Adaptive inverse control using kernel identification

Kernel methods are exploited to implement an adaptive inverse control scheme of which a first introductory presentation is given. The resulting controller has faster convergence than the solutions proposed in literature utilizing Support Vector Machines (SVMs) [1] and Artificial Neural Networks (ANNs) [2]. Smaller residual errors are obtained for trajectory tracking. Simulations are carried out for different scenarios.