A new model based on Colliding Bodies Optimization for identification of Hammerstein plant

A Hammerstein plant consist of a nonlinear static part in series with a linear dynamic block. Identification of such complex plant finds enormous applications in stability analysis and control design. In this paper a new model to identify the Hammerstein plant is proposed based on a recently developed meta-heuristic algorithm Colliding Bodies Optimization (CBO). The CBO is based on the collision between bodies, each of which has a specific mass and velocity. The collision leads to move the bodies towards better positions in the search space with new velocities. The performance of the proposed CBO model is compared with two other meta-heuristics models based on Bacterial Foraging Optimization (BFO) and Adaptive Particle Swarm Optimization(APSO). The results demonstrate the superior performance of the new model terms of better response matching, accurate identification of system parameters and reasonable convergence speed achieved.