System Identification and Model Validation of Recursive Least Squares Algorithm for Box–Jenkins Systems

In this paper, a new-type recursive least squares algorithm is proposed for identifying the system model parameters and the noise model parameters of Box–Jenkins Systems. The basic idea is based on replacing the unmeasurable variables in the information vectors with their estimates. The proposed algorithm has high computational efficiency because the dimensions of the involved covariance matrices in each subsystem become small. Validation of the model is evaluated using some statistical methods, Which, best-fit criterion and Histogram. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

Adaptive Backstepping Controller of PMLSM

In this paper, a nonlinear adaptive speed controller for permanent magnet linear synchronous motors based on a newly developed adaptive recursive Backstepping control approach for a permanent magnet synchronous motor drive is discussed and analyzed. The Backstepping technique provides a systematic method to address this type of problem. It combines the notion of Lyapunov function and a controller procedure recursively.  The adaptive Backstepping control approach is utilized to obtain the robustness for mismatched parameter uncertainties. The overall stability of the system is shown using Lyapunov stability theorem. The simulation results clearly show that the proposed scheme can track the speed reference.