Genetic Algorithm Based Improved Sub-Optimal Model Reduction in Nyquist Plane for Optimal Tuning Rule Extraction of PID and PI{\lambda}D{\mu} Controllers via Genetic Programming

TL;DR

This paper introduces a genetic algorithm-based method for improved model reduction and optimal tuning of PID and fractional order controllers using Nyquist criteria, outperforming traditional techniques.

Contribution

It presents a novel Nyquist-based sub-optimal model reduction technique combined with genetic programming for optimal controller tuning, enhancing control performance and reducing model complexity.

Findings

Outperforms conventional H2-norm based model reduction methods.

Generates efficient tuning rules from Pareto optimal front.

Achieves better control performance with minimal control signal.

Abstract

Genetic Algorithm (GA) has been used in this paper for a new Nyquist based sub-optimal model reduction and optimal time domain tuning of PID and fractional order (FO) PI{\lambda}D{\mu} controllers. Comparative studies show that the new model reduction technique outperforms the conventional H2-norm based reduced order modeling techniques. Optimum tuning rule has been developed next with a test-bench of higher order processes via Genetic Programming (GP) with minimum value of weighted integral error index and control signal. From the Pareto optimal front which is a trade-off between the complexity of the formulae and control performance, an efficient set of tuning rules has been generated for time domain optimal PID and PI{\lambda}D{\mu} controllers.