Model Predictive Control Method of Separating Trucks from Passenger Vehicles at Freeway

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.

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.

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

This work deals with the feedback operation strategy of a low-voltage distributed generation system composed by a three-phase four-leg inverter connected to a typical three-phase four-wire grid-utility in a PCC (Point of Common Coupling). The desired modulation strategy and switching pattern for the four-leg inverter is determined by the Model Predictive Control (MPC) method. Some reasons for choosing the control method and the inverter topology are presented and discussed. The rules of the MPC are defined so that the load is supplied in full and that the generator power factor is maximized. A total of four cases of local loads are considered to validate the proposed control method.

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 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.

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 proposes a model predictive controller for two-stage inverter with less switching losses and capacitor current. In implementation, model predictive control (MPC) method is simple and flexible without assuming the traditional PWM block. As the capacitor current has great influence on the capacitor losses and lifetime, an adjustment before the switching signals are sent to the switches is added in the proposed MPC method to reduce the capacitor current RMS value. The decrease of capacitor current RMS value enables the reduction of capacitor losses and the increase of capacitor lifetime. Besides, the proposed method can achieve less switching losses of front-end dc-dc converter, compared with the conventional MPC method. This paper presents the detailed operational principle of proposed MPC method and compares the performance of conventional and proposed MPC methods using the same system parameters. Finally, the simulation and experimental results verified the validity and advantage of the proposed method.

This paper proposes a time-lag model predictive control (MPC) method to solve the problem of communication delay in networked automatic generation control (AGC). The method uses the optimum weighting method to predict delay and the time-lag dynamic state equation of interconnected grid as the MPC prediction model. Then the deviation of predicted output is added to the objective function of rolling optimization in MPC method. The backup suboptimal control command from MPC controller can handle the loss of data packet in networked AGC. The correctness of the proposed method is verified by dynamic simulation.

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.

This paper proposes a highly efficient single-phase three-level neutral point clamped (NPC) converter operated by a model predictive control (MPC) method with reduced commutations of switches. The proposed method only allows switching states with none or a single commutation at the next step as candidates for future switching states for the MPC method. Because the proposed method preselects switching states with reduced commutations when selecting an optimal state at a future step, the proposed method can reduce the number of switchings and the corresponding switching losses. Although the proposed method slightly increases the peak-to-peak variations of the two dc capacitor voltages, the developed method does not deteriorate the input current quality and input power factor despite the reduced number of switching numbers and losses. Thus, the proposed method can reduce the number of switching losses and lead to high efficiency, in comparison with the conventional MPC method.

In this paper, a modified model predictive control method has been proposed for voltage control of an inverter in an islanded microgrid. The main objective of this method is to reduce voltage THD and the reference tracking error. In the proposed scheme, some vectors are added to existing vectors, available in FCS-MPC method to reduce the error between the reference voltage and the output voltage. In this control scheme, the model of the system is used to predict the behavior of the output voltage. Then based on a quadratic cost function, the predictions are evaluated to select the optimized control signal which should be applied to the system in the next sampling interval. The performance of the proposed control scheme is evaluated using MATLAB/Simulink environment.

To improve the load frequency control (LFC) performance of power systems incorporating virtual power plants (VPPs) while reducing network resource consumption, a model predictive control (MPC) method based on a mixed time/event-triggered mechanism (MTETM) is proposed. This mechanism integrates an event-triggered mechanism (ETM) with a time-triggered mechanism (TTM), where ETM avoids unnecessary signal transmission and TTM ensures fundamental control performance. Subsequently, for the LFC system incorporating VPPs, a state hard constrained MPC problem is formulated and transformed into a “min-max” optimisation problem. Through linear matrix inequalities, the original optimisation problem is equivalently transformed into an auxiliary optimisation problem, with the optimal control law solved via rolling optimisation. Theoretical analysis demonstrates that the proposed auxiliary optimisation problem possesses recursive feasibility, whilst the closed-loop system satisfies input-to-state stability. Finally, validation through case studies of two regional power systems demonstrates that the MPC approach based on MTETM outperforms the ETM-based MPC approach in terms of control performance while maintaining a triggering rate of 33.3%. Compared with the TTM-based MPC algorithm, the MTETM-based MPC method reduces the triggering rate by 66.7%, while maintaining nearly equivalent control performance. Consequently, the results validate the effectiveness of the MTETM-based MPC approach in conserving network resources while maintaining control performance.

A novel method of neural network model predictive control integrated process monitoring and applications to hot rolling process

This article proposes a dynamic characteristic improvement of phase-shift full-bridge center-tapped (PSFB-CT) converter using a model predictive control (MPC) method. The PSFB-CT converter is widely used for low-voltage dc–dc converters in electric vehicles. In general, its output voltage is a control object which is usually controlled using a proportional–integral control-based output voltage controller and current controller. However, it has a disadvantage such as undesirable dynamic characteristic of the PSFB-CT converter and the necessity of gain tuning. Especially, if the gain of the PI control is increased to improve the dynamic characteristic of the PSFB-CT converter, an overshoot of the output voltage can occur. In this article, the advanced voltage control using the MPC method is proposed to improve the dynamic characteristic of the PSFB-CT converter without overshoot of the output voltage. As a result, the proposed method has advantages with not only fast dynamic characteristic but also robustness from a sudden change of load impedance. The effectiveness of the proposed MPC method was proved by the simulation and experimental results.