Jib system control of industrial robotic three degree of freedom crane using a hybrid controller

Abstract

Cranes are used to carry loads effectively. During movement, often undesired fluctuations of lifted payload occur, which needs to be controlled. Control is the basic requirement for desired operation of crane. Objective is to control the trolley position and swing angle of payload. The continual flow requires an effective control methodology to achieve a high positioning control of the trolley carrying payload and suppression of swing angle of payload during operation. Optimal control techniques can be used to control these undesired vibrations. These techniques result in some undesired overshoot and undershoot causing the payload to swing prior to system getting stable. However if these techniques are combined with intelligent control techniques then a more stable system can be obtained. In this paper a hybrid controller called neuro-optimal controller has been used to control the swing angle of lifted payload by controlling the trolley position.The proposed technique of using a hybrid controller has stabilized the system by reducing the overshoot, undershoot and settling time. The proposed technique is very useful in many industrial applications. Experimental analysis can further provide the insight and limitations of the proposed techniques. Keywords: Artificial Neural Network (ANN), Algebraic Riccati Equation (ARE), Back Propagation (BP), Linear Quadratic Regulator Controller (LQR), Neural Network Predictive Controller (NNPC), 3 Degree of Freedom (3DOF)