Data-Driven Predictive Current Control for Active Front Ends with Neural Networks

This paper focuses on the model predictive current control of power converters with the aim of indicating the influence of some system parameters used in predictive control on the load current and load voltage. A model predictive current control algorithm is proposed, specifically directed at the utilization of power obtained from renewable energy systems (RESs). In this study the renewable energy systems model is used to investigate system performance when power is supplied to a resistive-inductive load (RL-load). A finite set-model predictive current control (FS-MPCC) method is developed to control the output current of three-phase, voltage source inverter (VSI). The approximation methods for the derivatives of the model differential equations and delay compensation of model predictive control (MPC) system for power converters are assessed. Simulation results of a two-level, three-phase VSI using FS-MPCC are carried out to show the effects of different approximation methods on the load current and voltage regulation as well as on the predictive current control operation with and without delay compensation for different sampling times. It has been noticed that the ripple in the load currents is considerable when the delay compensation is not accounted for and the delay compensation method that reduces the ripple and operation is similar to the ideal case. It is confirmed that for larger sampling times the delay is noticeable, but when the sampling time is smaller it is not visible.

Torque coordination control significantly affects the mode transition quality during the mode transition dynamic process of hybrid electric vehicles (HEV). Most of the existing torque coordination control methods are based on the mechanism model, whose control effect heavily depends on the modeling accuracy of the HEV powertrain. However, the powertrain structure is so complex, that it is difficult to establish its precise mechanism model. In this paper, a torque coordination control strategy using the data-driven predictive control (DDPC) technique is proposed to overcome the shortcomings of mechanism model-based control methods for a clutch-enabled HEV. The proposed control strategy is only based on the measured input-output data in the HEV powertrain, and no mechanism model is needed. The conflicting control requirements of comfortability and economy are included in the cost function. The actual physical constraints of actuators are also explicitly taken into account in the solving process of the data-driven predictive controller. The co-simulation results in Cruise and Simulink validate the effectiveness of the proposed control strategy and demonstrate that the DDPC method can achieve less vehicle jerk, faster mode transition and smaller clutch frictional losses compared with the traditional model predictive control (MPC) method.

Data-driven model predictive control for building climate control: Three case studies on different buildings

In this paper, a Lyapunov based finite control set model predictive direct torque control for the permanent magnet synchronous machine (PMSM) is proposed. In the proposed control scheme, the finite control set prediction and the Lyapunov theory are combined to minimize the torque ripple. The 8 voltage vectors of the 2-level converter are utilized as a finite control set for the torque prediction of the PMSM. A cost function considering the torque error, the Maximum Torque per Ampere (MTPA) operation and the current limitation is introduced. Comparing to the conventional finite control set predictive control, the dominant part of the cost function is utilized as a Lyapunov function to estimate the duty cycle of each voltage vector. An optimum voltage can be obtained by the optimum voltage vector from the 8 vectors and their duty cycles. A small sampling frequency and a fixed switching frequency can be realized when compared to the conventional finite set model predictive control. In the end, the simulation and experimental results validate the performance of the proposed control scheme.

This paper proposes a finite set model predictive control (FS-MPC) based thrust maximization technique for linear induction machines used in linear metros. For modeling of the proposed control method, the end effect is taken into consideration. The proposed control method is used to achieve maximum thrust per ampere and to reduce the thrust ripples. It differs from the FS-MPC methods, where the cost function consists of the thrust and angle errors. The thrust error is calculated from the difference between the reference thrust and the predicted thrust, and the angle error is calculated from the difference between the angle of predicted primary current and the angle of the predicted secondary flux in one side and π/4 on the other side. A comparison between the proposed method and the finite set model predictive direct thrust control (FS-MPDTC) is presented to illustrate the superiority of the proposed method. Both simulation and experimental analysis are conducted to validate the effectiveness of the proposed finite set model predictive direct angle control (FS-MPDAC). A prototype test platform is developed in the laboratory with two 3 kW arc induction motors. The simulation model, experimental test platform, and test results are presented in this paper.

This paper proposes a current model predictive control strategy for the permanent magnet synchronous motor (PMSM) based on a novel sliding mode observer to reduce the cost of PMSM and ensure good tracking performance. A super twisting sliding mode observer (STSMO) is designed to address the issues of high-frequency chattering and noise sensitivity caused by the large positive gain of traditional SMO. The discontinuous effect of the traditional SMO switching function is introduced into the derivative of the control rate, and a smooth estimate of the back electromotive force (EMF) is obtained through integration. Replace the sign function with a sigmoid function with smooth continuity to further reduce the chattering effect. To enhance the dynamic performance of the PMSM current loop, a finite control set model predictive control (FCS-MPC) strategy is employed in place of the conventional PI controller. Within each sampling period, all possible switching states are evaluated, and the optimal one is selected and directly applied to the inverter. Additionally, a dual-vector model predictive current control (DVMPCC) method is adopted to reduce current ripple. This approach synthesizes a voltage vector with arbitrary magnitude and direction by combining two voltage vectors within each sampling period. Numerical results demonstrate that the proposed sensorless PMSM predictive current control method achieves high accuracy in speed estimation and excellent dynamic response performance.

Due to model predictive control can better deal with the constraints problem of nonlinear systems and improve the dynamic performance of the controlled system, therefore, this technology has attracted much attention in the field of motor drive. This article first introduced the basic principle of model predictive control, continuous control set model predictive control and finite control set model predictive control. Secondly, summarized the research status of generalized predictive control, explicit model predictive control, model predictive current control, model predictive torque control and commonly used improved model predictive control in motor drive systems. Thirdly, prospected the future development trend based on the current research status of model predictive control in motor drive system. Finally, the advantages and disadvantages of the continuous control set model predictive control and the finite control set model predictive control were comprehensively compared, and the ways in which the two algorithms act on the motor drive system were summarized.

