The Application of Stochastic Optimization Algorithms to the Design of a Fractional-order PID Controller

TL;DR

This paper introduces a novel method for designing fractional-order PID controllers using stochastic optimization algorithms, demonstrating improved performance over traditional integer-order controllers through comparative analysis.

Contribution

It proposes a new synthesis technique for fractional-order PID controllers based on peak overshoot and rise-time specifications, optimized via Particle Swarm Optimization and Differential Evolution.

Findings

Fractional-order PID controllers outperform integer-order ones in the example.

Particle Swarm Optimization and Differential Evolution effectively solve the design problem.

The proposed method achieves better control specifications than traditional approaches.

Abstract

The Proportional-Integral-Derivative Controller is widely used in industries for process control applications. Fractional-order PID controllers are known to outperform their integer-order counterparts. In this paper, we propose a new technique of fractional-order PID controller synthesis based on peak overshoot and rise-time specifications. Our approach is to construct an objective function, the optimization of which yields a possible solution to the design problem. This objective function is optimized using two popular bio-inspired stochastic search algorithms, namely Particle Swarm Optimization and Differential Evolution. With the help of a suitable example, the superiority of the designed fractional-order PID controller to an integer-order PID controller is affirmed and a comparative study of the efficacy of the two above algorithms in solving the optimization problem is also presented.