Low-complexity model predictive control method for hybrid six-level converters

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.

The model predictive control method can be effective for converter control in distributed power generation but requires a large amount of computation, leading to a considerable time delay in the actuation. If the delay is not reflected, the system performance could get worse. This paper presents a two-step (horizon) prediction algorithm of model predictive control technique for grid-tied inverters used in wind turbine systems. The control objectives such as active and reactive power flow and switching loss reduction are reflected in the objective function of the controller. The proposed model predictive control strategy is verified numerically by using MATLAB/Simulink.

The paper presents a new multi-objective modulated model predictive control (MMPC) method for power converters with four different model states. The proposed method is based on a new concept of composing an error vector with fundamental state vectors. It can significantly minimize voltage and current ripples while retaining the nonlinear and dynamic performance of conventional MPC methods. Simulation and experimental results on a SiC-based 3-level flying capacitor(FC) converter are provided to validate this concept, and indicate a much reduced control error with the new method.

Traffic congestion has become a significant issue in urban road networks. There have been massive works about traffic signal optimization to improve the efficiency of traffic flow operation, and the so-called back-pressure control policy has proven to be excellent for oversaturated conditions. Most of the existing works with back-pressure are based on an adaptive phase sequence, and research with cyclic phase sequence is based on calculating the splits for different phases using the traffic flow data at the beginning of each cycle, which is unfair for the non-initial phases. In this paper, we propose a decentralized model predictive signal control method with fixed phase sequence using back-pressure policy. The main idea of the new method is to form a control loop using the model predictive control, enabling the system to obtain real-time feedback from the traffic network and dynamically adjusting signal timing plans at the beginning of each phase. As links within a certain area have various lengths, the same queue length can imply different traffic conditions, so a method to normalize queue lengths is proposed. The normalized queue length decreases drastically when the actual length approaches link capacity, thus avoiding spillover. The proposed method was tested in a virtual road network. Numerical results suggest that the new method improves performance under congested conditions in terms of throughput, Gini coefficient and comprehensive transportation efficiency.

This paper presents centralized control for a wind farm using model predictive control (MPC) method. In order to solve intra-day unit commitment (UC) problem, the UC problem is solved by using the MPC method with short-term wind power forecasting. We introduce a new dynamics model for the UC with some constraints to utilize the benefits of the MPC method. The objective function considering the operation and maintenance costs is formulated by adding a new variable in order to average the operating time of each wind turbine (WT) within the whole time. The proposed method could solve the UC problems on-line using the prediction time horizon that could be selected flexibly considering time horizon based on wind power forecasting errors. From the simulation study using 10 WTs, we observed that the proposed dynamics model of UC effectively provided the optimal solution to each scenario. Numerical study will validate that the proposed method can be applied to solving the UC problem of a large scale wind farm by aggregating WTs.

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

For multiobjective model predictive current control (MPCC) method with penalties, the penalty weights are introduced into the cost function to determine the relative importance of control targets. However, the dimensions and value ranges of each control target are different. Thus, the penalty coefficient is difficult to determine. To avoid the difficulty of dimensionless parameter design, this article proposes a multiobjective control method called MPCC with multiple current bounds. In this article, the switching frequency and the common-mode voltage (CMV) are selected as the control target. Their control is converted into the limitation of the current ripple so that the control parameters have a clear physical meaning. On the one hand, the proposed method can realize the cooperative control of reference current tracking, switching frequency suppression, and CMV suppression. On the other hand, the key control parameters have the same dimension and strong correlation with others. Therefore, appropriate parameter design can be realized. Experimental investigations for permanent magnet synchronous motor prove the effectiveness of the control method.

