Design of an optimal fractional fuzzy gain-scheduled Smith Predictor for a time-delay process with experimental application.

This study addresses an experimental investigation of a novel modified Smith Predictor (SP) based fractional fuzzy gain-scheduled control scheme in control of a time-delayed thermal process. The control strategy employees a fuzzy algorithm to adjust convenient controller parameters based on the system's operating conditions. Performance enhancement of the closed-loop system enables more robust behavior in the presence of disturbance while reducing energy consumption by producing a smooth control signal in comparison with the traditional integer order SP structures. The proposed controller comprises self-tuning capabilities at runtime which makes it adaptive in nature. The motivation of the present paper is in both points of theory and experimental application. The theoretical contribution is to propose a new Smith Predictor based fractional order fuzzy dead-time compensation scheme that can handle uncertainties, parameter variations, and internal/external disturbances. The practical contribution is to apply the proposed control scheme to a real-time air-heating process. The performances of the elaborated control strategies are investigated in both computer simulation and experimental application under different operating conditions. The proposed fractional fuzzy control scheme is found superior to the classical PI-PD SP and integer fuzzy controllers for temperature profile tracking tasks. Moreover, complementary comments are highlighted on the advantages and drawbacks of each controller.