Abstract
This chapter aims to describe the development and two tuning methods for a self-organising fuzzy PID controller. Before application of fuzzy logic, the PID gains are tuned by conventional tuning methods. In the first tuning method, fuzzy logic at the supervisory level readjusts the three PID gains during the system operation. In the second tuning method fuzzy logic only readjusts the values of the proportional PID gain, and the corresponding integral and derivative gains are readjusted using Ziegler-Nichols tuning method while the system is in operation. For the compositional rule of inferences in the fuzzy PID and the self-organising fuzzy PID schemes two new approaches are introduced: the Min implication function with the Mean-of-Maxima defuzzification method, and the Max-product implication function with the Centre-of-Gravity defuzzification method. The self-organising fuzzy PID controller, the fuzzy PID controller and the PID controller are all applied to a non-linear revolute-joint robot-arm for step input and path tracking experiments using computer simulation. For the step input and path tracking experiments, the novel self-organising fuzzy PID controller produces a better output response than the fuzzy PID controller; and in turn both controllers produce better process output that the PID controller.
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Kazemian, H.B. (2008). Intelligent Fuzzy PID Controller. In: Lowen, R., Verschoren, A. (eds) Foundations of Generic Optimization. Mathematical Modelling: Theory and Applications, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6668-9_7
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