Evolutionary modeling of industrial plants and design of PID controllers: Case studies and practical applications

Abstract

This chapter brings forth the practical aspect of using genetic algorithms (GAs) in aiding PID (Proportional-Integral-Derivative) control design for real world industrial processes. Plants such water tanks, heaters, fans and motors are usually hard to tune on-site, especially after prolonged use of the equipment when degradation of performances is inevitable, while plants like seismic dampers have inherent nonlinear behaviors that make formal controller design difficult at best. Therefore, this chapter introduces a series of practical steps that can be taken by control engineers in order to (re)design viable PID controllers for their plants. This chapter describes how genetic algorithms can be applied to problems in control systems and model identification. Considering the plant inputs and outputs that can be observed during functioning, we offer a quick method for identifying model parameters, which can be used later by the genetic algorithm to find a suitable controller. While, in formal control theory, a raw estimation of the model parameters can significantly reduce the performance of a real-world system, the genetic algorithm method can find suitable controllers quickly and efficiently, offering access even to performance criteria that is hard to quantify in classical design procedure, such as integral indexes. The applicability of the GAs in real world problems is outlined through case studies that take into account the particularities of each system, from first to second order responses, the absence or presence of time delay, nonlinearities, constraints and controller performance. The steps performed in the case studies show how GAs have made the jump from their origins to a practicing engineer’s toolbox (GAOT-ECM in this case, a Genetic Algorithm Optimization Toolbox Extension for Control and Modeling). Moreover, a comprehensive analysis is performed, that takes into account both the various performance criteria, and the tuning parameters of the genetic algorithm, over the obtained models and controllers. The influence of the GA parameters is discussed in order to help practitioners choose the best suited GA configuration for their particular problem. In all, this chapter offers a comprehensive step-by-step application of genetic algorithms in industrial setting, from plant modeling to controller design.