This paper presents a reduced control set model predictive control (RCSMPC) method for three-phase T-type neutral-point-clamped (NPC) inverter. The whole control set (WCS) consists of all the 27 switching states of T-type NPC inverter. The reduced control set (RCS) with 19 switching states is formed from WCS by excluding the switching states with common mode voltage (CMV) value higher than one-sixth of input DC voltage [Formula: see text]. With RCS, single-objective model predictive current control method can restrict the CMV peak value to [Formula: see text]. To further reduce the CMV below this threshold, a cost function with the weighted sum of two control targets is formulated in the RCSMPC method. The two control targets of RCSMPC method are CMV mitigation and load current control. The weight for CMV is called bias factor. The RCSMPC method is computationally efficient, as the number of switching states is less than that of WCSMPC. To further reduce the computational burden, CMV values corresponding to all the switching states are calculated offline and stored in memory. Robustness of both the methods is investigated with parameter deviations at different bias factors and reference currents. The proposed method is validated using simulation and experimental results and compared with the existing methods.

This paper describes a new model predictive control method for buck DC-DC converters. The target is to achieve a good trade-off between fast response and low overshoot in the transient response of output voltage. In the well-known PWM-PI method, such trade-off is difficult to attain. The proposed model predictive control is based on an analytic algorithm which can be easily implemented on-line. Further, the advantage of the proposed model predictive control is verified experimently. Experiment result shows that the proposed method outperforms the PWM-PI method.

An economic dispatch (ED) problem of committed turbines is solved minimizing each wind turbine's output power variation by model predictive control (MPC) method. The proposed method could solve the ED problem on-line using prediction time horizon that could be selected flexibly considering time horizon based on wind forecasting errors. Dispatched wind turbines are classified into three groups considering variations of forecasted power generation: maximum power point tracking, balance control, and delta control. From the simulation study using 10 wind turbines we observed that the proposed method effectively provided the optimal solution to the scenario.

The general model predictive current control uses a discrete-time model of the system to predict the future current behavior for all the possible voltage vectors generated by the inverter, and then the vector which minimizes a cost function is selected and applied. In this paper, a simplified model predictive current control method for a three-phase voltage-source inverter is presented in order to reduce the amount of calculations in the practical implementation of this method, especially for multilevel ones with large predictions. Utilizing the sector information of the reference voltage space vector, the proposed method just needs a subset of all the available voltage vectors for the prediction and optimization. The computational effort has been greatly reduced while the steady-state performance and dynamic response of the current control are improved. In addition, a reduced switching frequency is obtained. Simulation results are presented to verify the proposed method.

This paper presents the dynamic modeling and predictive control method and the controller design of an electric driven eight-wheeled differential steering autonomous vehicle. First, the dynamic model of autonomous vehicle is built. Then, the model predictive control method is introduced. Consequently, the path tracking controller of autonomous vehicle is designed on the basis of dynamic model and predictive control method. Finally, the real vehicle experiment is performed at a middle speed up to 60km/h on a two kilometers long highway. The experimental results show a mean lateral error less than 0.3m and heading error less than 2 deg when follow the given refer path. The experiment also proofs the controller has certain anti-interference ability, which demonstrates the effectiveness of proposed control method and autonomous vehicle path tracking controller.

A novel isolated 7‐level (7L) multi‐level inverter (MLI) topology for grid‐connected medium and high voltage power applications is presented in this paper. The topology consists of two isolated transformers with different winding ratio and four switches connected to a single‐phase grid. This topology provides a great advantage over conventional MLIs by reducing passive and active components to allowing a 7L output voltage. Moreover, model predictive control (MPC) method is implemented to 7L‐MLI to generate gate signals of switches and ensure capacitor voltage balancing and grid connection. Performance of proposed topology has been confirmed by in‐depth analysis under simulation and laboratory setup with existing studies. Experimental results of a 1.5 kW 220 Vrms prototype are presented to support the simulation results. The prototype shows that the harmonic results match the limits of the IEC 61000‐3‐2 standard and show a better performance for grid connection and capacitor voltage balancing than conventional ones.

This paper presents a model predictive voltage control method for single-inductor multiple-output (SIMO) DC/DC converters operating in the continuous conduction mode (CCM). The state-space model for a SIMO DC/DC converter system is formulated and a Model Predictive Control (MPC) strategy, which has the capability to track the reference voltage automatically and reduce the impact of cross regulation problem, is proposed to regulate the system. Considering that the switches in the converter only have ON/OFF modes, the quadratic programming algorithm is employed to obtain the MPC's solution. Simulation studies are conducted in MATLAB/Simulink to verify the performance of the proposed method.

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources.