Abstract-Efficient torque control of induction motor drives in combination with resonant dc-link input filters can lead to a type of stability problem that is known as negative impedance instability. An often-proposed solution to this problem is the nonlinear system stabilizing controller (NSSC). Stability is usually analyzed under the simplifying assumption of perfect torque control. This indicates that the NSSC stabilizes the drive at any operating point. In this paper, however, we show power laboratory experiments where the NSSC stabilization fails. An improved framework for stability analysis and synthesis of stabilization, based on a linear feedback model of the drive, is therefore proposed. With this approach, effects of time delays can easily be included, and stability margins can be directly established from measurements. To solve the indicated problems with NSSC, a stabilization controller that considers the practical limitations of torque control is derived. In the design of the stabilization controller, the tradeoff between damping and acceptable torque control is also explicitly taken into account. The proposed stabilization scheme is implemented and evaluated on a hardware-in-the-loop simulator as well as in a power laboratory. The results show that the proposed method outperforms the NSSC method.
This work is an extension of the paper (Mosskull et al., 2003), in which the modelling, identification and stability of an nonlinear induction machine drive is studied. The validation of the stability margins of the system is refined by an improved estimate of the induced L 2 loop gain of the system. This is done with a procedure called power iterations where input sequences suitable for estimating the gain are generated iteratively through experiments on the system. The power iterations result in higher gain estimates compared to the experiments previously presented. This implies that more accurate estimates are obtained as, in general, only lower bounds can be obtained as estimates for the gain. The new gain estimates are well below one, which suggests that the feedback system is stable. The experiments are performed on an industrial hardware/software simulation platform. In this paper we also discuss the power iterations from a more general point of view. The usefulness of the method for gain estimation of nonlinear systems is illustrated through simulation examples. The basic principles of the method are provided.
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