Model Predictive Control with Reach-avoid Analysis

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 chapter presents the results of experiments comparing different computationally efficient model predictive control (MPC) methods applied to a laboratory device, demonstrating the active vibration control (AVC) of lightly damped mechanical structures. Because of the combination of long prediction horizons, short sampling times and large actuator-disturbance asymmetry, the implementation of the predictive control strategy on lightly damped vibrating structures is highly demanding. The vibration damping effect and online timing properties of model predictive control algorithms such as infinite horizon cost dual-mode quadratic programming based MPC (QPMPC), pre-computed explicit multi-parametric programming based MPC (MPMPC), minimum-time MPMPC and the very efficient but suboptimal Newton–Raphson based MPC (NRMPC); all with guaranteed stability and constraint feasibility are analyzed in different disturbance and loading scenarios. All MPC methods along with the baseline linear quadratic (LQ) controller decrease vibration settling to equilibrium by an order of magnitude time. The damping effect of all investigated MPC strategies is comparable with a slight decrease in performance for the suboptimal minimum-time MPMPC and NRMPC controllers. Due to the excessive online computational needs of QPMPC, it is a very unlikely candidate for lightly damped vibrating systems given currently available hardware. The online timing analysis presented here demonstrates that MPMPC provides significantly shorter online execution times, however its suboptimal minimum-time version does not bring a convincing improvement. NRMPC can provide online execution times on par with linear quadratic controllers; however, its suboptimality becomes excessive with increasing prediction model orders.

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 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.

In this paper, using the two control methods of model predictive control (MPC), model predictive current control (MPCC) and model predictive torque control (MPTC) is applied to the control system of permanent magnet synchronous motor(PMSM). The algorithms are based on the minimization of different cost functions, and the corresponding optimal voltage vector can be obtained. Comparison of the dynamic and steady state performance of the tested PMSM with MPCC and MPTC is elaborated. The delay compensation is considered to reduce the fluctuation of current and torque, in order to reduce the computational burden by using a single vector, comparison of two methods in the speed control system of dynamic and steady-state performance and speed tracking performance and resistance to load disturbance. The simulation results show that both MPCC and MPTC can be well applied to the PMSM driving systems with good speed tracking performance. However the MPCC can achieve less harmonics in the stator currents, and consequently the circular flux linkage trajectory is smoother, while the MPTC shows advantages in smaller ripples in the mechanical torque.

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.

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.

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.

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 two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal solution for the integrated optimal control problem (OCP), we first build an MPC, referred to as a joint MPC, in which the goal is to minimize battery energy consumption while maintaining cabin-cooling comfort. Second, we divide the integrated OCP into two small-scale problems and devise a co-optimization MPC (co-MPC), where speed planning on hilly roads and cabin-cooling management with propulsion power information are addressed successively. Our proposed MPC methods are then validated through two case studies. The results show that both the joint MPC and co-MPC can produce significant energy benefits while maintaining driving and thermal comfort. Compared to regular constant-speed cruise control that is equipped with a proportion integral (PI)-based AC controller, the benefits to the battery energy earned by the joint MPC and co-MPC range from 2.09% to 2.72%. Furthermore, compared with the joint MPC, the co-MPC method can achieve comparable performance in energy consumption and temperature regulation but with reduced computation time.

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.

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.

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.

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.

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.