Improving PID controller of motor shaft angular position by using genetic algorithm

This study represents Genetic Algorithm optimization of PID parameters gain in model reference robust control system structure for desired position of incremental servomotor. Experiments had been took out via Lab-Volt 8063 Digital Servo system equipment at Servo Control Laboratory. The key issue for PID controllers is the accurate and efficient tuning of parameters. The plant repeatedly has a problem in achieving the desire position control and system performance have an oscillatory response and gives a slightly steady state error. This problem among other is affected by existing the nonlinearities component in the system, the system communication noise, and not optimize PID parameter. The existing PID controller tuning with the help of the offline Genetic Algorithms approach comprises of automatically obtaining the best possible outcome for the three parameters gain (Kp, Ki, Kd) for improving the steady state characteristics and performance indices. Their step responses are then compared with a tuned conventional Ziegler-Nichols based PID controller. This paper explores the well established methodologies of the literature to realize the workability and applicability of Genetic Algorithms for process control applications. At last, a comparative study done between ZN-PID experiment and GA-PID experiment shows that the GA optimal controller is highly effective and outperforms the PID controller in achieving an enhancing the output transient response with improvement percentage of rise time is 91.83%, settling time is 89.36% and maximum overshoot is 82.24%. The robust and automatic gains parameter calculator; GA based PID technique also proven to be time savers as they are much faster to be conducted than ZN method which is basically based on trial-and-error in getting the best PID values before the system can be narrowed down in getting the closest to the optimized value.

Name dc:type.qualificationname

mphil

Level dc:type.qualificationlevel

masters

Grantor dc:publisher.institution

Universiti Tun Hussein Onn Malaysia

Year dc:date.issued

2015

Author dc:creator

• Muhamad, Arif Abidin

• TJ212-225 Control engineering systems. Automatic machinery (General)

Language dc:language

en