Online Tuning of the Fuzzy PID Controller using the Back-Propagation Algorithm

Abstract This paper presents a novel methodology for the   online update the fuzzy rule base of the type-1 fuzzy logic system that estimates the proportional-integral-derivative (PID) gains of the professional PID control. Two different types of benchmarking PID controllers are used to compare the performance of the proposed methodology. The first controller is the so-called professional PID (P-PID), where the proportional gain KP, the integral gain KI, and the derivative gain KD, are offline calculated based on the dynamics of the process under control using the Zeigler Nichols method: in this controller the three gains remains fixed during the entire process control. The second controller uses three type-1 fuzzy logic systems to estimate each one of the gains of the professional PID controller every control cycle; each fuzzy rule base is offline estimated by the expert and remains fixed during the complete control process. This paper proposes a fuzzy self-tuning professional PID controller: it has three singleton type-1 fuzzy logic systems to calculate each gain of the controller every control cycle, with the novel characteristics that each fuzzy rule base is updated and tuned each feedback cycle using the back-propagation BP algorithm. This proposal is named the fuzzy professional proportional-integral-derivative controller (T1 SFLS P-PID) with back-propagation (BP) tuning. The experiments show that the proposed fuzzy self-tuning controller has better transient performance compared with the two benchmarking controllers. It shows the minimum overshoot and the minimum response time.

Keywords PID control, singleton type-1 fuzzy logic systems, back propagation algorithm.

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