ANFIS multi-tasking algorithm implementation scheme for ball-on-plate system stabilization

Abstract

This paper presents the design and realization of a ball-on-plate system using a 3-degree-of-freedom parallel robot controlled by an adaptive neuro-fuzzy inference system. The ball-on-plate system is nonlinear, multivariable, with an under-actuated feature. Initially, the parallel robot is designed using SolidWorks and mechanized using a computer numerical control machine. Followed by the presentation of the ball-on-plate system mathematical model and the simplified model obtained. Afterwards, the inverse kinematics are performed to derive the appropriate angle for each servomotor. Eventually, the controller is designed and implemented in a double loop feedback scheme. A comparison between the proposed controller and a conventional proportional– integral–derivative controller in terms of time response, overshoot, and steady-state error is carried out. Furthermore, a comparison between sequential and asynchronous parallel processing is conducted for two different scenarios. The first scenario is when moving the ball to the origin while the second is for disturbance rejection. Simulation and experimental results show that the adaptive neuro-fuzzy inference system implemented using asynchronous parallel processing improves the real-time system stability by considerably decreasing oscillations as well as enhancing the ball movement smoothness with a small stead-state error.