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Disturbance observer-based fuzzy adaptive optimal finite-time control for nonlinear systems

APPLIED MATHEMATICS IN SCIENCE AND ENGINEERING 2023年第1期31卷

作者： Sun, Zidong Liang, Li Gao, Wei Yunnan Normal Univ Sch Informat Sci & Technol Kunming Peoples R China Yunnan Normal Univ Sch Informat Sci & Technol Kunming 650500 Peoples R China

This paper investigates the issue of disturbance observer-based fuzzy adaptive optimal finite-time control in light of the backstepping approach for strict-feedback nonlinear systems with bias fault term and full state constraints. Considering that external disturbance and bias fault signal can affect the stability of control and control quality, a disturbance observer is constructed to track the external disturbances and bias fault online. A disturbance observer-based finite-time control strategy is proposed to achieve optimized control by utilizing the fuzzy logic system approximation-based adaptive dynamic programming method under the critic-actor framework. The purpose of the critic is to evaluate control performance and the role of the actor is to execute control behaviour. In addition, it is proved that the proposed fuzzy adaptive optimal finite-time scheme not only realizes all signals in closed-loop system are bounded, but also ensures that system states are restricted within specific sets. Finally, simulation results are shown to demonstrate the effectiveness of the proposed control strategy.

关键词： adaptive dynamic programming fuzzy adaptive optimal finite-time control external disturbance observer state observer

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Discrete-Time Multi-Player Games Based on Off-Policy Q-Learning

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IEEE ACCESS 2019年 7卷 134647-134659页

作者： Li, Jinna Xiao, Zhenfei Li, Ping Liaoning Shihua Univ Sch Informat & Control Engn Fushun 113001 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

In this paper, an off-policy game Q-learning algorithm is proposed for solving linear discrete-time non-zero sum multi-player game problems. Unlike the existing Q-learning methods for solving the Riccati equation by on-policy learning approaches for multi-player games, an off-policy game Q-learning method is developed for achieving the Nash equilibrium of multiple players. To this end, first, a non-zero sum game problem is formulated, and the value function and the Q-function defined according to each-player individual performance index are rigorously proved to be linear quadratic forms. Then, based on the dynamic programming and Q-learning methods, an off-policy game Q-learning algorithm is developed to find the control policies for multi-player games, such that the Nash equilibrium is reached under the learned control policies. The merit of this paper lies in that the proposed algorithm does not require the system model parameters to be known a priori and fully utilizes measurable data to learn the Nash equilibrium solution. Moreover, there is no bias of Nash equilibrium solution when implementing the proposed off-policy game Q-learning algorithm even though probing noises are added to control policies for maintaining the persistent excitation condition. While bias of the Nash equilibrium solution could be produced if on-policy game Q-learning is employed. This is another contribution of this paper

关键词： adaptive dynamic programming off-policy Q-learning non-zero sum game Nash equilibrium discrete-time systems

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Prioritized experience replay based reinforcement learning for adaptive tracking control of autonomous underwater vehicle

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APPLIED MATHEMATICS AND COMPUTATION 2023年第1期443卷

作者： Li, Ting Yang, Dongsheng Xie, Xiangpeng Beijing Inst Graph Commun Coll Mech & Elect Engn Beijing 102627 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China

A novel adaptive trajectory tracking control method is proposed in this paper for au- tonomous underwater vehicle systems with external disturbances. The control action is composed of sliding mode control and action-depended heuristic dynamic programming (ADHDP) controller to track the expected sailing position and angle in the horizontal co- ordinate system with fixed depth. As an auxiliary control of sliding mode control, the AD- HDP controller observes the difference between the actual sailing position/angle and the expected sailing position/angle, and adaptively provides corresponding supplementary con- trol actions using a data-driven fashion. At the same time, we design a weight-related pri- ority experience replay (PER) technology to update the online weight network by using the relevant historical data stored in the database to improve the learning rate. The proposed algorithm can adjust parameters online under various conditions. Furthermore, it is very suitable for underwater vehicle systems with parameter uncertainties and external distur- bances. Based on Lyapunov stability method, the stability of closed-loop system state and network weight error is analyzed. Finally, the validity of our control strategy is verified by simulation.(c) 2022 Elsevier Inc. All rights reserved.

