检索结果-内蒙古大学图书馆

Linear quadratic stochastic optimal control with state- and control-dependent noises: A deterministic data approach

NEUROCOMPUTING 2024年 575卷

作者： Zhang, Heng Yan, Zhiguo Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Informat & Automat Jinan 250353 Peoples R China

This paper investigates an infinite -horizon linear quadratic stochastic optimal control (LQSOC) problem with state- and control -dependent noises, in which two system matrices are unknown. First, a deterministic system is introduced and a relationship among its system trajectory, its control and the matrices to be solved is formulated. Subsequently, based on the deterministic system, an adaptive dynamic programming (ADP) algorithm is established to tackle the LQSOC problem. Then, the convergence analysis is presented by proving the equivalence between the proposed algorithm and an existing policy iteration algorithm. Finally, the effectiveness of the obtained algorithm is verified by a perturbed turbocharged diesel engine optimal control design example.

关键词： Linear quadratic Stochastic optimal control State-and control-dependent noises adaptive dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Generalized value iteration for discounted optimal control with stability analysis

引用

SYSTEMS & CONTROL LETTERS 2021年 147卷 104847-104847页

作者： Ha, Mingming Wang, Ding Liu, Derong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this work, the generalized value iteration with a discount factor is developed for optimal control of discrete-time nonlinear systems, which is initialized with a positive definite value function rather than zero. The convergence analysis of the discounted value function sequence is provided. The condition for the discount factor is given to guarantee the stability of the controlled plants. With this operation, the iterative control policy that asymptotically stabilizes the closed-loop system can be determined. The introduction of a discount factor has eased some conditions that the system dynamics and the initialization of the generalized value iteration need to fulfill. It is not required that the initial control policy is stabilizing. In the iteration process, under some conditions, if the system is asymptotically stable at the current iteration, then it can be guaranteed that the iterative control policies after this current iteration step also are stabilizing. It is convenient and practical to evaluate the asymptotic stability of the closed-loop system using the iterative control policy. A numerical example with physical background is carried out to validate the present results. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Reinforcement learning adaptive dynamic programming Generalized value iteration Admissible control policy Nonlinear systems Discounted optimal control

来源：评论

学校读者我要写书评

暂无评论

Fault-tolerant tracking control for nonlinear systems with multiplicative actuator faults in view of zero-sum differential games

引用

NEUROCOMPUTING 2024年 567卷

作者： Tan, Cong Lin, Mingduo Xia, Hongbing Zhao, Bo Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China

In view of zero-sum differential games, this paper addresses the fault-tolerant tracking control (FTTC) problem for nonlinear systems with multiplicative actuator faults. By augmenting the nominal system, the trajectory tracking problem is transformed into an optimal regulation problem. A fault observer is designed to estimate the multiplicative actuator fault factor. In order to better reflect system performance, a comprehensive performance index function including the trajectory tracking error, the control input, and the multiplicative actuator fault is constructed. By considering the control input and the multiplicative actuator fault as two players in the zero-sum differential game (ZSDG), the FTTC problem is thus transformed into a ZSDG problem. Then, a fault-tolerant control based on ZSDG is designed by solving the Hamilton-Jacobi-Isaacs (HJI) equation. Since the HJI equation is difficult to solve, a critic neural network is constructed to obtain its approximation, which constitutes the Nash equilibrium of the ZSDG. The closed-loop faulty system is guaranteed to be uniformly ultimately bounded via the Lyapunov's direct method. The effectiveness of the developed FTTC method is demonstrated by two simulation examples.

关键词： adaptive dynamic programming Reinforcement learning Zero-sum differential games Fault-tolerant control Multiplicative faults

来源：评论

学校读者我要写书评

暂无评论

Data-driven fault-tolerant control for nonlinear systems with output saturation

引用

CHAOS SOLITONS & FRACTALS 2023年第1期173卷

作者： Wang, Xianming Shao, Yiyu Zhang, Zhihao Shen, Mouquan Karimi, Hamid Reza Nanjing Tech Univ Sch Mech & Power Engn Nanjing 211816 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China Politecn Milan Dept Mech Engn I-20156 Milan Italy

This paper is focused on data-driven fault-tolerant control for nonlinear systems with output saturation. A modified observer with measurement output is built to approximate sensor fault. According to this approximation, corresponding data-driven fault-tolerant controller is proposed by utilizing optimization criteria and adaptive dynamic programming method. Consequently, a triggering rule is constructed by employing instantaneous and average measurement output. Resorting to the Lyapunov method, the resulted error system is uniformly ultimately bounded. The effectiveness of the presented approach is demonstrated by an example comparison.

