Adaptive PID controller based on Q -learning algorithm

CAAI Transactions on Intelligence Technology

article

Qian Shi¹  Hak-Keung Lam¹  Bo Xiao²  Shun-Hung Tsai³ 

[1] Department of Informatics, King's College London, Strand Campus;Hamlyn Centre for Robotic Surgery, Imperial College London;Graduate Institute of Automation Technology, National Taipei University of Technology

关键词: three-term control;    fuzzy control;    adaptive control;    learning (artificial intelligence);    linear systems;    adaptive PID controller;    adaptive proportional–integral–derivative controller;    cart–pole system;    linear PID controllers;    control process;    initial positions;    conventional PID controller;    Q-learning algorithm;    learned Q-tables;    C1230L Learning in AI;    C1340E Self-adjusting control systems;    C1340F Fuzzy control;    C1340L Linear control systems;   

DOI  :  10.1049/trit.2018.1007

学科分类：数学（综合）

来源: Wiley

【 摘 要 】

An adaptive proportional–integral–derivative (PID) controller based on Q -learning algorithm is proposed to balance the cart–pole system in simulation environment. This controller was trained using Q -learning algorithm and implemented the learned Q -tables to change the gains of linear PID controllers according to the state of the system during the control process. The adaptive PID controller based on Q -learning algorithm was trained from a set of fixed initial positions and was able to balance the system starting from a series of initial positions that are different from the ones used in the training session, which achieved equivalent or even better performances in comparison with the conventional PID controller and the controller only uses Q -learning algorithm. This indicates the advantage of the adaptive PID controller based on Q -learning algorithm both in the generality of balancing the cart–pole system from a relatively wide range of initial positions and in the stabilisability of achieving smaller steady-state error.

【 授权许可】

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