Self-Balancing and Position Control of a Balancer System Using a Pattern-Based Intelligent Optimization Method

Abstract

This paper presents a new Data-Driven Teaching Learning-Based Optimization (DDTLBO) method used to improve the self-balancing and position control of a two-degree of freedom ball and balancer (2DOFBB) system. DDTLBO is a modified version of teaching learning-based optimization (TLBO) method, based on the natural pattern followed by “learners” in an academic class to improve their knowledge. In this, an intelligent algorithm is used on MATLAB platform to handle the nonlinearities and un-stability of the ball and balancer system. A ball-balancer is a highly nonlinear, electromechanical, multivariable and underactuated system. A sophisticated automated intelligent control approach is used to handle these complications. In the proposed approach, the pattern followed by inclination angle of balancer plate is traced after observing the control dynamics of 2DOFBB system. Modeling of ball balancer system is accomplished with the help of proportional integral and derivative (PID) controller. Further, an intelligent data-driven computational approach is implemented, which improves the execution of TLBO optimization method. This improved optimization is used to tune the performance of proportional integral derivative controller employed on the ball balancer system. This approach addresses the major challenge of unknown disturbances on the closed loop system of the considered problem. The dominance of proposed intelligent approach is verified over other optimization techniques using benchmark CEC functions. The optimized parameters of PID controllers hence obtained are implemented on 2DOFBB system. Results obtained are validated using real-time response analysis on the MATLAB/Simulation platform, which demonstrates the dominance of DDTLBO-PID controller over traditional controllers.