Data-driven predictive control (DPC) has gained an increased interest as an alternative to model predictive control in recent years, since it requires less system knowledge for implementation and reliable data is commonly available in smart engineering systems. Several data-driven predictive control algorithms have been developed recently, which largely follow similar approaches, but with specific formulations and tuning parameters. This review aims to provide a structured and accessible guide on linear data-driven predictive control methods and practices for people in both academia and the industry seeking to approach and explore this field. To do so, we first discuss standard methods, such as subspace predictive control (SPC), and data-enabled predictive control (DeePC), but we also include newer hybrid approaches to DPC, such as γ–data-driven predictive control and generalized data-driven predictive control. For all presented data-driven predictive controllers we provide a detailed analysis regarding the underlying theory, implementation details and design guidelines, including an overview of methods to guarantee closed-loop stability and promising extensions towards handling nonlinear systems. The performance of the reviewed DPC approaches is compared via simulations on two benchmark examples from the literature, allowing us to provide a comprehensive overview of the different techniques in the presence of noisy data.

This paper introduces the comparison of four predictive torque control schemes for a permanent-magnet synchronous machine (PMSM). The first method is the finite-set model predictive control (FS-MPC). In FS-MPC, the optimal switching state is selected based on the evaluation and minimization of a cost function for all possible voltage space vectors (VSVs) of the inverter. The second method performs a simplified FS-MPC where the selection and evaluation of the possible VSVs are reduced to only three. The third method is based on the principle of predictive direct torque control (PDTC), where the duty cycle of the switching state is optimized for application in the inverter. Finally, a method that combines FS-MPC and PDTC named model predictive torque control is presented. This paper introduces the methodology and the results of a comprehensive comparison of the four predictive schemes based on different criterions. The control schemes are implemented on a field-programmable gate array and are applied to a PMSM. Experimental results are presented to validate the presented comparison and discussion.

Finite-set model predictive controls have been widely used in inverter control because of the flexible target control and no need of a modulation unit. However, the mismatching of prediction model parameters produces prediction errors, resulting in a significant decline in the performance of finite-set model predictive controls. Aiming at the problem of model parameter mismatch, an extended-state observer was proposed to accurately estimate the disturbance of the system in this paper, and the obtained disturbance value was added to a finite-set model predictive control controller to compensate for the prediction error and achieve parameter robustness. By constructing a prediction model of the inverter output voltage in αβ coordinates, all the possible output voltage values were predicted by using different voltage vectors and system measurement values. A set of voltage vectors that minimized the cost function was selected, and its corresponding switching state was applied to the inverter in the next sampling period to achieve control of the output voltage quality. Both the simulation and experimental results showed that the finite-set model predictive voltage control method based on the extended-state observer can estimate the total disturbance quickly and accurately, suppress the influence of capacitance parameter disturbance, and improve the control effect of an inverter.

The paper presents a new implementation of direct flux and current vector control of an induction motor drive using the techniques of model predictive control. The advantages offered by predictive control are used to enhance the dynamics of direct flux vector control. To minimize the problems of variable switching frequency inherent to finite control set predictive control, an alternative approach using pulse width modulation is studied for command execution as occurs in the so-called modulated model predictive control. A comparison between finite control set and modulated model predictive control is presented and the results are also compared with the control implementation through traditional proportional-integral regulators to highlight the advantages and drawbacks of predictive control based strategies. Apart from a greater harmonic content in stator currents, the predictive control can offers control dynamics comparable with proportional-integral control while maintaining immunity against machine parameter variations and excluding the need for controller tuning.

Data-driven predictive control (DPC), using linear combinations of recorded trajectory data, has recently emerged as a popular alternative to traditional model predictive control (MPC). Without an explicitly enforced prediction model, the effects of commonly used regularization terms – and the resulting predictions – can be opaque. This opacity may lead to practical challenges, such as reliance on empirical tuning of regularization parameters based on closed-loop performance, and potentially misleading heuristic interpretations of norm-based regularizations. However, by examining the structure of the underlying optimal control problem (OCP), more precise and insightful interpretations of regularization effects can be derived. In this paper, we demonstrate how to analyze the predictive behavior of DPC through implicit predictors and the trajectory-specific effects of quadratic regularization. We further extend these results to cover typical DPC modifications, including DPC for affine systems, offset regularizations, slack variables, and terminal constraints. Additionally, we provide a simple but general result on (recursive) feasibility in DPC. This work aims to enhance the explainability and reliability of DPC by providing a deeper understanding of these regularization mechanisms.

Embedded Model Predictive Direct Switching Control for High Performance Electrical Drives - A Quantitative Comparison

Finite Control Set Model Predictive Field-Oriented Control For Three-Phase Induction Motor Drives

Disturbance rejection strategies are very useful for the robustness improvement of the predictive control method. But they can only be used in the modulated-based predictive control methods such as continuous set model predictive control (CS-MPC) and deadbeat control. This paper presents a robust current prediction model based on total disturbance observer (TDO), which is applicable in the finite set model predictive current control (FS-MPCC). In the proposed method, the disturbance is directly used as a part of the prediction model instead of the disturbance rejection loop. So, the proposed method has two advantages over the disturbance rejection-based CS-MPC schemes. The first advantage is no need for a controller, which is an essential part of the disturbance rejection-based CS-MPC. Therefore, the proposed method is simpler and has fewer control parameters. The second feature is that the proposed model is in the stationary frame. In this way, the frame transformation is avoided in the prediction model. Moreover, to guarantee zero steady-state error in the current prediction model, this paper proposes a complete designing process for TDO based on the convergence analysis. The performance of the proposed control system is evaluated through simulations and experimental tests.