Nonlinear control and estimation in induction machine using state estimation techniques

In this paper, several techniques are addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. The estimation techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Specifically, two comparative studies are performed. In the first comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square errors with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF, due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF and EKF and can still provide both convergence as well as accuracy-related advantages over other estimation methods. This is because the covariance is propagated through linearization of the underlying nonlinear model, when the state transition and observation models are highly nonlinear.