Wenjie Wu

Finite control-set model predictive control (FCS-MPC) has emerged as a promising control scheme for power converters. However, its dependence on precise physical parameters presents challenges to practical implementation. To address this inherent model dependence issue, this paper proposes a data-driven predictive control strategy for LC-filtered voltage source inverters, utilizing full-form dynamic-linearization (FFDL) technique. To mitigate the inherent computational delay in digital implementations, conventional model-based and model-free MPC typically adopt a sequential prediction framework. In contrast to this widely deployed sequential prediction structure, this paper constructs a holistic input-output data model that directly captures the mapping dynamics from the current instant to the delay-compensated future. Consequently, neither physical parameters nor delay compensation procedures are required in this design, rendering a simplified implementation. Furthermore, by leveraging the comprehensive data structure of FFDL, the proposed strategy achieves direct output capacitor voltage control, in the absence of inductor current sensors and specific circuit parameters, thereby enhancing system reliability and reducing cost. Comparative experimental results on a three-level neutral-point-clamped (3L-NPC) inverter validate the effectiveness of the proposed method, demonstrating its computational efficiency, and strong robustness against parameter mismatches compared to existing schemes.

Finite control-set model predictive control (FCS-MPC) is regarded as a promising control method in power converters due to its excellent performance, simple implementation, and fast dynamic response. However, standard model predictive control suffers from a high dependency on model parameters. To solve this issue, a novel finite set subspace predictor-based predictive voltage control strategy is proposed in this article, aiming to improve the robustness of the controlled system while maintaining the attractive features of standard FCS-MPC. Specifically, by replacing the original physical model with a subspace predictor at each operating point, the optimal control input can be directly obtained according to the reference output trajectory, and the control action can be executed without knowing the system structure and load parameters. This method uses only historical input–output data in the prediction process, thereby avoiding performance degradation caused by variations in load parameters. Finally, experiments on a three-level neutral-point clamped inverter validate the proposed design.

This article focuses on introducing soft reinforcement learning (RL) techniques into the finite controlset model predictive control (FCS-MPC) framework to enhance robust performance. More precisely, building upon a neural predictor, an intelligent agent trained using the soft actor–critic algorithm is developed to explore the optimal control input embedded within the MPC framework. Meanwhile, during training, a constraint based on a Lyapunov function is introduced, and a corresponding update law for the weights is provided. Furthermore, this proposed method guarantees the stability of the system integrated with the RL intelligent agent. Finally, both simulation and experimental results validate the superiority of this approach compared to the existing FCS-MPC methods.

Finite control-set model predictive control (FCS-MPC) has been found as a promising alternative in the control of power converters and motor drives, albeit with model dependence issues. This inherent defect of the FCS-MPC controller triggered the widespread of model-free or data-driven control schemes in recent decades. This article, at hand, presents a data-enabled finite set predictive control solution subject to model dependence issues from the dynamic modeling point of view. In this regard, a dynamic-linearization data model is utilized to equivalently reformulate the governed power converter at each operation point. In pursuit of the accurate modeling of the plant, the time-varying parameters of the data model are updated online by an adaptive linear neural network, rendering a favorable influence on implementation. Additionally, an improved capacitance-less voltage balancing method is proposed to regulate the neutral point potential. Since the parameterless prediction process for both load currents and capacitor voltage relies solely on measured and historical input– output data of the plant, the destructive effect of parameter variations can be circumvented. To evaluate the correctness of the proposed solution, the comparative simulation and experimentation with the conventional method and state-of- the-art solutions are examined on a classic three-level neutral-point-clamped inverter.

In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input–output data, exhibiting strong rejection against parameter variations while inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.

Parallel inverters are widely used in the micro-grid systems to meet the demand for increased power capacity and enhanced reliability. However, unbalanced connections between the parallel inverters may cause a spike in the zero-sequence circulating current (ZSCC), leading to performance degradation. To address this issue, a virtual voltage vector-based sequential model-free predictive control method is proposed in this paper for parallel converters. Specifically, extended state observer (ESO) and virtual voltage vectors are introduced into the finite control-set model predictive control (FCS-MPC) architecture to cope with the multi-objective including ZSCC inhibition and robustness performance improvement. Next, to prevent the adjusting of weighting factors, a sequential model-free predictive control solution is constructed. The main feature of the proposed control methodology is that the reliability and robustness of the controlled system can be significantly enhanced without knowing the accurate mathematical models and parameters. Finally, the effectiveness of the suggested approach is verified by performance evaluation.

This paper investigates a data-driven based predictive current control (DD-PCC) approach for a modular multilevel converter (MMC) to circumvent the sensitiveness to parameter variation and unmodeled dynamics of finite control-set model predictive control (FCS-MPC) method. By integrating a model-free adaptive control based data-driven solution into the FCS-MPC framework, the performance deterioration caused by model uncertainties is suppressed. The design of the suggested controller is only based on input-output measurement data, where neither the parameter information nor the knowledge of detailed MMC models are required, leading to improved robustness against parameter drifts and model uncertainness. Moreover, a simplified cost function formula that takes into account output current tracking and circulating current regulation is constructed to efficiently determine the optimal insertion index of each arm. Finally, simulation and experimental results are obtained to verify the steady-state, dynamics, and robustness performance of the proposed approach.

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.

This article proposes a novel data-driven neural predictors-based robust finite control-set model predictive control (FCS-MPC) methodology for power converters, which aims to enhance the robustness of the control system and the flexibility of multiple control objectives. To be specific, the data-driven predictors-based neural network solution, which has a good potential to estimate the unknown nonlinear system dynamics by deploying real-time and historical data, is incorporated into the proposed design with a cost function derived intuitively from Lyapunov’s control theory. The key feature of this work is that the undesired effects of the uncertainties and the complex multiobjective optimization procedure can be explicitly dealt with, without involving accurate modeling information and weighting factor combinations, while guaranteeing adaptability to unknown nonlinear system dynamics, model variations, and environment changes. Finally, the stability analysis is given, and the effectiveness of the proposed FCS-MPC methodology is verified analytically and confirmed by comprehensive results that prove the theoretical investigations for active front-end modular multilevel converter.

An event-triggered control technique has been developed recently. This technique explicitly reduced the signal transmission by introducing a flexible design of threshold inequalities. It was later extended to event-triggered model-predictive control for power converter systems. In this letter, by incorporating this control technique into an extended state-observer-based finite-control-set model-predictive control framework, we have developed a new model-predictive control architecture for power converter systems with parametric uncertainties. Meanwhile, a novel cost function with respect to the angle minimization term is embedded into this proposal. The novelty of our development lies not only in integrating the event-triggered mechanism with the suggested finite-control-set model-predictive control architecture for facilitating the alleviation of performance deterioration caused by parameter variations and model uncertainties, but also in a multiobjective optimization design that allows the switching frequency in a low value. Finally, extensive simulative and experimental investigations for a modular multilevel converter confirm the interest and the viability of the proposed design methodology.