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- 저자 : Yongfeng Lv (검색결과 2 건)
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Approximate Optimal Stabilization Control of Servo Mechanisms based on Reinforcement Learning Scheme
Yongfeng Lv,Xuemei Ren,Shuangyi Hu,Hao Xu 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.10
A reinforcement learning (RL) based adaptive dynamic programming (ADP) is developed to learn the approximate optimal stabilization input of the servo mechanisms, where the unknown system dynamics are approximated with a three-ayer neural network (NN) identifier. First, the servo mechanism model is constructed and a three-layer NN identifier is used to approximate the unknown servo system. The NN weights of both the hidden layer and output layer are synchronously tuned with an adaptive gradient law. An RL-based critic three-layer NN is then used to learn the optimal cost function, where NN weights of the first layer are set as constants, NN weights of the second layer are updated by minimizing the squared Hamilton-Jacobi-Bellman (HJB) error. The optimal stabilization input of the servomechanism is obtained based on the three-layer NN identifier and RL-based critic NN scheme, which can stabilize the motor speed from its initial value to the given value. Moreover, the convergence analysis of the identifier and RL-based critic NN is proved, the stability of the cost function with the proposed optimal input is analyzed. Finally, a servo mechanism model and a complex system are provided to verify the correctness of the proposed methods.
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Output-feedback Robust Tracking Control of Uncertain Systems via Adaptive Learning
Jun Zhao,Yongfeng Lv 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.4
This paper presents an adaptive learning method to achieve the output-feedback robust tracking control of systems with uncertain dynamics, which uses the techniques developed for optimal control. An augmented system is first constructed using the system state and desired output trajectory. Then, the robust tracking control problem is equivalent to the optimal tracking control problem with an appropriate cost function. To design the output-feedback optimal tracking control, an output tracking algebraic Riccati equation (OTARE) is then constructed, which can be used in the online learning process. To obtain the solution of the derived OTARE, an online adaptive learning method is proposed, where the input gain matrix is removed. In this learning algorithm, only the system output information is required and the observers widely used in the output-feedback optimal control design are removed. Simulations based on the power system are given to test the proposed method.
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