Manuel R. Arahal

A Proof of Concept Study of Predictive Current Control for VSI-Driven Asymmetrical Dual Three-Phase AC Machines

Multiphase (more than three phases) drives possess interesting advantages over conventional three-phase drives. Over the last years, various topics related to the extension of the classical control schemes to these specifics drives have been covered in depth in literature, such as vector control of a six-phase induction machine with two sets of three-phase stator windings spatially shifted by 30 electrical degrees (also called asymmetrical dual three-phase ac machine). In this paper, a model-based predictive control (MBPC) for the current regulation of asymmetrical dual three-phase AC machines is analyzed. MBPC overcomes the difficulties of multiphase current control, avoiding complex controllers and modulation techniques, but at the expense of an increased computational cost. Simulation results are provided to examine the potential of the control method. The influence of the number of voltage vectors considered to evaluate the predictive model is studied, and different cost functions are analyzed. The computation time needed for the implementation of the control method is discussed to prove its real-time feasibility. Finally, experimental results are given to illustrate the capability of the control method.

Bifurcation Analysis of Five-Phase Induction Motor Drives With Third Harmonic Injection

The interest in variable-speed multiphase induction- motor drives has substantially increased in recent years and novel proposals show good prospects for industrial implementation in high-power applications. The additional degrees of freedom existing in multiphase machines have allowed for new applications with high torque density by current harmonic injection in concentrated winding machines. This paper addresses, for the first time, the bifurcation analysis of a five-phase induction-motor drive when a third harmonic is injected for torque-enhancement purposes. The main focus of this paper is to present a mathematically based study of the nonlinear dynamics of the proposed drive with torque enhancement. The overall bifurcation analysis for both concentrated and distributed winding machines confirms that the harmonic injection provides not only torque enhancement but also more robust controllers. This further advantage offers improved performance of multiphase drives compared to their three-phase counterparts.

One-Step Modulation Predictive Current Control Method for the Asymmetrical Dual Three-Phase Induction Machine

Multiphase (more than three phases) drives exhibit interesting advantages over conventional three-phase drives. Over the last years, topics related to the extension of control schemes to these specific drives have been covered in depth in the literature. Direct torque control and predictive current control are normally used in conventional AC drives when fast electrical dynamic performance is required. In this paper, a one-step modulation predictive current control technique is proposed for asymmetrical dual three-phase AC drives. Based on the use of a predictive model including the motor and the inverter, the control algorithm determines the switching state which minimizes errors between predicted and reference state variables. The period of application of the selected switching state is then obtained, resulting in a submodulation method. The proposed predictive current control algorithm uses a prediction horizon of one sampling period; however, two switching states are applied during the sampling period. The switching states are the selected optimum active vector and a null voltage combination. Simulation and experimental results are provided to examine the features of the control method. Performances, advantages, and limitations are also discussed.

Comparative Study of Predictive and Resonant Controllers in Fault-Tolerant Five-Phase Induction Motor Drives

One of the most attractive features of multiphase machines is the fault-tolerant capability due to the higher number of phases. Different postfault control strategies based on hysteresis, proportional integral (PI)-resonant, and predictive techniques have been recently proposed. They all proved their capabilities to withstand fault situations and to preserve the fundamental component of the air-gap field, while achieving minimum losses, maximum torque per ampere, and reducing torque vibrations. Nonetheless, due to their recent introduction, no thorough study has yet appeared comparing the performance of these controllers. In this paper, two open-phase fault-tolerant control schemes are experimentally compared in a real five-phase induction machine. The controllers being compared are based on PI-resonant and predictive control techniques, respectively. The experiments include pre- and postfault situations. Obtained results show that both control methods offer nearly the same performance. When compared, predictive control provides faster control response and superior performance at low-speed operation but is found to be less resilient to fault detection delays and to have higher current ripple. Regarding the controller implementation, it is shown that the transition from prefault to postfault operation involves modeling the nonlinear effects observed when an open-phase fault occurs for the predictive controller and proper retuning of the PI trackers for the PI-resonant controller, to ensure postfault operation.

