not only ensures the exponential convergence of the estimation errors under relaxed excitation conditions but also can restrict all estimates to predetermined bounds during the whole estimation process and 2) the proposed controller can strictly guarantee the user-defined performance specifications on tracking errors, including convergence rate, maximum overshoot, and residual set. More importantly, it has the optimizing ability for the tradeoff between performance and control cost. A state transformation method is employed to transform the constrained optimal tracking control problem to an unconstrained stationary optimal problem. Then, a critic-only adaptive dynamic programming algorithm is designed to approximate the solution of the Hamilton-Jacobi-Bellman equation and the corresponding optimal control policy. Uniformly ultimately bounded stability is guaranteed via a Lyapunov-based stability analysis. Finally, numerical simulation results demonstrate the effectiveness of the proposed control scheme.

关键词： Nonlinear systems Convergence Iron Stability analysis Indexes Mathematical model Uncertain systems Adaptive dynamic programming (ADP) optimal tracking control parameter estimation prescribed performance reinforcement learning (RL)

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optimal tracking control of Nonlinear Multiagent Systems Using Internal Reinforce Q-Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第8期33卷 4043-4055页

作者： Peng, Zhinan Luo, Rui Hu, Jiangping Shi, Kaibo Nguang, Sing Kiong Ghosh, Bijoy Kumar Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Chengdu Univ Sch Informat Sci & Engn Chengdu 610106 Peoples R China Univ Auckland Dept Elect Comp & Software Engn Auckland 1142 New Zealand Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal tracking control problem of unknown nonlinear multiagent systems (MASs). Different from the representative RL-based optimal control algorithms, an internal reinforce Q-learning (IrQ-L) method is proposed, in which an internal reinforce reward (IRR) function is introduced for each agent to improve its capability of receiving more long-term information from the local environment. In the IrQL designs, a Q-function is defined on the basis of IRR function and an iterative IrQL algorithm is developed to learn optimally distributed control scheme, followed by the rigorous convergence and stability analysis. Furthermore, a distributed online learning framework, namely, reinforce-critic-actor neural networks, is established in the implementation of the proposed approach, which is aimed at estimating the IRR function, the Q-function, and the optimal control scheme, respectively. The implemented procedure is designed in a data-driven way without needing knowledge of the system dynamics. Finally, simulations and comparison results with the classical method are given to demonstrate the effectiveness of the proposed tracking control method.

关键词： optimal control Mathematical model Heuristic algorithms Iterative algorithms Artificial neural networks Nonlinear dynamical systems Multi-agent systems Neural networks (NNs) nonlinear multiagent systems (MASs) optimal tracking control reinforcement learning (RL)

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optimal tracking control of two-time-scale system under cyber-attacks 36

Optimal tracking control of two-time-scale system under cybe...

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36th Chinese control and Decision Conference (CCDC)

作者： Deng, Wudan Li, Qingkui Beijing Informat Sci & Technol Univ Sch Automat Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

In this paper, an optimal tracking problem of two-time-scale systems under cyber-attacks is studied. And an optimal tracking control method based on singular perturbation theory and reinforcement learning is proposed for the two-time-scale systems. Firstly, aiming at the singular perturbation parameter problem of the system, the system is decomposed into fast and slow subsystems by studying the singular perturbation theory. Secondly, the off-policy Q-learning is used to design the controller solution algorithm for the two subsystems respectively. The merit of the proposed algorithm is that the optimal tracking control law based on predicted states of systems can be learned using only measured data without knowing system dynamics. Besides, to solve the problem of system state data loss caused by cyber-attacks, a state predictor based on recurrent neural networks (RNN) is designed by using the historical data generated in the system operation. Finally, the effectiveness of the proposed method is proved by simulation examples.

关键词： Two-time-scale system off-policy Q-learning cyber-attacks recurrent neural network optimal tracking control

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