This paper presents a predictive strategy for the speed control of a two-mass system driven by a permanent magnet synchronous motor (PMSM). The proposed approach allows to manipulate all the system variables simultaneously, including mechanical and electrical variables in a single control law. The state feedback is achieved with a reduced order extended Kalman filter, which observes the non-measured variables as well as reduces the impact of measurement noise. The performance of the control strategy is shown through simulation and experimental results in a 4 [kW] laboratory prototype.

Models for deterministic continuous-time nonlinear systems typically take the form of ordinary differential equations. To utilize these models in practice invariably requires discretization. In this paper, we show how an approximate sampled-data model can be obtained for deterministic nonlinear systems such that the local truncation error between the output of this model and the true system is of order /spl Delta//sup r+1/, where /spl Delta/ is the sampling period and r is the system relative degree. The resulting model includes extra zero dynamics which have no counterpart in the underlying continuous-time system. The ideas presented here generalize well-known results for the linear case. We also explore the implications of these results in nonlinear system identification.

In this paper we consider the problem of identification of linear systems using quantized data. We argue that, where possible, it is desirable to not utilize "naively" quantized data but instead it is preferable to choose the quantization mechanism carefully. In particular, we show that using a generalized noise shaping coder improves the accuracy of the estimates. We examine the accuracy of estimates for both naive and coded quantizers.

In this paper we discuss how to obtain accurate and simple sampled-data models for model predictive control (MPC) in power electronics. We highlight the role that relative degree plays in the model accuracy. To support our presentation, we review examples from the literature where model complexity, time-varying parameters, and nonlinearities make the discretization procedure a key issue to achieve good performance in MPC strategies. Moreover, we propose a general discretization procedure based on a simple Taylor series expansion, which provides a sampled model with higher accuracy than Euler approximation.