Adaptive Backstepping Control with Fast Parametric Convergence for a Class of Nonlinear Systems

The paper addresses the problem of performance improvement of adaptive backstepping control for a class of nonlinear system. Proposed solution to the problem involves an adaptation algorithm with regressor dynamically extended by a linear filter. On the one hand the dynamic extension accelerates the tuning of adjustable control due to the “memory” effect of the filter, but on the other hand it permits to obtain time derivatives of adjustable parameters used in virtual and actual controls up to required order. The actual control is free from overparameterization, does not contain tuning functions and completely compensates nonlinear dynamics and uncertainties of the plant. The efficiency of proposed solution is demonstrated via simulation.

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