Finite Set Model Predictive Control (FS-MPC) has many advantages, such as a fast dynamic response and an intuitive implementation. For these reasons, it has been thoroughly researched during the last decade. However, the wave form produced by FS-MPC has a switching component whose spread spectrum remains a major disadvantage of the strategy. This paper discusses a modulated model predictive control that guarantees a spectrum switching frequency in the linear modulation range and extends its optimized response to the overmodulation region. Due to the equivalent high gain of the predictive control, and to the limit on the voltage actuation of the power converter, it is expected that the actuation voltage will enter the overmodulation region during large reference changes or in response to load impacts. An optimized overmodulation strategy that converges towards FS-MPC’s response for large tracking errors is proposed for this situation. This technique seamlessly combines PWM’s good steady-state switching performance with FS-MPC’s high dynamic response during large transients. The constant switching frequency is achieved by incorporating modulation of the predicted current vectors in the model predictive control of the currents in a similar fashion as conventional Space-Vector Pulse Width Modulation (SV-PWM) is used to synthesize an arbitrary voltage reference. Experimental results showing the proposed strategy’s good steady-state switching performance, its FS-MPC-like transient response and the seamless transition between modes of operation are presented for a permanent magnet synchronous machine drive.

This paper presents a novel Oversampling DeadBeat (OS-DB) current control approach for Permanent Magnet Synchronous Motors (PMSMs) drives capable of operating at a controller sampling frequency multiple of the power converter switching frequency. Model based controllers suffer form heavy computational demand and performance degradation due to parameters uncertainties. The proposed controller concurrently with FPGA implementation permits to achieve a constant switching frequency and an optimal current ripple along with a high current loop bandwidth and robust behaviour to parameters variation. A disturbance observer has been added to the proposed controller in order to compensate for the converter voltage distortions. The proposed control strategy is tested through both simulations and experiments.

This paper describes a hybrid control scheme for a permanent magnet machine-based starter-generator (S/G) system. There has been increased usage of electric drive systems in the transportation sector for increased efficiency and reduced emissions. One of the advantages of utilizing suitable electric drives is the capability to operate as a starter or generator. The control design of such a system should be considered due to the operating requirements and fast load changes. Different control approaches should therefore be considered in order to achieve these goals, which are a current trend in the transportation sector. Model predictive control (MPC) is considered due to its very fast dynamic performance. In particular, modulated MPC (M ² PC) was recently introduced and showed significantly better performance than the standard MPC. The control scheme used in this paper utilizes M ² PC for the current inner loop and PI controllers for the outer loop. The use of M ² PC allows very fast transient-current response for the S/G system. The proposed overall control benefits from reduced current ripple when compared with a full cascaded PI control scheme. Simulation analyzes and experimental results show the capability and performance of the designed controller across both starter and generator modes.

The integration of artificial intelligence (AI) techniques in power converter-based systems has the potential to revolutionize the way these systems are optimized and controlled. With the rapid advancements in AI and machine learning technologies, this paper presents the analysis and evaluation of these powerful tools as well as in computational capabilities of microprocessors that control the converter. This paper provides an overview of AI-based controllers, with a focus on online/offline supervised, unsupervised, and reinforcement-trained controllers. These controllers can be used to create surrogates for inner control loops, complete power converter controllers, and external supervisory or energy management control. The benefits of using AI-based controllers are discussed. AI-based controllers reduce the need for complex mathematical modeling and enable near-optimal real-time operation via computational efficiency. This can lead to increased efficiency, reliability, and scalability of power converter-based systems. By using physics-informed methods, a deeper understanding of the underlying physical processes in power converters can be achieved and the control performance can be made more robust. Finally, by using data-driven methods, the vast amounts of data generated by power converter-based systems can be leveraged to analyze the behavior of the surrounding system and thereby forming the basis for adaptive control. This paper discusses several other potential disruptive impacts that AI could have on a wide variety of power converter-based systems.

The angle-based repetitive controller (RC) has been applied successfully in the literature for torque ripple suppression in variable speed permanent magnet synchronous machines (PMSMs). The RC is preferred because of its learning capability, which allows the cancelation of all periodic ripples from multiple sources. However, its tuning method has not yet been proposed. Since the structure of the RC is found similar to the disturbance observer (DOB), which is convenient to tune through pole placement, this paper merges the angle-based RC and DOB into a novel angle-based repetitive observer (ARO) for the first time for the torque ripple reduction in PMSMs. ARO takes advantages of its DOB structure that it can be designed separately and can easily be added into an existing control loop. Taking advantage of its RC nature, ARO can tackle a wide frequency range of torque ripple even in the presence of measurement noise. The experimental test results have confirmed the robust performance of ARO under five conditions, including the ideal integral delay, the fractional delay, the load transient, the speed transient, and the detuned mechanical parameters conditions. The execution time of the ARO is less than 10 μs.