Abstract
In this work, the design and analysis of a Neural Adaptive Super-Twisting Sliding Mode Controller is presented. Radial Basis Function Neural Network (RBFNN) is used to estimate the model uncertainties and an adaptive super-twisting sliding mode controller is designed to provide robustness against the external disturbances. The stability of the proposed controller is proven and the adaptive laws are derived from the Lyapunov stability condition. The performance of the proposed controller is compared with an existing work using a numerical example and as a case study the controller is applied to the problem of slung load carrying using an aerial vehicle and the corresponding simulation results are presented.
Published Version
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