Over 60 years, the proportional-integral-derivative(PID) controller has been especially popular in industrial processes, such as chemical, petroleum, power and manufacturing industries due to its simple structure and robustness. Since the performance of the PID controller depends highly on its three parameters, the proper tuning of the parameters is required to guarantee acceptable control performance. Therefore, a number of tuning methods, such as the Ziegler-Nichols methods, the Cohen-Coon method, and the IMC method have been proposed. These conventional tuning methods are based on experience and experiment.
In this thesis, a method for obtaining model-based tuning rules for the PID controller are proposed incorporating with real-coded genetic algorithms. First, the optimal parameter sets for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controller, performance indices(IAE, ISE and ITAE) are adopted. Then, tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm.
A set of simulation works are carried out to verify the effectiveness of the proposed rules.