Adaptive PID controller based on Q ‐learning algorithm. Issue 4 (14th November 2018)

• Record Type: Journal Article
• Authors: Shi, Qian; Lam, Hak‐Keung; Xiao, Bo; Tsai, Shun‐Hung
• Is Part Of: CAAI transactions on intelligence technology. Volume 3:Issue 4(2018)
• Page Start: 235
• Page End: 244
• Publication Date: 2018-11-14
• Subjects: 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; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2018.1007
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : 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.