关键词： Data-driven control Fault-tolerant adaptive dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 096205-096205页

作者： Dong, Bo Ding, Zhendong An, Tianjiao Cui, Yiming Zhu, Xinye Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China

Under an event-triggered mechanism, a non-zero-sum (NZS) game optimal tracking control method for modular robot manipulator (MRM) systems with input constraints is proposed using the adaptive dynamic programming (ADP) method based on integral reinforcement learning (IRL). First, a dynamic model of the MRM system is developed based on joint torque feedback technology, consisting of an n-joint subsystem related to interconnected dynamic coupling (IDC). Second, we design a robust compensation controller to handle the known model term and an optimal compensation controller to deal with the uncertainty term caused by the IDC and friction, respectively. In addition, a nonlinear disturbance observer is established to dispose of the negative effects caused by the uncertain sensor output disturbance. Third, based on differential game theory, we transform the optimal tracking control problem of the MRM system into an n-player NZS game problem. Then, the IRL-based ADP method is adopted, which relaxes the need for system partial unknown dynamic information, and only a critic neural network is used to solve the coupled Hamilton-Jacobi equation, so as to obtain the optimal control policy. Then, using Lyapunov theory, the tracking error of the MRM system is demonstrated to be uniformly ultimately bounded. Finally, the effectiveness and superiority of the proposed algorithm are verified through experiments.

关键词： modular robot manipulators non-zero-sum game adaptive dynamic programming input constraint and event-triggered mechanism

来源：评论

学校读者我要写书评

暂无评论

Multilayer adaptive critic design with digital twin for data-driven optimal tracking control and industrial applications

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期133卷

作者： Wang, Ding Ma, Hongyu Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Key Lab Computat Intelligence & Intelligent Syst Beijing 100124 Peoples R China Beijing Univ Technol Lab Smart Environm Protect Beijing 100124 Peoples R China Beijing Univ Technol Inst Artificial Intelligence Beijing 100124 Peoples R China

In this paper, an optimal trajectory tracking control problem for general nonlinear systems is investigated. An adaptive critic control method with the digital twin (DT) theory is developed. Divergent from the existing tracking control methods, the advantages of adaptive dynamic programming (ADP) and the theory of DT are combined in this paper, and the novel multilayer artificial system structure is constructed. The actioncritic structure is employed by each artificial system to obtain an approximate optimal control policy. The model network (MN) is built by using the actual input and output data sets of the controlled system, which means the dependence on the dynamics of the system is overcame. Then, the weights of the trained action network (AN) and MN are passed to the real system to realize the optimal tracking control. The feasibility of the algorithm is proved by theoretical analysis. Finally, the algorithm is applied to a simple nonlinear torsional pendulum system and an industrial wastewater treatment system (WWTS), and the effectiveness of the algorithm is verified. The algorithm effectively realizes the tracking control of nonlinear systems.

关键词： adaptive dynamic programming Data-driven control Digital twin theory Neural networks Tracking control Wastewater treatment

来源：评论

学校读者我要写书评

暂无评论

Zero-sum game-based security control of unknown nonlinear Markov jump systems under false data injection attacks

引用

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第0期

作者： Gao, Xiaobin Deng, Feiqi Zeng, Pengyu South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China

In order to solve the security control problem of discrete-time unknown Markov jump systems (MJSs) subject to false data injection attacks, a new zero-sum game-based control method is developed in this paper by using model-free adaptive dynamic programming algorithm. The controller and the attacker are regarded as two players. An optimal cost function of MJSs is firstly introduced based on the evolution of the Markov chain. Then, a critic-action-attack framework is established to approximate the cost function, control input and false data, respectively. Additionally, the boundedness of the neural network (NN) weight estimate errors is guaranteed via Lyapunov theory. In the end, the validity of the proposed approach is verified by simulation of a single link robot arm.

