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作者：Qi, Yiwen; Zhang, Simeng; Shi, Yang
作者单位：Fuzhou University; Shenyang Aerospace University; University of Victoria
摘要：This article studies active disturbance rejection control (ADRC) for uncertain switched systems under triggered learning. The novel triggered-learning ADRC framework optimizes the ADRC performance of switched systems through reinforcement learning (RL) and enables on-demand updates of neural networks with guidance from a predesigned trigger. The innovation of this article is mainly reflected in four aspects: First, the RL-based gain automatic update mechanism (i.e., the dual-gain optimization ...
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作者：Chang, Zeze; Jiao, Junjie; Li, Zhongkui
作者单位：Peking University; Technical University of Munich
摘要：This article considers a localized data-driven consensus problem for leader-follower multiagent systems with unknown discrete-time agent dynamics, where each follower computes its local control gain using only their locally collected state and input data. Both noiseless and noisy data-driven protocols are presented to achieve leader-follower consensus, by addressing the challenge of the heterogeneity in control gains caused by the localized data sampling and distinct parameters of agents. The ...
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作者：Sun, Qingdong; Yang, Guang-Hong
作者单位：Northeastern University - China; Northeastern University - China
摘要：This article studies the secure state estimation problem of continuous-time cyber-physical systems affected by sensor attacks and actuator faults, where the attackers do not target fixed transmission channels but instead stochastically select the channels to attack at each moment based on a specific Markov process. To address these challenges, a reduced-order observer is designed to simultaneously estimate the original system states, faults, and attacks, while ensuring that its observation err...
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作者：Xu, Yong; Wu, Zheng-Guang
作者单位：Beijing Institute of Technology; Zhejiang University
摘要：This article investigates the adaptive optimal output regulation of completely unknown linear time-invariant systems. First, a dynamic state feedback control policy with assured convergence rate requirement is developed such that the output regulation problem is transformed into a tractable optimization problem by incorporating the internal model. Then, an online-verifiable initial excitation-based dual-integrator-based learning algorithm is first proposed for establishing data-driven learning...
