Self-tuning PD+I fuzzy logic controller with minimum number of rules

Abstract

A novel 9 rules self-tuning PD+I fuzzy logic controller applicable for a class of nonlinear plants is proposed in this paper. The controller comprises of three separate fuzzy logic controllers with each uses minimum number of rules and the output scaling factor is tuned automatically depending on the tracking error dynamic conditions. The controller is applied to a two-link revolute robot for the tracking control. Simulation results show that the robustness and tracking performance of the proposed controller is comparable to standard PD+I fuzzy logic controller at low and medium speed motions. However, the performance of the proposed new design far exceeds the standard design at high speed motions.

Optimisation and rule firing analysis in fuzzy logic based maximum power point tracking

“…The rule bases are modelled using first order Takagi-Sugeno fuzzy model. The output of each rule is the change in converter duty ratio evaluated using (8). The constants a, b, and c are to be determined for each output MF in the rule base.…”

“…The optimization parameters of one FLC are encoded as a particle swarm vector S i as shown in (9), where a i , b i , and c i are the output MF constants as expressed in (8). Uniformly partitioned input MFs with fixed input range are used in the study.…”

“…Previous works on automated tuning of fuzzy controllers have concentrated on either the tuning of input and output scaling factors [7] or employing adaptive techniques in designing self-tuning fuzzy logic controllers [8]. However, a large rule base is required in both cases.…”

Particle swarm metaheuristic applied to the optimization of a PID controller

“…They are also simple and easy to implement on only in industrial controllers but also through PLC (Programmable Logic Controller). There are several adjustment and design methodologies reported in the specialized literature for the optimal tuning of PID controllers [1], [2]. Controller tuning tables fail to meet the multiple performance demands of the controlled system posed by the design engineer.…”

Novel Fuzzy Logic Controllers with Self-Tuning Capability

“…From (3.1), (3.2) and (3.3), it is clear that the adjustment of u N lead to the alteration of the proportional, integral and derivative gain constant simultaneously. Some preliminary results of this design have been reported in [14].…”