Parameter estimation method for separable fractional-order Hammerstein nonlinear systems based on the on-line measurements

This paper investigates the problem of parameter estimation for fractional-order Hammerstein nonlinear systems. To handle the identification difficulty of the parameters of the system and the order, the maximum likelihood and hierarchical identification principles are combined to derive a maximum likelihood gradient-based iterative algorithm. Moreover, to achieve the higher estimation accuracy, the multi-innovation identification theory is introduced, based on which the residual can be formulated as a linear combination of the innovation. Then, a multi-innovation maximum likelihood gradient-based iterative algorithm is proposed, which further improves the innovation utilization. Meanwhile, the computational cost of the proposed algorithm is assessed through the use of flops, which is less than those of its peers. Finally, the convergence analysis and simulation examples demonstrate the efficacy and robustness of the proposed algorithms.