Tensor Product-Based Model Transformation Technique Applied to Servo Systems Modeling

This paper presents the design and validation of a Tensor Product (TP)-based model of a family of nonlinear servo systems using an appropriate technique. Two parameters of the first principles state-space model of the servo system are optimally tuned using a metaheuristic Grey Wolf Optimizer algorithm in terms of several runs that lead to the parameter intervals. The derivation of the TP model starts with the linear parameter varying model of the servo system, which is next transformed to the strictly speaking TP model, inserted in a series connection with the servo system nonlinearity. The behaviors of the servo system, the TP model and the first principles model are tested in a different scenario to the parameter identification one, and the outputs are measured. The experimental results on a servo system laboratory equipment show that the TP model derived for this system ensures good performance in terms of small relative modeling errors.