Application of Biogeography based optimization in tuning a PID controller for nonlinear systems

Abstract

This paper is dedicated to present the newly developed evolutionary algorithm: Biogeography based optimization (BBO). It is based on the migration of information between habitats like in Biogeography. The BBO is then used to tune a PID controller of nonlinear systems where the parameters are optimized. Simulations of the proposed algorithm are carried out over an inverted pendulum and second on mass-spring damper system. Performances of the BBO are compared to those of genetic algorithm in PID tuning problem and the BBO gives acceptable results even best then GA.

Nonlinear inverted pendulum PID control by an improved Artificial Bees Colony-Predator and prey approach

“…Indeed, the predator and prey model allows us to avoid local optima and explore new sources. An application of the ABC-PP algorithm to tune a PID controller for a nonlinear inverted pendulum is presented and its performances are compared to those of genetic algorithm [7]. M …”

“…They involve three gains to be adjusted [1] with Trial / Error in case of nonlinear systems. Adjusting these parameters is considered as an optimization problem which has been solved by evolutionary algorithms (EAs), including genetic algorithms [2], [3], ant colony optimization [4], particle swarm optimization [5], [6] and biogeography based optimization (BBO) [7].…”

Predator and Prey Modified Biogeography Based Optimization Approach (PMBBO) in Tuning a PID Controller for Nonlinear Systems

“…A comparison of the performances of our approach with those of BBO and GA is done [14] and a study of the influence of hunting rate of P&P behavior on method performance is presented.…”

“…The whole BBO algorith m could be explained as follows [14]:  Step 1: The BBO starts by initializing the algorithm parameters: the SIV's number n siv and ranges, maximu m species number, termination criterion (iterat ion number or other performance criterion), maximu m immigration and emigrat ion rates E and I, mutation coefficient and define the appropriate HSI, then the start population islands are generated randomly [20].  Step 2: Evaluate each island in the population, get its HSI value and map it to obtain the species count $s$.…”

“…The PID control involves three gains to be determined Proportional, Integral and Derivative [8]. Adjusting the PID parameters is considered as an optimization problem which has been solved by evolutionary algorith ms (EA s), including genetic algorith ms [9,10], ant colony optimization [11], particle swarm optimization [12,13] and biogeography based optimization (BBO) [14].…”