Self-Tuning Method of PID Parameters Based on Belief Rule Base Inference

Xiao-Bin Xu, Xue Ma, Cheng-Lin Wen, Da-Rong Huang, Jian-Ning Li


As a generic inference mechanism, the belief rule-based (BRB) system can effectively integrate quantitative information with qualitative knowledge to model causal relationships of complex application systems. Based on the BRB, this paper develops a novel self-tuning strategy of PID parameters such that the output of closed-loop control system generated by PID controller can accurately follow control input. Firstly, the initial belief rule base is abstracted from expert’s control experiences to depict the highly nonlinear relationship between the variables of control system and each PID parameter. Secondly, the objective function is established to minimize the error between the given control input and the closed loop output, and then the online optimization method via sequential linear programming is presented to optimize the parameters of BRB system so as to adaptively adjust PID parameters by the optimized BRB system in real time. Typical control simulation experiments of DC motor are implemented to illustrate the advantages of the proposed BRB-PID over widely used neural network-based PID.



Belief Rule Base (BRB); PID controller; Sequential linear programming (SLP) algorithm; Evidence Reasoning(ER)

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Print ISSN: 1392-124X 
Online ISSN: 2335-884X