An Auxiliary Model-Based Multi-Innovation Recursive Least Squares Algorithm for Identifying the Electrically Stimulated Muscle Model

Electrical muscle stimulation has extensive applications in the field of rehabilitation medicine, and the reasonable and effective modeling and identification of electrical muscle stimulation hold significant importance. This paper regards the electrical muscle stimulation model as a controlled autoregressive system with a nonlinear input module and employs an auxiliary model-based recursive least squares algorithm to achieve parameter estimation. The introduction of multi-innovation theory further enhances the utilization of data and the precision of the algorithm's results. A numerical model is utilized for simulation, demonstrating the effectiveness of the proposed algorithm.