Optimal Design of Lead Compensator Using Nature-Inspired Algorithms

There are numerous algorithms for designing lead compensators, some of which are graphical whereas others are analytical. When designing a lead compensator, the parameters of the compensator are considered as an optimization problem which aims at getting the required time and frequency specifications. This paper presents a comparison between lead compensators designed by nature-inspired algorithms against those designed by conventional algorithms for various types of systems. The nature-inspired algorithms considered in this paper are the genetic algorithm (GA), which is built on the concept of natural selection process which imitates biological evolution, and the particle swarm optimization (PSO), which is stimulated by social behavior of fish schooling or bird flocking. In this paper, two different examples are considered to demonstrate the comparison between the design methods. The simulation results of these examples show that the nature-inspired algorithms provided better transient response due to reduced settling and rise times and provided better relative stability due to zero overshoot and higher phase margin.