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检索条件"主题词=event-triggered adaptive dynamic programming"

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event-triggered adaptive dynamic programming for Continuous-time Nonlinear System Using Measured Input-Output Data

Event-Triggered Adaptive Dynamic Programming for Continuous-...

引用

International Joint Conference on Neural Networks

作者： Xiangnan Zhong Zhen Ni Haibo He Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (纸本)9781479919611

In this paper, we propose a novel event-triggered adaptive dynamic programming (ADP) method using only the input-output data. event-triggered method is widely used for its computational efficiency capacity. Comparing with the traditional method which updates the controller periodically, the event-triggered method only updates the controller when it is necessary and therefore the computation is reduced. Generally, the triggered condition is based on the system current and sampled states. In this paper, we consider a neural-network-based observer to recover the system dynamics using the measured input-output data. The triggered instants are calculated according to the recovered state. Stability analysis of the proposed approach is presented. We verify our proposed method through a robot-arm example.

关键词： event-triggered adaptive dynamic programming Continuous-time Nonlinear System Measured Input-Output Data

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Self-adaptive Faulted Spacecraft Attitude Adjustment event-triggered Control Via Reinforcement Learning Critic Mechanism 43

Self-Adaptive Faulted Spacecraft Attitude Adjustment Event-T...

引用

43rd Chinese Control Conference (CCC)

作者： Huang, Jing Meng, Yizhen Cheng, Ming Yan, Xiaoxuan Shanghai Aerosp Control Technol Inst Shanghai key Lab Aerosp Intelligent Control Techn Shanghai Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Addressing the intricate challenge of achieving optimal ignition and attitude control during orbital deviation in a malfunctioning spacecraft with constrained inputs, this study integrates the Actor-Critic control framework derived from reinforcement learning into the interconnected constraints of spacecraft orbit decay and attitude control. Initially, we acquire an evaluative metric (Critic neural network) to assess the effectiveness of ignition and orbital deviation attitude control. Subsequently, this metric is amalgamated with spacecraft attitude control errors to formulate a self-adjusting, event-triggered control strategy for spacecraft ignition and orbital deviation utilizing adaptive dynamic programming. Given that both performance metrics entail future states and lack direct computability, the robust approximation capabilities of neural networks are harnessed to address the evaluation network and the input-limited optimal performance metric of adaptive dynamic programming. This methodological approach aims to pinpoint the optimal attitude control for orbital deviation in malfunctioning spacecraft. Ultimately, the results from numerical simulations vividly illustrate the efficacy of the algorithm developed in this investigation.

关键词： Spacecraft deorbit self-driven reinforcement learning event-triggered adaptive dynamic programming adaptive neural network

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Self-adaptive Faulted Spacecraft Attitude Adjustment event-triggered Control Via Reinforcement Learning Critic Mechanism

Self-Adaptive Faulted Spacecraft Attitude Adjustment Event-T...

引用

第43届中国控制会议

作者： Jing Huang Yizhen Meng Ming Cheng Xiaoxuan Yan Shanghai Aerospace Control Technology Institute Shanghai key laboratory of aerospace intelligent control technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Addressing the intricate challenge of achieving optimal ignition and attitude control during orbital deviation in a malfunctioning spacecraft with constrained inputs, this study integrates the Actor-Critic control framework derived from reinforcement learning into the interconnected constraints of spacecraft orbit decay and attitude control. Initially, we acquire an evaluative metric(Critic neural network) to assess the effectiveness of ignition and orbital deviation attitude control. Subsequently, this metric is amalgamated with spacecraft attitude control errors to formulate a self-adjusting, eventtriggered control strategy for spacecraft ignition and orbital deviation utilizing adaptive dynamic programming. Given that both performance metrics entail future states and lack direct computability, the robust approximation capabilities of neural networks are harnessed to address the evaluation network and the input-limited optimal performance metric of adaptive dynamic programming. This methodological approach aims to pinpoint the optimal attitude control for orbital deviation in malfunctioning spacecraft. Ultimately, the results from numerical simulations vividly illustrate the efficacy of the algorithm developed in this investigation.

关键词： Spacecraft deorbit self-driven reinforcement learning event-triggered adaptive dynamic programming adaptive neural network

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