Red-FLC: an Adaptive Fuzzy Logic Controller with Reduced Learning Parameters

In this paper, an adaptive Takagi-Sugeno (TS)-fuzzy controller is developed for nonlinear dynamical systems, where a new structure of the controller with reduced learning parameters is proposed. The proposed controller is named as a reduced learning parameter based fuzzy logic controller (Red-FLC). Being a model-free controller, the classical TS-fuzzy one performs well in slow-process control-based complex applications. However, the controller’s structure is associated with several antecedent and consequent parameters, which need to be adapted during control operation. Adaptation of a high number of parameters is computationally expensive, especially in controlling a system where a fast response is expected. From this research gap, in our developed adaptive fuzzy controller, the tuning parameters have reduced significantly since it has no antecedent parameters. The closed-loop stability of the controller has been proved using a new adaptation law. To evaluate the proposed controller’s performance, it has been utilized to stabilize an inverted pendulum’s simulated plant on a cart by considering an impulse disturbance. The performance of Red-FLC has been compared with a classical TS-fuzzy controller and a Proportional Integral Derivative (PID) controller, where better tracking of the cart’s position and better disturbance rejection is observed from the proposed TS-fuzzy controller.