To achieve high efficiency and dynamics in electric drive applications, it is necessary to have accurate torque control. This is typically accomplished through a current regulator that is fed by references generated by various open-loop control strategies, in order to obtain the desired torque. As an alternative, this work presents a model predictive torque control. Starting from the torque reference, the algorithm generates optimal voltage references to the inverter-fed synchronous motor drive while working at maximum efficiency and considering the motor current limit. This feature is achieved by combining two different norms in the cost function. The electromagnetic torque is estimated from the measured currents and an accurate magnetic model of the motor. According to the paradigm of the more autonomous drives, an important feature is that the algorithm requires only knowledge of the motor model. This means that a tuning procedure for control weight is no longer required as analytically discussed in this work. Experimental validation of the proposed technique is performed on a test rig featuring an anisotropic permanent magnet motor in different dynamic operation, including flipping from the motor nominal working point to the generator one.

Climate changes and the lack of running water across vast territories require the massive use of pumping systems, often powered by solar energy sources. In this context, simple drives with high-efficiency motors can be expected to take hold. It is important to emphasise that simplicity does not necessarily lie in the control algorithm itself, but in the absence of complex manual calibration. These characteristics are met by synchronous reluctance motors provided that the calibration of the current loops is made autonomous. The goal of the present research was the development of a current control algorithm for reluctance synchronous motors that does not require an explicit model of the motor, and that self-calibrates in the first moments of operation without the supervision of a human expert. The results, both simulated and experimental, confirm this ability. The proposed algorithm adapts itself to different motor types, without the need for any initial calibration. The proposed technique is fully within the paradigm of smarter electrical drives, which, similarly to today’s smartphones, offer advanced performance by making any technological complexity transparent to the user.

The theory of a new analytical-solution-based algorithm for calculating the optimal solution in model-predictive control applications with hexagonal constraints is discussed in this article. Three-phase voltage-source inverters for power electronic and electric motor drive applications are the target of the proposed method. The indirect model-predictive control requires a constrained quadratic programming (QP) solver to calculate the optimal solution. Most of the QP solvers use numerical algorithms, which may result in unbearable computational burdens. However, the optimal constrained solution can be calculated in an analytical way when the control horizon is limited to the first step. A computationally efficient algorithm with a certain maximum number of operations is proposed in this article. A thorough mathematical description of the solver in both the stationary and rotating reference frames is provided. Experimental results on real test rigs featuring either an electric motor or a resistive–inductive load are reported to demonstrate the feasibility of the proposed solver, thus smoothing the way for its implementation in industrial applications. The name of the proposed solver is aVsIs , which is released under Apache License 2.0 in GitHub, and a free example is available in Code Ocean.

This article presents a torque control strategy for permanent magnet synchronous motors based on a continuous control-set model predictive control with maximum torque-per-current operations capability. The voltage references are generated by solving the optimization problem without any control weights, thus smoothing the way for an easy implementation. A distinctive feature of this control method is its ability to guarantee the maximum torque-per-current criterion under all operating conditions, including during torque transients. This capability is generally unattainable by traditional torque control techniques. The performances of the proposed algorithm are guaranteed, thanks to the novel design of the optimization problem and constraints formulation. The only requirement of the proposed technique is the accurate knowledge of the motor model. Experimental results are provided using an interior permanent magnet motor, where various operating conditions were tested to validate the effectiveness of the proposed control method.

To achieve high efficiency and dynamics in electric drive applications, it is necessary to have accurate torque control. This is typically accomplished through a current regulator that is fed by references generated by various open-loop control strategies, in order to obtain the desired torque. As an alternative, this work presents a model predictive torque control. Starting from the torque reference, the algorithm generates optimal voltage references to the inverter-fed synchronous motor drive, while working at maximum efficiency and considering the motor current limit. This feature is achieved by combining two different norms in the cost function. According to the paradigm of the more autonomous drives, an important feature is that the algorithm requires only knowledge of the motor model. This means that a tuning procedure for control weight is no longer required as analytically discussed in this work. Experimental validation of the proposed technique are performed on a test rig featuring an anisotropic permanent magnet motor in different dynamic operation, including flipping from the motor nominal working point to the generator one.