关键词： adaptive dynamic programming Tracking control Disturbances Data-driven Experience replay Autonomous underwater vehicle

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Fault-tolerant control for nonlinear offshore steel jacket platforms based on reinforcement learning

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OCEAN ENGINEERING 2022年 246卷 110247-110247页

作者： Ziaei, Amin Kharrati, Hamed Rahimi, Afshin Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Univ Windsor Dept Mech Automot & Mat Engn Windsor ON N9B 3P4 Canada

In this article, reinforcement learning based fault-tolerant control for the nonlinear offshore platform is studied. First, the dynamic model of the nonlinear steel-jacket offshore platform with active mass damper and actuator fault is investigated then an affine nonlinear representation for the offshore platform is obtained. By estimating actuator fault and irregular wave force and inserting their estimation into the cost function, the disturbance control and fault-tolerant control problem (FTC) are converted into optimal control problem. Online policy iteration is used to minimize the performance index. Finally, simulation results show the effectiveness of our method.

关键词： Nonlinear steel-jacket offshore platforms Fault-tolerant control Fault observer Reinforcement learning adaptive dynamic programming Disturbance observer

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Dual-type-triggers-based cooperative adaptive critic control of swarm UAVs under FDI attacks

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AUTOMATICA 2024年 167卷

作者： Wu, Ying Chen, Mou Chadli, Mohammed Li, Hongyi Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Univ Paris Saclay Univ Evry IBISC F-91020 Gif Sur Yvette France Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Chongqing Key Lab Gener Technol & Syst Serv Robots Chongqing 400715 Peoples R China

This paper solves the problem of distributed adaptive event -triggered optimal control for six -rotor unmanned aerial vehicles under compound false data injection attacks and lumped disturbances by using the adaptive dynamic programming algorithm. Two types of triggers are introduced, one for achieving intermittent communication between unmanned aerial vehicles and the other for acting on the electronic speed data transmission network of each unmanned aerial vehicle to address bandwidth limitations. Additionally, the design of all triggers takes into consideration the adverse effects of introducing the event -triggered mechanism on system performance, and all trigger mechanisms do not display Zeno phenomena. Then, to guarantee the robustness of the unmanned aerial vehicle systems, a novel performance index function that considers attack information is designed, and a distributed disturbance observer is also designed. Moreover, on account of finding the Hamilton- Jacobi-Bellman partial differential equation more efficiently, an intelligent learning control algorithm applying identifier -critic neural networks is proposed. Using the Lyapunov stability analysis method, the proposed disturbance -observer -based optimal cooperative control scheme can guarantee that all signals in the swarm systems are bounded and the consensus control objective is achieved. Finally, some relevant simulation verification diagrams are given to test the effectiveness of the designed control scheme. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Six-rotor unmanned aerial vehicles Two types of triggers Compound false data injection attacks adaptive dynamic programming Disturbance observer

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Fault-tolerant control for second-order nonlinear systems with actuator faults via zero-sum differential game

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Ma, Yajie Meng, Qingyuan Jiang, Bin Ren, Hao Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Dept Automat Control 29 Jiangjun Ave Nanjing 211111 Peoples R China

Stable operations of control systems play a vital role in mission accomplishments of the second-order nonlinear systems, such as six-axis robots used in intelligent production lines, industrial equipment and control systems. In this paper, a fault-tolerant control scheme is developed for a class of second-order nonlinear control system under actuator bias faults and loss of effectiveness faults via the zero-sum differential game method. Based on the backstepping method, a controller is designed to ensure system tracking performance. Then by the zero-sum differential game method, a fault-tolerant controller is designed for the equivalent error system. Simulation results show the validity of the designed fault-tolerant control scheme.

