This article proposes a generalization of the model-free predictive current controller (MFPCC) for multilevel voltage source inverters (VSIs). MFPCC is an alternative to mitigate the parameter sensitivity faced by model-based PCC (MPCC), but it requires a constant update of the stored current variations (CVs) associated with each VSI state to provide a prediction, which results in a satisfactory closed-loop response. When a multilevel VSI is considered the number of states increases, which results in a decrease of the CV update rate, since only one state can be updated at each sample. To solve this issue, the extended adjacent state scheme is used to reduce the possible number of solution candidates and then a controlled CV is used to compensate the updated CV. Simulation and experimental results obtained with a sampling rate of 5 kHz show that the proposed MFPCC exhibits performance similar to the one of MPCC for nominal parameters. However, a better current response is obtained in the case of the load parameter mismatch, especially at steady state.

Model-Free Predictive Current Control for Multilevel Voltage Source Inverters

In the paper a comparative study of the two control structures based on MPC (Model Predictive Control) for an electrical drive system with an induction motor are presented. As opposed to the classical approach, in which DFOC (Direct Field Oriented Control) with four controllers is considered, in the current study only one MPC controller is utilized. The proposed control structures have a cascade free structure that consists of a vector of electromagnetic (torque, flux) and mechanical (speed) states of the system. The first investigated framework is based on the finite-set MPC. A short horizon predictive window is selected. The continuous set MPC is used in the second framework. In this case the predictive horizon contains several samples. The computational complexity of the algorithm is reduced by applying its explicit version. Different implementation aspects of both MPC structures, for instance the model used in prediction, complexity of the control algorithms, and their properties together with the noise level are analyzed. The effectiveness of the proposed approach is validated by some experimental tests.

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Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches

Strong parameter dependence and large current ripple are two shortcomings that obstruct the development of model predictive control for voltage source inverters (VSIs). To solve these problems, in this article,a double-vector model-free predictive control (MFPC) method for VSI with visualization analysis is proposed. First, the ultralocal model of the VSI is established and a full-order sliding model observer is designed to estimate the lumped disturbance. MFPC is achieved with enhanced parameter robustness. Then, a double-vector MFPC method is further proposed. By applying two vectors per control period, the current ripple is reduced significantly. A detailed visualization analysis method is proposed, which verifies the effectiveness of the proposed double-vector MFPC method in theory. Besides, the proposed visualization analysis method has the potential to be used to analyze the effectiveness of other types of multivector model predictive control method with different cost functions. Detailed comparative experimental studies verify the effectiveness of the proposed double-vector MFPC method. 

Double-Vector Model-Free Predictive Control Method for Voltage Source Inverter With Visualization Analysis

Sensorless control theory is considered as one of the most important keys to reduce the cost of manufacturing and maintenance in automotive applications. This article presents a current sensorless control algorithm of a wound rotor synchronous machine using a model-free-adaptive control (MFAC). The only controlled variable in this method is the rotor speed, and no observation or estimation will be performed on the phase currents. The presented controller is also compared with a current sensorless control method that uses a Lyapunov-based observation of phase currents in the control algorithm. The developed control algorithm is first tested in simulation under several operating conditions, such as starting, tracking, parameter variation, and load step. Then, experimental tests validated the effective performance of MFAC in a current sensorless control algorithm. According to these results, the control of phase currents is guaranteed with a small error but without using any current sensors.

Current Sensorless Control for WRSM Using Model-Free Adaptive Control

This paper states that Model-Free Control (MFC), which must not be confused with Model-Free Reinforcement Learning, is a new tool for Machine Learning (ML). MFC is easy to implement and should be substituted in control engineering to ML via Artificial Neural Networks and/or Reinforcement Learning. A laboratory experiment, which was already investigated via today’s ML techniques, is reported in order to confirm this viewpoint.

Machine learning and control engineering: The model-free case

A modulated model-free predictive control with minimum switching losses (MSL-MMFPC) is proposed to improve the steady-state performance and reduce the switching losses for a permanent magnet synchronous motor (PMSM) drive system. Firstly, two adjacent current vectors are determined based on the predefined first-level cost function, and then, make the current vector at the next control period equal to the reference current vector by modulating the selected current vectors properly. Additionally, in order to keep optimal control performance also in the over-modulation region, a new rotating coordinate frame is used to adjust the optimal voltage vector. Then, the second-level cost function is designed to select the optimal voltage vector sequence, so that the switching of a VSI leg does not happen during the phase-current maximum, which can reduce the switching losses of the inverter. The simulation and experimental results verify the effectiveness of the proposed control method.

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Hongmei Li

Papers

Modulated Model-Free Predictive Control With Minimum Switching Losses for PMSM Drive System