Adaptive optimal output feedback tracking control for unknown discrete‐time linear systems using a combined reinforcement Q‐learning and internal model method

Sun, Wei; Zhao, Guangyue; Peng, Yunjian

doi:10.1049/iet-cta.2019.0560

Cited by 12 publications

(8 citation statements)

References 38 publications

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“…These periodic disturbances can usually be described by some kinds of sinusoidal signals. For a tracking system, to reduce the tracking error, some strategies have been adopted in literature, such as internal model control(IMC) [8], [9], predictive control [10], [11], sliding mode control [12], [13]. Among these methods, internal model control is an effective method to eliminate certain kinds of interferences like periodic sinusoidal disturbances.…”

Section: Introductionmentioning

confidence: 99%

Networked Tracking Control Based on Internal Model Principle and Adaptive Event-Triggering Mechanism

2023

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In this paper, the non-static error tracking control issue for the networked control system is considered. First, the unstable modes of the external disturbance signal and the output signal of the reference system are established to form a new series system with the controlled objects. Then, an internal model compensation controller is presented to achieve non-static tracking error. Furthermore, an adaptive eventtriggering mechanism is introduced as a communication method to improve network resource utilization. By formulating the tracking control problem of the networked control system as the stabilization problem of a time-varying time-delay system, a sufficient condition such that the closed-loop system is asymptotically stable and satisfies the H∞ output tracking performance is derived. Finally, the simulation examples illustrate the feasibility and effectiveness of the proposed approach.INDEX TERMS The internal model principle, The H∞ output tracking control, Networked control systems, Adaptive event-triggering mechanisms, Time-delay systems

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Section: Introductionmentioning

confidence: 99%

Networked Tracking Control Based on Internal Model Principle and Adaptive Event-Triggering Mechanism

2023

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“…Specifically, an adaptive PID controller was proposed in [14], a self-tuning PID controller for a soccer robot was proposed in [15], and a Q-Learning approach was used to tune an adaptive PID controller in [16]. Moreover, the Q-learning algorithm was applied to the tracking problem for discrete-time systems with an H ∞ approach [17] and linear quadratic tracking control [18,19]. Recently, some relevant applications of model-free reinforcement learning for tracking problems can be found in [20,21].…”

Section: Introductionmentioning

confidence: 99%

Design and Comparison of Reinforcement-Learning-Based Time-Varying PID Controllers with Gain-Scheduled Actions

Yeh

Yang

2021

Machines

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This paper presents innovative reinforcement learning methods for automatically tuning the parameters of a proportional integral derivative controller. Conventionally, the high dimension of the Q-table is a primary drawback when implementing a reinforcement learning algorithm. To overcome the obstacle, the idea underlying the n-armed bandit problem is used in this paper. Moreover, gain-scheduled actions are presented to tune the algorithms to improve the overall system behavior; therefore, the proposed controllers fulfill the multiple performance requirements. An experiment was conducted for the piezo-actuated stage to illustrate the effectiveness of the proposed control designs relative to competing algorithms.

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“…3) Inspired by the research on ADP-based linear quadratic tracking problems [32], [33], we extended the system states with an approximated external disturbance and introduced an intermedia auxiliary system to generate data for robust critic learning. To the best of our knowledge, this is the first research article that combines robust design and active disturbance attenuation in an ADP-based control scheme.…”

Section: Introductionmentioning

confidence: 99%

Online Adaptive Critic Learning Control of Unknown Dynamics With Application to Deep Submergence Rescue Vehicle

Wang

Liu

2020

IEEE Access

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From 2017 to 2019, he joined Stanford University as a Visiting Student, to collaborate with the Soltesz Laboratory, funding provided by the China Scholarship Council. His current research interests include underwater vehicle system stabilization motion control, intelligent underwater vehicle system based on reinforcement learning, control systems, and pattern recognition. Mr. Wang was a recipient of the two programming contests rewards and one National Electronics Design Contest Rewards.

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Adaptive optimal output feedback tracking control for unknown discrete‐time linear systems using a combined reinforcement Q‐learning and internal model method

Cited by 12 publications

References 38 publications

Networked Tracking Control Based on Internal Model Principle and Adaptive Event-Triggering Mechanism

Networked Tracking Control Based on Internal Model Principle and Adaptive Event-Triggering Mechanism

Design and Comparison of Reinforcement-Learning-Based Time-Varying PID Controllers with Gain-Scheduled Actions

Online Adaptive Critic Learning Control of Unknown Dynamics With Application to Deep Submergence Rescue Vehicle

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