Optimization-based control strategies are an affirmed research topic in the area of electric motor drives. These methods typically rely on the accurate parametric representation of equations of a motor. In this article, we present the transition from model-based to data-driven optimal control strategies. We start from the model-predictive control paradigm, which uses the voltage balance model of the motor. Then, we discuss the prediction error method, where a state-space model is identified from data, without parameterization. Moving toward data-driven controls, we present the subspace predictive control, where a reduced model is constructed based on the singular value decomposition of raw data. The final step is represented by a complete data-driven approach, named data-enabled predictive control, in which raw data are not encoded into a model but directly used in the controller. The theory behind these techniques is reviewed and applied for the first time to the design of the current controller of synchronous permanent magnet motor drives. Design guidelines are provided to practitioners for the proposed application, and a way to address offset-free tracking is discussed. Experimental results demonstrate the feasibility of the real-time implementation and provide comparisons between the model-based and data-driven controls.

The data-driven control paradigm allows overcoming conventional troubles in the controller design related to model identifications procedures. Raw data are directly exploited in the control input selection by forcing the future plant dynamics to be coherent with previously collected samples. This article focuses, in particular, on the data-enabled predictive control algorithm. A relevant disadvantage of this algorithm is the fact that the complexity of the online control program grows with the dimension of the dataset. This issue becomes particularly relevant when considering embedded applications, such as the control of synchronous motor drives, characterized by challenging real-time constraints. This work proposes a systematic approach for dramatically reducing the complexity of such algorithms. Such methodology enables real-time feasibility of the constrained version of this control structure, which was previously precluded. Simulations and experimental results are provided to validate the method, considering the current control of an interior permanent magnet motor as test-case.

This paper deals with motor design aspects and control strategies for the flux-weakening (FW) operation of synchronous motors. The theory of FW is described by taking into account different control schemes. The advantages and drawbacks of each one are discussed, as well. Moreover, some motor design considerations for achieving an effective FW operation are illustrated for permanent magnet (PM), wound rotor (WR) and reluctance (REL) synchronous machines, using the per unit approach. The distinguishing characteristic of this review provides two-fold attention on both machine design and control strategies obtained by several collaborations with industrial and commercial companies.

This paper proposes a motor parameter-free predictive current control of synchronous motor drives. In the context of motor parameter-free controls, the distinctive feature proposed in this paper is the use of the discrete space vector modulation technique. The advantages of the proposed architecture are manifold. As a distinctive feature of the proposed control algorithm compared to model-based solutions, is the real-time self-adaptation capability to the installed motor. Two recursive least-square estimators update runtime an accurate non parametric model. Compared to previous parameter-free solutions, the proposed algorithm can achieve a significantly lower current harmonic distortion at the same control frequency, while keeping the switching frequency value at bay. The result is an efficient and smooth torque delivery. The paper gives the necessary design hints and analyses the steady state performance in terms of both current harmonic distortion and switching frequency, taking finite-set model-based and parameter-free schemes as benchmark. This analysis was previously missing in literature for parameter-free schemes. Experimental validation is completed by including dynamic test results.

This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications. A relevant feature of the proposed solver is that the total number of operations can be computed in the worst-case scenario. Thus, the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. The proposed method outperforms general-purpose algorithms in terms of computation time, while keeping the same accuracy.