Adaptive Critic Nonlinear Robust Control: A Survey

By Ding Wang, Haibo He, Derong Liu. IEEE Transactions on Cybernetics, 2017

322 citations recorded by the source corpus.

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear $ {H_{\infty }}$ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4df845ef004f67585db016200f63cfe62ead4dc8

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Citations in the corpus

State-of-the-art in artificial neural network applications: A survey (2018, background): “Recently, research interest in brain functionality has rapidly increased globally [16].”
Adaptive Dynamic Programming for Control: A Survey and Recent Advances (2021, methodology): “Remark 4: According to the relationship between the optimal value function and the optimal control derived from the HJB equation, the actor network can be omitted for afﬁne nonlinear systems and only a single-critic network is used [15], [59], [65], [139].”