State and Parameter Estimation for Affine Nonlinear Systems

This paper proposes a new approach to online state and parameter estimation for affine nonlinear systems. Unlike conventional methods limited to specific classes of nonlinear systems and reliant on stringent excitation conditions, the proposed approach uses multiplier matrices and a data-driven concurrent learning method to develop an adaptive observer for affine nonlinear systems. Through rigorous Lyapunov-based analysis, the technique is proven to guarantee locally uniformly ultimately bounded state estimates and ultimately bounded parameter estimation errors. Additionally, under certain excitation conditions, the parameter estimation error is guaranteed to converge to a given neighborhood of the origin.