Neural identification applied to predictive control of a solar plant

Abstract This paper presents an application of the general identification methodology to obtain neural predictors for use in a nonlinear predictive control scheme derived from the generalized predictive controller (GPC) structure. Every step of the design procedure is illustrated using a real-life problem: the identification of a solar power plant for predictive control. Different solutions are discussed for the problems that arise in the application of the methodology. Experimental results are given, showing the performance of the predictor and the controller.

Min-max predictive control of a heat exchanger using a neural network solver

Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical optimization problem that has to be solved at every sampling time. This fact severely limits the class of processes in which this control is suitable. In this brief, the use of a neural network (NN) to approximate the solution of the min-max problem is proposed. The number of inputs of the NN is determined by the order and time delay of the model together with the control horizon. For large time delays the number of inputs can be prohibitive. A modification to the basic formulation is proposed in order to avoid this latter problem. Simulation and experimental results are given using a heat exchanger.

Implementation of min–max MPC using hinging hyperplanes. Application to a heat exchanger ☆

Abstract Min–max model predictive control (MMMPC) is one of the few control techniques able to cope with modelling errors or uncertainties in an explicit manner. The implementation of MMMPC suffers a large computational burden due to the numerical min–max problem that has to be solved at every sampling time. This fact severely limits the range of processes to which this control structure can be applied. An implementation scheme based on hinging hyperplanes that overcome these problems is presented here. Experimental results obtained when applying the controller to the heat exchanger of a pilot plant are given.

Modelling the free response of a solar plant for predictive control

Abstract This paper demonstrates the identification of a nonlinear plant using neural networks for predictive control. The problem of neural identification is tackled using a static (non-recurrent) neural network in an autoregressive configuration (NARX). The selection of a set of input variables, a set of input/output vectors for training, and a neural structure, is discussed. In particular, an algorithm is proposed to obtain the number of past values of the measured variables needed to feed the network. The neural model is then used within a model-based predictive control (MBPC) framework. The MBPC scheme uses the prediction of the output of the system calculated as the sum of the free response (obtained using the nonlinear neural model) and the forced response (obtained linearizing around the current operating point) to optimize a performance index. The on-line adaptation of the model and other issues are discussed. The control scheme has been applied and tested in a solar power plant.

Optimal Fault-Tolerant Control of Six-Phase Induction Motor Drives With Parallel Converters

Multiphase drives and parallel converters have been recently proposed in low-voltage high-power applications. The fault-tolerant capability provided by multiphase drives is then extended with parallel converters, increasing their suitability for safety-critical and renewable uses. This advantageous feature, compared with standard three-phase drives, has been analyzed in the event of open-phase faults. However, when using parallel converters, a converter fault does not necessarily imply an open-phase condition, but usually just a limited phase current capability. This paper analyzes the fault-tolerant capability of six-phase drives with parallel converter supply. Different scenarios considering up to three faults for single and two neutral configurations are examined, optimizing offline the postfault currents and modifying accordingly the control strategies. Experimental results confirm the smooth transition from prefault to postfault situation and the enhanced postfault torque capability.

Five-Phase Induction Motor Rotor Current Observer for Finite Control Set Model Predictive Control of Stator Current

Model predictive control (MPC) has recently been applied to induction motor (IM) drives in a configuration known as finite control set MPC (FCS-MPC). Its implementation must solve the problem of estimating rotor quantities, a task that is usually done using a simple backtracking procedure. On the other hand, observers have been used with field-oriented control (FOC), sensorless drives, and for fault detection but they have not been used yet for finite control set predictive current control of drives. This paper shows the benefits of incorporating a rotor current observer in a finite control set model predictive controller for the stator current of a five-phase drive. The observer design methodology employed in this work uses pole placement based on Butterworth filter design. The new estimation scheme is compared with the standard one in which nonmeasurable state components and other variables are lumped into one term that is updated. The differences between both approaches are experimentally analyzed and verified.