关键词： adaptive dynamic programming cyber attacks Markov jump systems security control zero-sum game

来源：评论

学校读者我要写书评

暂无评论

Robust adaptive Control of Maximum Power Point Tracking for Wind Power System

引用

IEEE ACCESS 2020年 8卷 214538-214550页

作者： Chen, Peng Han, Dezhi Li, Kuan-Ching Shanghai Maritime Univ Dept Comp Sci & Technol Shanghai 200120 Peoples R China Providence Univ Dept Comp Sci & Informat Engn CSIE Taichung 43301 Taiwan

A novel data-driven robust approximate optimal Maximum Power Point Tracking (MPPT) control method is proposed for the wind power generation system by using the adaptive dynamic programming (ADP) algorithm. First, a data-driven model is established by a recurrent neural network (NN) to reconstruct the wind power system dynamics using available input-output data. Then, in the design of the controller, based on the obtained data-driven model, the ADP algorithm is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Ulteriorly, developing a robustifying term to compensate for the NN approximation errors introduced by implementing the ADP method. Based on the Lyapunov approach, it proves the stability of the designed model and controller to show that the proposed controller guarantees the system power asymptotically tracking the maximum power. Finally, the simulation results demonstrate that the control method stabilizes the tip speed ratio near the optimal value when the wind speed is lower than the rated wind speed. Moreover, the tracking response speed of the proposed method is fast, which enhances the stability and robustness of the system.

关键词： Wind power generation Wind energy Wind speed Mathematical model dynamic programming Wind turbines Recurrent neural networks adaptive dynamic programming maximum power point tracking neural network wind power system

来源：评论

学校读者我要写书评

暂无评论

Optimized intelligent tracking control for a quadrotor unmanned aerial vehicle with actuator failures

引用

AEROSPACE SCIENCE AND TECHNOLOGY 2024年 144卷

作者： Li, Bo Liu, Hui Ahn, Choon Ki Gong, Wenquan Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 201306 Peoples R China Korea Univ Sch Elect Engn Seoul 136701 South Korea

This work investigates the implementation of tracking control for a quadrotor unmanned aerial vehicle exposed to disturbances and actuator failures. Cost reduction in control processes is not usually considered in conventional control methods. To overcome this limitation, this work proposes an optimized intelligent control scheme that utilizes adaptive dynamic programming. Firstly, a critic-only structure is employed to learn the nominal control strategies, including two critic neural network update laws, lifting the persistent excitation condition. Subsequently, two specifically designed observers are developed on the radial basis function neural network (RBFNN) to reconstruct the disturbances and actuator failures and compensate for the nominal strategies. The observers achieve fixed-time convergence by employing the fixed-time update laws of the RBFNNs. Moreover, two variable vectors using the Gaussian error function are introduced to reduce the impact caused by the observation errors. Additionally, the superior performance of the proposed control scheme is validated through numerical simulations.

关键词： Quadrotor adaptive dynamic programming Fault-tolerant control Radial basis function neural network

来源：评论

学校读者我要写书评

暂无评论

dynamic compensator-based near-optimal control for unknown nonaffine systems via integral reinforcement learning

引用

NEUROCOMPUTING 2024年 564卷

作者： Lin, Jinquan Zhao, Bo Liu, Derong Wang, Yonghua 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 Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, a dynamic compensator-based near-optimal control approach for unknown nonaffine nonlinear systems is developed by using integral reinforcement learning. Since system dynamics is unknown, it is difficult to obtain the optimal control policy via neuro-dynamic programming. To address this problem, a general dynamic compensator is introduced as the virtual control input to augment the unknown nonaffine nonlinear system as a partially unknown affine system. For the augmented system, a novel quadratic value function is designed with the system states, the actual control input and the virtual control input. The optimal control of the augmented system can be regarded as the near-optimal control for the original system since the novel optimal value function is an upper bound of the original optimal value function. In order to avoid the identification of system dynamics, the integral reinforcement learning framework is utilized to derive the optimal control based on the solution of Hamilton-Jacobi-Bellman equation via the critic-only structure. Meanwhile, the weight learning rule of the critic neural network is presented with the experience replay technique to relax the persistence of excitation condition. Moreover, the uniform ultimate boundedness of weight estimation errors and the stability of the closed-loop system are guaranteed by using the Lyapunov's direct method. Finally, simulation results of two examples demonstrate the effectiveness of the developed dynamic compensator-based near-optimal control method.

关键词： Neuro-dynamic programming adaptive dynamic programming Reinforcement learning Optimal control Neural networks dynamic compensator

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：