Data‐based robust optimal control of discrete‐time systems with uncertainties via adaptive dynamic programming

Liu, Yang; Xing, Zuoxia; Chen, Zhe; Xu, Jie

doi:10.1002/oca.2775

Cited by 10 publications

(8 citation statements)

References 38 publications

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“…In order to deal with unknown dynamics, reinforcement learning (RL) [5][6][7] and approximate dynamic programming (ADP) [8][9][10] have been developed. Recent applica-tions of RL can be found in areas such as autonomous vehicles and unmanned aerial vehicles [11,12].…”

Section: Introductionmentioning

confidence: 99%

Finite‐horizon Q‐learning for discrete‐time zero‐sum games with application to H∞$$ {H}_{\infty } $$ control

Liu

Cai

Meng

et al. 2023

Asian Journal of Control

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In this paper, we investigate the optimal control strategies for model-free zero-sum games involving the H ∞ control. The key contribution is the development of a Q-learning algorithm for linear quadratic games without knowing the system dynamics. The finite-horizon setting is more practical than the infinite-horizon setting, but it is difficult to solve the time-varying Riccati equation associated with the finite-horizon setting directly. The proposed algorithm is shown to solve the time-varying Riccati equation iteratively without the use of models, and numerical experiments on aircraft dynamics demonstrate the algorithm's efficiency.

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Section: Introductionmentioning

confidence: 99%

Finite‐horizon Q‐learning for discrete‐time zero‐sum games with application to H∞$$ {H}_{\infty } $$ control

Liu

Cai

Meng

et al. 2023

Asian Journal of Control

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“…Adaptive dynamic programming (ADP) [1][2][3][4][5][6][7] has developed a lot, recently. The model-free version of ADP relies on sampling for optimization decisions, rather than relying on mechanism models.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven robust value iteration control with application to wind turbine pitch control

Liu

Jiang

Hao

et al. 2021

Optim Control Appl Methods

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For the large equipment, system model is usually difficult to obtain, or changes slowly with the running time. Therefore, some methods based on accurate model are difficult to apply, such as wind turbine. Therefore, the data‐based robust value iteration method based on data is studied for variable pitch control of wind turbine for the first time, which can deal with uncertainty and do not require an accurate model. First, a new data‐driven Bellman equation is derived base on the system operation data. Next, the robust VI structure based on data is designed, which is a model free iteration algorithm. Then, a data‐driven value iteration pitch controller is proposed, the simulation results show the effectiveness of the control method.

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“…A partial model‐free sliding mode control strategy is proposed for a class of disturbed systems 14 . A new data‐based adaptive dynamic programming algorithm is proposed to solve the optimal control policy for discrete‐time systems with uncertainties 15 . A method that applies event‐triggered mechanism

{\mathrm{H}}_{\infty }

control to continuous‐time nonlinear systems with asymmetric constraints based on dual heuristic dynamic programming structure is proposed 16 .…”

mentioning

confidence: 99%

“…11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third group of papers [13][14][15][16][17][18][19] focuses on robustness on data-based optimal learning control. A novel Nash game-theoretical optimal adaptive robust control design approach is proposed to address the constraint-following control problem for the uncertain underactuated mechanical systems with fuzzy evidence theory.…”

mentioning

confidence: 99%

“…14 A new data-based adaptive dynamic programming algorithm is proposed to solve the optimal control policy for discrete-time systems with uncertainties. 15 A method that applies event-triggered mechanism H ∞ control to continuous-time nonlinear systems with asymmetric constraints based on dual heuristic dynamic programming structure is proposed. 16 A novel anti-disturbance inverse optimal controller design method is proposed for a class of high-dimensional chain structure systems with any disturbances, matched, or mismatched.…”

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confidence: 99%

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Data‐based learning control for optimization of nonlinear systems

Wei

Song

Zhang

et al. 2023

Optim Control Appl Methods

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With the development of science and technology, practical systems such as the power systems, traffic systems, robot manipulator systems, etc., have become more complex. Therefore, it is difficult to build practical systems by accurate models. Under the lack of accurate process models, using system data to improve system performance and learn optimal decisions becomes very important. Through the recent years, data-based learning control theories and technologies have widely been investigated, including adaptive dynamic programming, reinforcement learning, iterative learning control, and so on. Data-based methods require the system data instead of the accurate knowledge of system dynamics that can be considered as model-free learning control methods. The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue.This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers [1][2][3][4][5][6][7] focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector. 1 A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. 2 A multi-verse optimizer tuned PI-type active disturbance rejection generalized predictive control method is described for the motion control problems of ships. 3 The sufficient optimality conditions for the optimal controls are established under some convexity assumptions. 4 A receding-horizon reinforcement learning algorithm is proposed for near-optimal control of continuous-time systems under control constraints. 5 In order to solve the interference compensation control problem of a class of nonlinear systems, a method based on memory data is introduced to suppress interference greatly. 6 A new controller design method is proposed for the trajectory tracking problem of robots with imprecise dynamic properties and interference. 7 The second group of papers 8-12 considers iterative learning identification and iterative learning control. An iterative learning control approach is proposed for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. 8 The quantized data-based iterative learning tracking control problem is studied for nonlinear networked control systems with signals quantization and denial-of-service attacks. 9 The output tracking problem is considered for a class of nonlinear parabolic distributed parameter systems with moving boundaries. 10 A just-in-time learning based dual heuristic programming algorithm is proposed to optimize the control performance of autonomous wheeled mobile robots under faults or disturbances. 11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third...

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Data‐based robust optimal control of discrete‐time systems with uncertainties via adaptive dynamic programming

Cited by 10 publications

References 38 publications

Finite‐horizon Q‐learning for discrete‐time zero‐sum games with application to H∞$$ {H}_{\infty } $$ control

Finite‐horizon Q‐learning for discrete‐time zero‐sum games with application to H∞$$ {H}_{\infty } $$ control

Data‐driven robust value iteration control with application to wind turbine pitch control

Data‐based learning control for optimization of nonlinear systems

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