Intelligent controller modelling for steerable robotic bar using bio-inspired control synthesis

In any non-linear system, designing and testing the behaviour of a controller is a tedious and time consuming task. Especially balancing the robotic bars or needles under unstable situation, for example continually moving of human’s arm or body, to the specific position in medical robots is very important to get accurate information or action from patients. This paper proposes a new balancing control approach for unstable environment using a ball and beam system. The ball and beam system is one of a famous non-linear and unstable control system, wherein the control of a rolling ball at a desired position on the beam is difficult and providing a challenge to the robotic controller researchers. There are a number of control algorithms with varying parameters to find the most optimal solution resulting in stabilizing the ball and beam system. This paper investigates a particle swarm optimization algorithm (PSO) to automatically tune the gains of the PID controllers in the two feedback loops of the ball and beam control system. It involves the derivation of linearised mathematical modelling, designing of those controllers in order to be used with the linear controllers. Furthermore, The works followed simulation results from PSO tuning are quantitatively compared to ITAE method of PID tuning and a fuzzy-logic controller to reach a conclusion of the efficient of the proposed algorithm.