DSP-Based Laboratory Implementation of Hybrid Fuzzy-PID Controller Using Genetic Optimization for High-Performance Motor Drives

This paper presents a real-time implementation of a genetic-based hybrid fuzzy-proportional-integral-derivative (PID) controller for industrial motor drives. Both the design of fuzzy-PID (FPID) controller and its integration with the conventional PID in global control system to produce a hybrid design are demonstrated. A genetic optimization technique is used to determine the optimal values of the scaling factors of the output variables of the FPID controller. The objective is to utilize the best attributes of the PID and FPID controllers to provide a controller which will produce better response than either the PID or FPID controller. The principle of the hybrid controller is to use a PID controller, which performs satisfactorily in most cases, while keeping in the background a FPID controller, which is ready to take over the PID controller when severe disturbances occur. The hybrid controller is formulated and implemented in real time, using the speed control of a brushless drive system as a testbed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital-signal-processor-based real-time data acquisition control system and MATLAB/Simulink environment. Experimental results show that the proposed FPID controller-based genetic optimization produces better control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.