In the traditional virtual flux estimation for a three-phase pulsewidth modulation rectifier, the integration element causes problems for the initial value and dc bias, and the unstable grid voltage induces a nonconstant flux amplitude. To address these issues, an improved direct power control scheme based on an adaptive sliding mode observer is proposed in this work. The observer employs a sigmoid function as switch function to estimate the grid-side source voltage. Meanwhile, an adaptive compensator instead of pure integral element is also designed to dynamically adjust compensation. The stability of this observer is proved by the Lyapunov function; moreover, simulations and experimental results indicate that this new virtual flux observer substantially improves the observation accuracy based on voltage sensorless control. The application of this strategy successfully suppresses the fluctuation of the dynamic voltage response in the dc bus, eliminating high-frequency noise from the grid side, while simultaneously boosting the power quality.

This article presents a novel strategy for designing weighting factors in the model predictive control (MPC) of a three-level inverter-fed permanent magnet synchronous motor (PMSM) by combining multi-objective particle swarm optimization (MOPSO) and artificial neural network (ANN). A cost function containing three weighting factors is designed to formulate a multi-objective optimization scheme. The MOPSO algorithm is employed to establish the Pareto front and calculate the optimal weighting factors for each operating condition. Additionally, an ANN is introduced for online tuning of the optimal weighting factors. Simulation and experimental results demonstrate that the proposed control scheme can solve the design problem of weighting factors, meet the real-time requirements of the inverter system, and exhibit excellent dynamic and steady performance.

The hot-rolled strip steel is a critical process in steel production, where equipment stability directly impacts product quality. Traditional evaluation methods struggle to balance uncertainties between influencing factors and expert judgments. This study proposes a novel fault evaluation framework: first, a cloud rough model is adopted to handle multi-source uncertainties in expert evaluations; second, game theory is used to optimize the combined weights of subjective and objective criteria; finally, an improved Grey Relational Analysis-Technique for Order Preference by Similarity to Ideal Solution (GRA-TOPSIS) method constructs a dual-scale measurement model by fusing grey relational degree and Euclidean distance, effectively reducing the influence of geometric sequence similarity and distance on fault prioritization and enhancing the robustness of ranking results under complex working conditions. A case study on hot rolling loopers demonstrates a diagnostic accuracy of 90%. Comparative analyses with seven typical multicriteria decision-making methods indicate the proposed method's feasibility and effectiveness in practical applications, offering a novel reference for equipment status evaluation.

In traditional model predictive control (MPC) methods, the power prediction of the state quantity is not only limited by the measurement of the voltage sensor, but also the open-loop control. In order to boost the overall prediction accuracy without AC voltage sensor, an improved sliding mode estimation-model predictive power precision control strategy without AC voltage sensor was proposed. In this paper, the voltage on the network side is dynamically estimated by using sliding mode sigmoid function, and the system stability is proved by the Lyapunov function. On this basis, the active power and reactive power at time k+2 are calculated through the designed rolling optimization prediction link with feedback correction, aiming to reduce power pulse vibration and accurate prediction. The simulation and experimental results show that the proposed control strategy effectively suppresses the influence of grid-side harmonics on the prediction accuracy and improves the overall of the dynamic and static performance of the system.

Dear Editor, This letter presents an intelligent model predictive control algorithm inspired by biological regulatory mechanism and operational research. In terms of overall architecture, based on biological regulatory system and operational research theory, priority factor module and central coordination module are innovatively added on the basis structure of heuristic dynamic programming to carry out overall regulation of the system. In internal structure, the neural network is integrated with the biofeedback mechanism, and a new multi-level feedback neural network that can obtain more feedback information is proposed. The network is applied to the model network, action network and critic network of the algorithm. The convergence speed is greatly improved and the predictive control speed for nonlinear time-varying systems is improved on the premise of ensuring the control accuracy. The effectiveness and superiority of the proposed algorithm in prediction and control are verified by experiment.