Analysis of Self Tuning Fuzzy PID Internal Model Control

Abstract

In this thesis internal model control and fuzzy self-tuning PID controller is combined into a whole controller which make up a new controller fuzzy self-tuning PID internal model controller. First the internal model control system can be changed into conventional PID unity feedback control system through introducing pade series to approximate the time delay unit. Then using fuzzy inference to tune the PID parameters online, the fuzzy selftuning PID internal model controller is realized. This controller combines the advantage of fuzzy control, internal model control and PID control. In short, fuzzy control is used to overcome the uncertainty of mathematical model of the real plant, Internal model control is used to resolve the large time-delay of real control system, PID control is used to improve the static and dynamic performance of control system. Fuzzy logic is used to tune each parameter of PID controller. Through simulation in Matlab by selecting appropriate fuzzy rules are designed to tune the parameters Kp, Ki and Kd of the PID controller. A set of rules which define the relation between the input and output of fuzzy controller are used to designing the system. The self tuning rule is deferent according to different e, ec, kp, ki and kd , where ‘e’ is error form set point and ‘ec’ is rate of change of error. These rules are defined using the linguistic variables. The two inputs, error and rate of change in error, result in 49 rules. For designing the controller, FIS system used are of Mamdani type rule-base model. This produces output in fuzzified form. Normal system need to produce precise output which uses a defuzzification process to convert the inferred possibility distribution of an output variable to a representative precise value. In the given fuzzy inference system this work is done using centroid defuzzification principle. In this min implication together with the max aggregation operator is used. Simulation experiments have been done to assumed model of a control system. Simulation results show that system adopted fuzzy self-tuning PID internal model control has smaller steady error, shorter adjusting time, smaller overshoot, faster rising time and comparatively strong robustness.