The purpose of this study is to investigate the application of different system identification techniques such as least square (LS), recursive least square (RLS) and neural network (NN) to identify the system model of a flexible manipulator structure and design a Proportional-Integral-Derivative (PID) controller for the system to control the angular motion and suppress the end-point vibration. The input-output data for the system identification usage is acquired through the experimental setup of a lab scale experimental rig. After the system is identified using the system identification techniques, the result is verifies using mean square error (MSE). All the results are compared which the NN system identification with NAR model has the smallest MSE value of 1.481×10-04 and RLS system has the smallest MSE value of 1.690×10-04. The transfer function obtained by using RLS and NN are used to develop the control scheme to suppress the vibration and control the angular motion of the flexible manipulator structure. PID controller is proposed to be used in the flexible manipulator system. The controller was tuned heuristically and automatically in Matlab SIMULINK environment. The results show that the PID controller developed with parametric model is better in suppressing the vibration while the PID controller developed with non-parametric model is better in controlling the angular motion of the flexible manipulator
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