Tuning PID Control: A Hybrid Approach using Fuzzy Logic and Genetic Programming

The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani method. However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this work, a method is proposed to optimal tuning the parameters of PID controllers. Different from traditional techniques, the tuning procedure of the proposed method is described in terms of fuzzy rules, in which the input variable is the error signal, and the output variables are the PID parameters. Genetic programming (GP) is then used to search for the optimal PID parameters that will minimize the integral of the squared error. The hybrid method to tune PID controllers was compared to the performance achieved by four classical PID tuning schemes that are widely used in industry. The simulations show that hybrid method always achieves a performance that is at least as good as those achieved of the classical PID tuning schemes, and often better: faster settling time and the minimal integral squared error. In addition, the parameters obtained through the four comparative methods, cannot always effectively control systems with changing parameters, and may need frequent on-line retuning, the controllers of the hybrid method are adapted on-line based on parameters estimation, requiring certain knowledge of the process.