Hybrid Iterative Learning Control of a Flexible Manipulator

sacrificed, efficient controls have to be studied and developed. Feedback control techniques use measurement and estimates of the system states for control of rigid-body motion and vibration suppression. In this case, using measurements at the hub and end-point of the manipulator as the basis for applying control torque at the hub will allow control of the end-point position. Thus, feedback control can be divided into collocated and non-collocated schemes. An important aspect of the flexible manipulator control that has received little attention is the interaction between the rigid and flexible dynamics of the links. An acceptable system performance with reduced vibration that accounts for system changes can be achieved by developing a hybrid control scheme that caters for rigid body motion and vibration of the system independently. Previously, a hybrid collocated and non-collocated controller has been proposed [1]. This utilizes end-point acceleration feedback through a proportional-integralderivative (PID) control scheme for vibration reduction and hub angle and hub velocity feedback through a proportional-derivative (PD) control scheme for rigidbody motion control. Experimental works have shown that this control structure performs better in respect of vibration reduction than a collocated controller. This paper presents an investigation into the development of a hybrid control scheme with iterative learning for input tracking and end-point vibration suppression of a flexible manipulator system. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller using hub angle and hub velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate a non-collocated proportional-integral-derivative (PID) controller with iterative learning for control of vibration of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid iterative learning control scheme is assessed in terms of input tracking and level of vibration reduction in comparison to a conventionally designed PD-PID control scheme. The effectiveness of the control scheme in handling various payloads is also studied.