Search

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. [ABSTRACT FROM AUTHOR]

In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of “explosion of complexity” inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter. [ABSTRACT FROM AUTHOR]

Traditional predictive control schemes dominated the research field of numerous power electronic applications over the past few years, since they usually lead to control solutions with good dynamic and steady results. However, these solutions strongly depend on the available knowledge of the control system (e.g., accurate modeling information), which often results in the lack of robustness in the presence of parametric uncertainties. Furthermore, unnecessary energy loss heavily correlates with high switching frequency, which directly affects efficiency. Aiming to cumbersome aforementioned scarcities, this letter is concerned with a novel approach to the controller design for a modular multilevel converter, which makes use of event-triggered neural-predictor-based finite-control-set model-predictive control methodology under low-switching-frequency operation. The salient feature of the proposed controller is that the uncertainties and unnecessary energy loss in practical systems can be explicitly dealt with, while keeping the switching frequency in a low value. Finally, steady-state and transient-state performance tests together with the analysis of the simulation and experimental results confirm the interest of the proposal, and the results found are promising and motivate further research in this field. [ABSTRACT FROM AUTHOR]

This letter investigates the possibility of deploying a novel finite control-set model predictive control solution for solving the ongoing research challenges in predictive control regulated modular multilevel converter, i.e., model parameter sensitiveness and excessive computational burden as well as weighting factors selection. Specifically, it is realized by cascading a predictor-based neural network design, which enables a smooth and fast identification of system dynamics, and a computationally efficient finite-set predictive control, which is responsible for simplifying the rolling optimization and reducing the computational complexity. The main contribution of the proposed methodology relies on the fact that no knowledge of any model parameters and weighting factors in whole control process are required, which leads to a significant enhancement in the robustness and reliability of the control system in the presence of parametric uncertainties, while remaining computationally feasible. Finally, the stability analysis is given, and the proposed methodology is experimentally assessed for modular multilevel converter, where steady-state and transient-state performance tests confirm the interest of the proposal. [ABSTRACT FROM AUTHOR]