Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—In this paper, the mathematical interpretation of VOR is derived and a VOR adaptation method is newly proposed. This study of cerebellar inspired adaptive control, which is augmented by the reference model, provides a potential general solution for robotic control. If the reference model is not used, the plant with more poles than zeroes finds it difficult to give a response immediately. The reference model determines the behaviour of the robot. The response of it at high frequency with small-time constant shows the roll-off, low frequency signals not affected. To verify the proposed method of neural control for image stabilisation using a reference model, first, it is briefly introduced and then it is utilized for Matlab and Simulink simulation and experiments. The described cerebellar algorithm has the potential to provide a modular controller for soft robots. Also, it can be applied to generic control tasks, as the force control, or impedance response, or task calibration.
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