Parametric Modelling of a TRMS Using Dynamic Spread Factor Particle Swarm Optimisation

Abstract

System identification in vibrating environments has been a matter of concern for researchers in many disciplines of science and engineering. In this paper, a sound approach for a Twin Rotor Multi-input Multi-Output System (TRMS) parametric modeling is proposed based on dynamic spread factor particle swarm optimization. Particle swarm optimization (PSO) is demonstrated as an efficient global search method for nonlinear complex systems without any a priory knowledge of the system structure. The proposed method formulates a modified inertia weight algorithm by using a dynamic spread factor (SF). The inertia weight plays an important role in terms of balancing both the global and local search. Thus, the usage of dynamic SF is proved experimentally to satisfy main issues of using basic PSO that are trapped in local optima and preservation of diversity. Results in both time and frequency domains portray a very good parametric model that mimic well the behavior of a TRMS. Validation tests clearly show the effectiveness of the algorithm considered in this work.