A Nature Inspired PID like Fuzzy Knowledge-Based Fractional-Order Controller for Optimization

Abstract

Conventional PID (CO-PID) controllers have dominated industrial process control applications. Though their use in industry is still prevalent, new avenues have emerged with the advent of soft computing tools. Several soft computing techniques for implementing conventional PID control have been proposed, e.g., cruise control using genetic algorithm (GA), conical tank regulation using ANT colony optimization (ACO), PID control using multi-objective ACO, automatic voltage regulator system (AVR) using particle swarm optimization (PSO), DC motor control using GA, evolutionary programing (EP) and PSO. The fract-order PID controllers have an advantage over conventional PID controllers in terms of availability of additional tuning parameters. In this research work, the authors propose what may be termed “optimized” tuning method for FFPID controllers using a combination of GA and ant colony techniques on a fuzzy logic platform. The research work attempts to design a controller for integer-order and fract-order plants by unifying nature inspired optimization techniques with proportional–integral–derivative (PID) like fuzzy knowledge-based control. The controller employs genetic algorithms (GA) and ANT colony algorithms for offline tuning of fract-order PID controller. Subsequently, fuzzy knowledge-based PID formulation fine-tunes the controller. The authors propose a modified GA-ANT approach wherein the inputs to the ANT system are generated in an optimal manner by using GA. They have simulated it on two distinct plants: (i) DC motor and (ii) a standard fract-order system. Simulation results and comparisons thereof show its superiority and feasibility for control of fract-order plants.