Abstract:
Genetic algorithms, one of the optimization techniques, is applied in the self-tuning PID controller in this thesis. Testing the program with the test case optimization problem, Rosenbrock's function, is carried out to validate the algorithms. Then the proposed controller is tested with the process that is relevance to the thesis. The results show that genetic algorithms can be applied to self-tuning PID controller because the proposed self-tuning PID controller gives better control performances over the conventional PID controller which is tuned conform to the Ziegler-Nichols method. The processes, second order process plus dead time and pH neutralization process that takes place in the continuous stirred tank reactor are used to test performance of the proposed controller. The controller are evaluated in the face of disturbances namely, inlet acid flow rate and inlet acid concentration. The genetic algorithms optimization part adjusts the parameters of the self-tuning controller to the proper values