Comparative Analysis of Control of Inverted Pendulum using Conventional and Fractional PID Controllers

Abstract

Fractional PID Controller has proved its mettle in last few decades and has been used in various applications to improve the performance and embeds some intelligence into the system. Conventional PID Controller also solves the problem of non linear systems and handles them with great efficiency and provides robustness to the system. However our aim always lies in achieving the optimal solution and there are different methods to find out optimal solution but Fractional PID Controller has emerged as a very fast and good method for optimal solution of many problems. The inverted pendulum problem is a classical problem of the non-linear control system. It is a highly non linear system. Such type of problem needs very precise and robust control. We will use PID controller and fractional PID controller for this purpose. The simulation of PID controller is done with MATLAB simulation tool. The Crone controller gives output in the incremental form (increment in controller’s parameters). The overshoot and the error, both play crucial role in the stability of Inverted Pendulum. Here in this case, the overshoot increases when error decreases and vice-versa. So crone controller has been used to find out optimal solution. The objective of the present work is to use new type of a Fractional-order controller (CRONE) which also shows better performance when used for the control of fractional-order system than the conventional PID-controller. Various steps in the implementation of Fractional PID controller Parameter like proportion gain Kp, Integral gain Ki, Derivative gain Kd etc. are realized with MATLAB Simulation. Crone controller has been used for robust over-ride control of the fraction controller. The modeling and simulation of the fractional order controller is done in MATLAB Simulation for inverted pendulum problem. With the advanced numerical techniques and computation facilities, the scientists have adopted more realistic methods for analysis controller characteristic for optimization. The realization of above work can also be done with the help of many other soft computing methods like Artificial Intelligence, Genetic Programming, Neural Networks and hybrid use of these methods