Abstract
This paper investigates the development of a hybrid collocated neural network-based model predictive control (NN-MPC) and non-collocated proportional-integral-derivative (PID) controller designed for input tracking and vibration control of a two-link flexible robotic manipulator (FRM). The flexible manipulator was modelled using Lagrange and assumed mode method. System identification is performed on the manipulator and a neural network model of the FRM is explicitly learnt. The NNMPC controller is designed such that the explicit model identified is stable in the closed loop. The NNMPC controller is used for motion tracking and the PID for vibration control. The effect of payload variations on the performance of the proposed controller is studied. The proposed controller is tested in the MATLAB/Simulink environment and its performance is compared with hybrid PD/PID controllers. The proposed controller's ability to track a constant input and sine wave is also studied. Simulation results show the effectiveness and robustness of the proposed hybrid NNMPC/PID controller.
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