An efficient identification approach for stable and unstable nonlinear systems using Colliding Bodies Optimization algorithm

- CBO based efficient and accurate identification scheme has been applied to different classes of nonlinear plant models.
- Different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart have been considered.
- The output MSE has been considered here as the fitness function to be minimized to get the optimal parameter vector.
- The sustainability and robustness of the CBO algorithm is evaluated in the presence of noise in every system.
- Parametric t-test and non-parametric Mann-Whitney-U test have been conducted the consistency of performance of the CBO algorithm.

This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme.