Finite Control Set Model Predictive Control (FCS-MPC) is an alternative to conventional linear controllers in power converters due to its attractive properties such as its conceptual simplicity, flexibility, direct consideration of non linearities as well as constraints, and fast control response. This last feature imposes computational constraints and short prediction horizons are therefore needed. In fact, in many applications an horizon one FCS-MPC controller is used. However, using FCS-MPC with a short prediction horizon in topologies that exhibit non-minimum phase behavior such as active-front-end rectifiers, dc-dc boost converters, and Z-source converters, among others, may lead to control issues. This work studies the origin of these control issues using nonlinear control theory considering a dc-dc boost converter as a case study. It is shown that the difficulty to directly control the converter output voltage using short horizon FCS-MPC is the resulting unstable internal dynamic, as the short horizon FCS-MPC controller acts like an input-output linearizing controller. To solve the problem of the unstable internal dynamic, a short horizon FCS-MPC controller based on input-state linearization is proposed allowing correct operation of the system. Experimental results show the feasibility of the proposal.

Renewable energy has gained significant attention of researchers in the last years, mainly due to the importance of using unlimited energy sources to supply homes, industries, cities and countries. In this context, this document focuses on the solar injection by employing a neutral point clamped (NPC) topology together with utilization of a maximum power point tracking (MPPT) and space vector modulation (SVM) techniques. Model predictive control (MPC) is employed to manage the currents and track their references. The proposed algorithms do not employ a cost function to decide which voltage to apply resulting in a spread frequency spectrum, and instead, a concentrated SVM spectrum is imposed. Notwithstanding, the DC link capacitors voltage balance is ensured and the computational burden is notably reduced as compared to traditional Finite Set Model Predictive Control (FS-MPC). Nevertheless, the consistent results are a consequence of the critical analysis that shows the feasibility of the proposal and guarantees the good performance of the entire system in simulated and experimental platforms.

Over the last few decades, the consolidated goal of reducing greenhouse gasses has increased the relevance of renewable energy research, electromobility, energy storage, and distributed generation, micro-grids, among others. Micro-grids, systems working in islanding mode, are particular cases where some disadvantages are present due to the wide variations which may appear across their electrical quantities. Variations on the voltage amplitude and the frequency are intrinsic in the operation of weak grids, because they have low inertia and therefore the load must be able to cope with these variations, otherwise loads may trip electrical system protection. Particularly, on power electronic drives, these frequency deviations will lead to increased system nonlinearities, entailing a more critical controller design. To overcome these issues, this paper presents an implementation of a resonant controller with self-tuned gains. The strategy imposes a constant sampling time which allows these controllers to be used in variable frequency environments. In addition, the computational capacity required for the digital board is also considered. The simulated and experimental results provided demonstrate the good performance of this proposal.

Power converters have turned into a critical and every-day solution for electric power systems. In fact, the incorporation of renewable energies has led towards the constant improvement of power converter topologies and their controls. In this context, over the last 10 years, model predictive control (MPC) is positioned as one the most studied and promising alternatives for power converter control. In voltage source inverters (VSI), MPC has only been applied in the inner current control loop, accelerating and improving its dynamic response, but as mentioned, has been limited only to the current control loop. The fastest of the MPC techniques is the Deadbeat (DB) control, and in this paper, it is proposed to employ DB control on the entire system, therefore accelerating the time response not only for the current loops, but also for voltage loops. At the same time, this avoids overshoots and overpower in order to protect the power converter, leading to the fastest dynamic response according to VSI capabilities. For renewable energies, fast-dynamics entails fast maximum power tracking and therefore, maximizes energy harvesting, or in other words, reduces the losses due to the control dynamics. Thus, this paper gives a clear procedure and key points for designing a DB control for all the variables based on a mathematical model, which is corroborated by simulations and the experimental results.

Three-phase active front end rectifiers are a widely used power topology in applications such as renewable energy interfaces and motor drives, among many others. Its control is usually performed by a cascade combination of linear controllers which must be properly tuned for correct operation, and which depend on the operating point and system parameters. These family of controllers place inherent limits on the system dynamic response. On the other hand, the predictive control approach has been proposed as an alternative technique for these converters due to its fast dynamic response, simple concept, flexibility, multiple objective capability, among other desirable properties. In the case of power converter applications, short prediction horizons are usually used due to computational limitations. However, when short horizons are considered, the non-minimum phase characteristic of the rectifier voltage response can introduce stability issues, as its direct inversion is not possible without jeopardizing it. Indeed, this may lead to voltage regulation loss, and in the worst case, overcurrents that could damage the converter. To overcome such problem, this paper proposes a new concept for a predictive horizon one voltage controller for grid-connected three-phase active front end rectifiers. The proposal is based on the minimum and non-minimum phase plant factorization concept and the use of different sampling periods, to prevent the direct inversion of the non-minimum phase voltage dynamic. Simulated and experimental results are included and show its correct operation, even with a horizon one predictive voltage controller, thus ensuring the fastest response of the system for a given sampling time.

This article presents a novel simplified method to implement the finite set - model predictive control technique for photovoltaic generation systems connected to the ac network. This method maintains the advantages of the conventional finite set - model predictive control, such as fast response, simple implementation, and easy understanding; but it also eliminates the use of a cost function and hence the weighting factors, instead, it finds the optimal operating state directly from the model and the discrete number of valid states of the converter. Although the proposed algorithm does not compute a cost function, it is able to select the inverter state that minimizes the tracking error by using a hexagonal convergence region. The main advantage of this technique is to reduce the computational cost in 43% of the algorithm that selects the best state, presenting a simple and complete algorithm without compromising the predictive control performance. The proposed algorithm properly operates under various conditions such as changes in the network frequency and changes in the system parameters.