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

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

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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

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Generalized value iteration for discounted optimal control with stability analysis

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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

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

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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

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

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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

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