Model Predictive Control of Double Stacked Suspension System

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 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.

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.

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.

A discretized rotor reference frame dq-model of the PMSM is the basis of Model Predictive Control (MPC) and Deadbeat Predictive Control (DBC). A modified self-tuning field orientation control (FOC) of PMSM was developed for comparison with the predictive control methods. Since torque is proportional to q-axis current and control of current results in control of torque, speed, and rotor position, the MPC, DBC, and FOC algorithms ensure that the actual motor dq-currents track their respective references. The models were implemented in MA TLAB/Simulink. The results show effective tracking of current and speed references with minimal transients. The MPC showed the fastest speed settling time of 0.025 seconds (87.5% faster) and about 16% overshoot while the DBC has a settling time of 0.12 seconds (40% faster) with no overshoot. The FOC has its settling time as 0.2 seconds with no overshoot. Compared to FOC, both developed models have improved performance.

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.

Under unbalanced dc links, the control of a three-phase Vienna rectifier is challenging. To deal with the unbalanced dc links, the optimal switching sequence model predictive control (MPC) regulates the neutral-point (NP) voltage through preselecting a redundant vector and grid current by reconstructing duty cycles of the optimal switching sequence. However, its one-redundant-vector-per-control-interval characteristic leads to large NP voltage ripple. To solve this problem, in this article, a model predictive duty cycle control method is proposed for three-phase Vienna rectifiers under unbalanced dc links. First, we study the impacts of duty cycles on the variations of instantaneous current, and then, the three-phase duty cycles are derived by predefined objective function minimization. Second, considering both the operational characteristics and unbalanced dc links of the Vienna rectifier, a new modification method for duty cycles is presented. This method ensures the grid current quality and decouples the control of grid current and NP voltage. Finally, an MPC-based NP voltage control strategy is proposed to reduce the NP voltage ripple. Experimental results demonstrate that the presented method can effectively reduce the NP voltage ripple.

Model Predictive Control (MPC) has the advantage of including constraints into the optimization. By combining MPC with Feedback Linearization (FBL) it is possible to use linear, discrete time MPC algorithms with nonlinear models. This way the constraints on the control systems, thrusters and aerodynamic surface deflections, and on the attitude of the vehicle can be integrated into the controller synthesis. The main disadvantage of this type of controller is the lack of robustness when uncertainties enter the system. This paper therefore improves the nominal FBL-MPC controller by replacing the Quadratic Programming algorithm with a minmax MPC technique involving Linear Matrix Inequalities. Uncertainties on the nonlinear aerodynamic coecients are used as a source of disturbances. By mapping these uncertainties to a linear state space model as used by the MPC controller, a polytopic set of uncertainty models is created that can be used by the min-max algorithm to minimize the performance cost over the worst case model from the uncertainty set. This approach will be illustrated using a model of the X-38 re-entry vehicle flying along a predefined trajectory with active input and state constraints. The results show that the robustness is improved significantly when compared to the nominal controller with the same uncertainties.

Modeling, estimation, and control in large-scale urban road networks with remaining travel distance dynamics

Predictive power management strategy for PV/battery hybrid unit based islanded AC microgrid

Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot

This paper presents a simplified finite‐control‐set model predictive control (S‐FCS‐MPC) based on fast vector selection method in a three‐level VIENNA rectifier. This method features a high‐power factor, a low input current THD as a well‐controlled DC‐link voltage with fewer redundant vectors and lower computational load. Moreover, the converter with the proposed control technique exhibits a faster dynamic response in terms of input current and DC‐link voltage compared with conventional finite‐control‐set model predictive control (C‐FCS‐MPC). In addition, the average switching frequency can be effectively reduced due to fewer switching times in a subset sector using the proposed method, which means fewer switching losses. Finally, the operation principle of the proposed algorithm has been analysed and an execution time comparison between S‐FCS‐MPC and C‐FCS‐M has been undertaken. The effectiveness of the proposed control technique has been validated using both simulation and experimental results.

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.

Unmanned logistics vehicle (ULV) realize the automation and intelligence of cargo transportation, which improves the efficiency, cost-effectiveness and safety of logistics and distribution, while the trajectory tracking control of ULV is the key technology to ensure their safe and efficient delivery of goods. In order to solve the trajectory tracking problem of ULV in the process of delivering goods, this paper proposes a model predictive control (MPC) method based on event-triggered and fuzzy adaptive optimization parameters. Firstly, the dynamics model of the ULV is established. Secondly, an event-triggered mechanism is introduced to establish ET-MPC, while a disturbance observer is designed considering the external disturbance and the controller calculation discarding the nonlinear term. Thirdly, the advantages of fuzzy control and MPC algorithms are integrated, and the four important parameters in the MPC controller are adaptively optimized by fuzzy control, and the improved MPC control strategy is designed. Finally, the CarSim-Matlab/Simulink co-simulation platform and the experimental vehicle platform are constructed to verify the effectiveness of the improved MPC trajectory tracking controller proposed in this paper. The results show that the improved MPC control strategy can reduce the computation time of the controller, and the total number of triggering times of the controller is reduced by 46.44% compared with the classical MPC, which reduces the computational complexity of the controller and improves the accuracy and smoothness of the trajectory tracking of the ULV.