Juan I. Yuz

Predictive Torque Control of Induction Machines Based on State-Space Models

In this paper, we present a predictive control algorithm that uses a state-space model. Based on classical control theory, an exact discrete-time model of an induction machine with time-varying components is developed improving the accuracy of state prediction. A torque and stator flux magnitude control algorithm evaluates a cost function for each switching state available in a two-level inverter. The voltage vector with the lowest torque and stator flux magnitude errors is selected to be applied in the next sampling interval. A high degree of flexibility is obtained with the proposed control technique due to the online optimization algorithm, where system nonlinearities and restrictions can be included. Experimental results for a 4-kW induction machine are presented to validate the proposed state-space model and control algorithm.

Model Predictive Control of an Inverter With Output

The use of an inverter with an output LC filter allows for generation of output sinusoidal voltages with low harmonic distortion, suitable for uninterruptible power supply systems. However, the controller design becomes more difficult. This paper presents a new and simple control scheme using predictive control for a two-level converter. The controller uses the model of the system to predict, on each sampling interval, the behavior of the output voltage for each possible switching state. Then, a cost function is used as a criterion for selecting the switching state that will be applied during the next sampling interval. In addition, an observer is used for load-current estimation, enhancing the behavior of the proposed controller without increasing the number of current sensors. Experimental results under linear and nonlinear load conditions, with a 5.5-kW prototype, are presented, verifying the feasibility and good performance of the proposed control scheme.

LC

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.

Filter for UPS Applications

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.

On sampled-data models for nonlinear systems

Modern signal processing and control algorithms are invariably implemented digitally, yet most real-world systems evolve in continuous time. Hence, the interaction between sampling and the behavior of continuous-time systems is an important ingredient in all real-world signals and systems problems.

Predictive Speed Control of a Two-Mass System Driven by a Permanent Magnet Synchronous Motor

Physical systems typically evolve continuously whereas modern controllers and signal processing devices invariably operate in discrete time. Hence sampling arises as a cornerstone problem in essentially all aspects of modern systems science. This paper reviews various aspects of sampling of signals and systems. We argue that careful consideration must be given to sampling to obtain meaningful results when interconnecting a physical system to a computer for the purpose of data storage, signal processing, or control. We also take the opportunity to dispel several common misconceptions about sampling and sampled-data systems.

Sampling and Sampled-Data Models: The Interface Between the Continuous World and Digital Algorithms

Maximum likelihood estimation has a rich history. It has been successfully applied to many problems including dynamical system identification. Different approaches have been proposed in the time and frequency domains. In this paper we discuss the relationship between these approaches and we establish conditions under which the different formulations are equivalent for finite length data. A key point in this context is how initial (and final) conditions are considered and how they are introduced in the likelihood function.

Sampling and sampled-data models

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.

On the equivalence of time and frequency domain maximum likelihood estimation

It is well-known that generalised hold functions can be used to shift the zeros of sampled-data models for continuous-time systems. In this paper, we consider the use of generalised holds to deal with sampling zeros only. We propose a hold design that places the sampling zeros asymptotically to the origin, when the sampling period tends to zero. The resulting generalised hold is only a function of the process relative degree. We also investigate the robustness of the procedure with respect to both finite sample periods and unmodelled plant dynamics.

On sampled-data models for model predictive control

A precursor to any advanced control solution is the step of obtaining an accurate model of the process. Suitable models can be obtained from phenomenological reasoning, analysis of plant data or a combination of both. Here, we will focus on the problem of estimating (or calibrating) models from plant data. A key goal is to achieve robust identification. By robust we mean that small errors in the hypotheses should lead to small errors in the estimated models. We argue that, in some circumstances, it is essential that special precautions, including discarding some part of the data, be taken to ensure that robustness is preserved. We present several practical case studies to illustrate the results.