Genetic Algorithm Optimisation of PID Controllers for a Multivariable Process

Abstract

This project is about the design of PID controllers and the improvement of outputs in multivariable processes. The optimisation of PID controller for the Shell oil process is presented in this paper, using Genetic Algorithms (GAs). Genetic Algorithms (GAs) are used to automatically tune PID controllers according to given specifications. They use an objective function, which is specially formulated and measures the performance of controller in terms of time-domain bounds on the responses of closed-loop process.

A specific objective function is suggested that allows the designer for a single-input, single-output (SISO) process to explicitly specify the process performance specifications associated with the given problem in terms of time-domain bounds, then experimentally evaluate the closed-loop responses. This is investigated using a simple two-term parametric PID controller tuning problem. The results are then analysed and compared with those obtained using a number of popular conventional controller tuning methods. The intention is to demonstrate that the proposed objective function is inherently capable of accurately quantifying complex performance specifications in the time domain. This is something that cannot normally be employed in conventional controller design or tuning methods.

Finally, the recommended objective function will be used to examine the control problems of Multi-Input-Multi-Output (MIMO) processes, and the results will be presented in order to determine the efficiency of the suggested control system.