A Novel Optimization Algorithm for the Estimation of ARMAX Models

This paper introduces a hybrid optimization algorithm, followed by a corresponding estimation technique, for the estimation of ARMAX models. The hybrid algorithm consists of a stochastic component and a deterministic counterpart and aims at combining high convergence rate together with reliability in the search for global optimum. The estimation procedure is slit in two phases, due to the mixed linear-nonlinear relationship between the residuals and the parameter vector, and results in stable and invertible models. The proposed methodology is implemented in the estimation of a half-car suspension model of a road vehicle, using noise-corrupted observations, and the results yield very stable performance of the hybrid algorithm, reduced computational cost, in comparison to conventional stochastic optimization algorithms, and ability to describe satisfactory system’s dynamics.