Kernel-based continuous-time identification of Hammerstein models: Application to the case of ankle joint stiffness dynamics

This paper deals with a new practical approach to continuous-time parametric identification for dynamic models in Hammerstein form and its application in the context of estimating the dynamics of joint stiffness. The proposed methodology deals the static non-linearities as a linear combination of nonlinear basis functions, while the linear dynamic parts of the system are modeled by a parametric rational transfer function. The proposed identification method is enabled by the algebra of Volterra linear integral operators by a suitable design of kernels admitting finite-dimensional and internally stable state-space realizations. Simulation results show the effectiveness of the proposed continuous-time identification technique.