Parameter identification based on prescribed estimation error performance for extended Wiener–Hammerstein systems

Most identification algorithms do not consider the prescribed error bound on the parameter estimation error information, which may result in the poor transient performance of parameter estimation during the identification process. In this study, a novel identification scheme is presented for the extended Wiener–Hammerstein systems with backlash non-linearity, which is implemented by using prescribed performance function and error transformation technique. To improve the transient performance of parameter estimation, the prescribed performance function is designed to prescribe estimation error bound. Then, the error transformation technique is applied to transform the prescribed estimation error problem into an equivalent generalised error problem, which can simplify the design of identification algorithm. By developing the parameter updating law, the convergence of generalised error problem can be guaranteed, and the parameter estimation of considered system can be achieved. The numerical example and experiment results validate that the proposed scheme can provide more accurate parameter estimation and better transient performance than the available identification approaches.