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This paper proposes a full order nonlinear dynamic model for a DC-DC Multilevel Boost Converter (MBC). This model is based on the equivalent circuits that depend on the commutation states of the converter. A reduced order nonlinear model to approximate the dynamics of the MBC containing any number of levels is also obtained. In addition, an input-output feedback linearization controller is derived and implemented. The stability of the closed loop system is analyzed. A Linux-based real-time software is employed for obtaining the experimental results of the closed loop system.

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State estimators for induction motors are generally designed based on standard simplified models, assuming linear magnetic characteristics. Since they are actually nonlinear, especially for high power machines, the mentioned state estimators are likely not able to achieve the estimation accuracy they have been designed for. In this paper, a new state estimator is developed for a (uniform air-gap) AC machine, based on a more accurate model that appropriately accounts for the saturation feature in the magnetic characteristics. The proposed estimator is a high-gain full-order nonlinear observer designed using Lyapunov stability tools. The resulting estimation errors are shown to asymptotically vanish, if their initial values belong to a well defined attraction region. Supremacy of the new observer over standard ones is illustrated by simulation using a 7.5 KW AC machine.

In this paper, an adaptive observer is proposed to solve the problem of simultaneous parameter identification and state estimation for a class of cascade state affine systems. Sufficient conditions are given in order to guarantee the exponential convergence of the proposed observer. Furthermore, simulation results are given illustrating the performance of the proposed observer when it is applied in the synchronization and identification problem of Rossler's chaotic system.