Related Experiment Video
Updated: Jun 11, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
Published on: March 10, 2011
Tuning fuzzy PD and PI controllers using reinforcement learning
Hamid Boubertakh1, Mohamed Tadjine, Pierre-Yves Glorennec
1LAMEL, University of Jijel, BP. 98, Ouled Aissa, 18000, Jijel, Algeria. boubert_hamid@yahoo.com
Abstract:
In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi-Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen's implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.
Related Concept Videos
Time and frequency -Domain Interpretation of PI Control
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires careful...
PD Controller: Design
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
PI Controller: Design
Time-Domain Interpretation of PD Control
Consider the example of control of motor torque. Initially, a positive...
PID Controller
Phase-lead and Phase-lag Controllers