Experimental design for identification of nonlinear systems with bounded uncertainties

This paper proposes an experimental design method for the identification of a class of nonlinear systems. The lumped uncertainty of the nonlinear system is assumed to be bounded by known bound. A closed-loop identification scheme is adopted for this system. Specifically, the problem of designing an optimal input that minimizes the worst-case identification error is converted to a constrained optimal trajectory planning problem. After this optimal desired trajectory is obtained, adaptive robust control (ARC) algorithm is utilized to design the control input such that the output of the system tracks the desired optimal trajectory as closely as possible. LSE is used to give an estimate of the unknown parameters based on the filtered input and output of the controlled plant after the input designed above is applied. Extensive experiments verify that the proposed identification method gives better results than the traditional open loop identification.