关键词： adaptive dynamic programming Fault-tolerant control Nonlinear systems Zero-sum differential game

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Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments

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JOURNAL OF NEURAL ENGINEERING 2014年第2期11卷 026019-026019页

作者： Oubbati, Mohamed Kord, Bahram Koprinkova-Hristova, Petia Palm, Guenther Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany Bulgarian Acad Sci Inst Informat & Commun Technol BG-1040 Sofia Bulgaria

The new tendency of artificial intelligence suggests that intelligence must be seen as a result of the interaction between brains, bodies and environments. This view implies that designing sophisticated behaviour requires a primary focus on how agents are functionally coupled to their environments. Under this perspective, we present early results with the application of reservoir computing as an efficient tool to understand how behaviour emerges from interaction. Specifically, we present reservoir computing models, that are inspired by imitation learning designs, to extract the essential components of behaviour that results from agent environment interaction dynamics. Experimental results using a mobile robot are reported to validate the learning architectures.

关键词： agent-environment interaction recurrent neural networks adaptive dynamic programming learning from demonstration system identification

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Model-free intelligent critic design with error analysis for neural tracking control

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NEUROCOMPUTING 2024年 572卷

作者： Gao, Ning Wang, Ding Zhao, Mingming Hu, Lingzhi Beijing Univ Technol Fac Informat 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

The core of the optimal tracking control problem for nonlinear systems is how to ensure that the controlled system tracks the desired trajectory. The utility functions in previous studies have different properties which affect the final tracking effect of the intelligent critic algorithm. In this paper, we introduce a novel utility function and propose a Q -function based policy iteration algorithm to eliminate the final tracking error. In addition, neural networks are used as function approximator to approximate the performance index and control policy. Considering the impact of the approximation error on the tracking performance, an approximation error bound for each iteration of the novel Q -function is established. Under the given conditions, the approximation Q -function converges to the finite neighborhood of the optimal value. Moreover, it is proved that weight estimation errors of neural networks are uniformly ultimately bounded. Finally, the effectiveness of the algorithm is verified by the simulation example.

关键词： adaptive dynamic programming Approximation errors Model-free control Optimal tracking control Policy iteration Neural networks

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adaptive neural control of PEMFC system based on data-driven and reinforcement learning approaches

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CONTROL ENGINEERING PRACTICE 2022年第0期120卷 105022-105022页

作者： Lin-Kwong-Chon, Christophe Damour, Cedric Benne, Michel Kadjo, Jean-Jacques Amangoua Grondin-Perez, Brigitte Univ Reunion Isl LE2P EnergyLab EA 4079 15 Ave Rene CassinBP 7151 F-97715 St Denis Reunion France

Proton exchange membrane fuel cell systems are being increasingly put forward as hydrogen energy carrier converters. Recent advancements in reliability strategies have been stimulated through maintaining a healthy operating condition of the system and covering plant faults. However, it is observed that occurrence or even the mitigation of these faults cause multilateral effects that can potentially destabilize the normal operation of the system. In the active fault tolerant control strategy, two modules are designed to fault management. The diagnostic module identifies the apparent fault and identifies the corrective commands, then the re-design module adapts the controller to dynamic system changes. In order to improve the generic characteristics of the re-design module, this paper presents a data-driven neural controller capable to automatically adapt to system health states. The developed approach comes from the machine learning class and combines adaptive dynamic programming, deep echo-state neural network models and fuzzy logic learning. The proposed controller is evaluated under occurrence of channels flooding and membrane drying faults, but also actuators and water purging disturbances. Simulation and experimental results show the effectiveness of the proposed data-driven approach without prior neural model training, while guaranteeing the stability and learning convergence of the adaptive controller.

关键词： Proton exchange membrane fuel cell Active fault tolerance adaptive dynamic programming Reinforcement learning Deep echo state network

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dynamic Energy Management System for a Smart Microgrid

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2016年第8期27卷 1643-1656页

作者： Venayagamoorthy, Ganesh Kumar Sharma, Ratnesh K. Gautam, Prajwal K. Ahmadi, Afshin Clemson Univ Real Time Power & Intelligent Syst Lab Clemson SC 29634 USA Univ KwaZulu Natal Eskom Ctr Excellence HVDC Engn ZA-4041 Durban South Africa NEC Labs Amer Inc Energy Management Dept Cupertino CA 95014 USA Spirae Inc Ft Collins CO 80524 USA Clemson Univ Dept Elect & Comp Engn Clemson SC 29634 USA

This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar;however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid's system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.

关键词： adaptive dynamic programming dynamic energy management system (DEMS) evolutionary computing microgrid neural networks reinforcement learning renewable energy

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