Tuning of a PID controller Using a Multi-objective Optimization Technique Applied to A Neutralization Plant

Most control engineering problems are characterized by several, often contradicting, objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion, and using Pareto-optimal solutions. Here we propose a Genetic Algorithm (GA) approach using a combination of both methods to find a fixed-gain, discrete-time PID controller for a chemical neutralization plant. Known to be highly non-linear and with varying time delay, this plant provides a challenging testbed for nonlinear control strategies. Experimental results confirm that a multi-objective, Pareto-based GA search gives a better performance than a single objective GA. The former method was also used to design a gain-scheduled PID controller, for which also experimental results are shown.