Earticle

This paper presents methods of model predictive control (MPC) for eco-driving minimizing trip-time and energy- consumption of electric vehicles (EVs). Considering both non- convex and convex optimization problem formulations for MPC- based eco-driving, we compare the performances of nonlinear and linear MPC solutions for high-level planning of vehicle speed and charging in a driving simulation of a Munich– Cologne trip (573 km). The linear MPC can be considered as a convex quadratic programming approximation (i.e., convexified quadratic program) of the original nonlinear MPC, but its performance of optimality is shown to be comparable to the nonlinear counterparts whereas its computation speed is one order of magnitude faster.

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method.

Forecasting quality of complex products is a major concern for quality engineers and enterprises' decision-makers. But only a few researchers have investigated how multiple linear regression analysis can improve forecasts. This article presents a predictive control model for personalized forecasting system of complex product quality using multiple linear regression analysis. First, this paper compares the performance of personalized forecasting system for product quality which leads to a better understanding of the applicability of the forecasting model. Second, this paper identifies the effect of predictive control model of multiple linear regression analysis on forecasting accuracy. Then, an example shows the feasibility of using predictive control model in complex product quality personalized forecasting. Finally, it makes a conclusion that the approved prediction system embraces superiority over the prediction of the quality factors and manufacturing resources.

This paper presents the method of multivariable integrated model predictive control for the nuclear power plant, including designing controller and building the modeling of turbine and once-through steam generator. The simulation results show that the rotate speed of turbine and outlet pressure of steam generator under the multivariable integrated model predictive control is faster steady and smaller overshoot than under the PID control, when the power load of nuclear power plant is changed. Simulation test indicates that the multivariable integrated model predictive control can obtain better control performance for nuclear power plant.

This paper addresses an optimal train trajectory planning and tracking problem for automatic train operation (ATO) with consideration of the train model uncertainty and constraints. Based on the discrete linear multi-points train model, an ATO control algorithm is presented to track piecewise reference by using model predictive control with constraints tightening such that the feasibility and robust convergence of this algorithm are guaranteed under the varying running resistance, automatic train protection (ATP) constraint, and train motor physical limits. Specifically, the features of the algorithm are: (i) taking traction and braking force of locomotives and braking force of carriges into account explicitly; (ii) integrating constraints tightening approach into piecewise reference tracking problem to ensure robustness; (iii) combining the optimal planning level and tracking control level together. Finally two case studies are conducted to verify the effectiveness of the algorithm.

Model predictive control is the family of controllers, makes the explicit use of model to obtain control signal. The reason for its popularity in industry and academia is its capability of operating without expert intervention for long periods. There are various control design methods based on model predictive control concepts. This paper provides review of the most commonly used methods that have been embedded in an industrial model predictive control. The most widely used strategies as Dynamic matrix control (DMC), Model algorithmic control (MAC), Predictive functional control (PFC), Extended prediction self-adaptive control (EPSAC), Extended horizon adaptive control(EHAC) and Generalized predictive control(GPC) have been described with history, basic idea, properties, and their controller formulation.

A model predictive control method is proposed for the point stabilization of wheeled mobile robots (WMRs) subject to nonholonomic constraints. The problem is simplified by considering only the steering system and neglecting the vehicle dynamics. A linearized error model is then formulated by transforming the robot position into polar frame. The feedback control policy is obtained by minimizing a quadratic cost function which penalizes the predicted errors and control variables in each sampling time over a finite horizon. The proposed control law is proven to guarantee the exponential stability of the robot system by considering additive inequality constraints in the optimization process. The performance of the stabilization algorithm is verified through computer simulations showing that the proposed method has a good regulation performance and convergence.

To improve the accuracy of model predictive control, this paper presents an improved multiple cell linear parameter varying model predictive control method for carrier-based aircraft. After establishing the lateral dynamic model of carrier-based aircraft for multiple cell predictive controller, the output-feedback linear parameter varying control based on states observation should be implemented. The model simulation results indicate the better performance of the new method in comparison with the traditional controller with more accuracy and practicability.

The modular multilevel converter (MMC) has been widely researched in the past decade for its superior performance in high voltage high power applications. However, applying MMC into electric motor drive systems is a intrinsically challenging problem because the capacitor voltages fluctuate in a wide range when the converter works at low output frequencies. A novel model predictive vector control (MPVC) scheme is presented to ensure the output phase currents’ quality of MMC for entire frequency range. In this scheme, model predictive control (MPC) is adopted to track the phase currents’ reference values and balance the capacitor voltages as far as possible. By replacing the conventional inner current control loops and pulse width modulation (PWM) module with MPC controllers, the vector control oriented by rotor flux is constructed to meet the requirements of drive systems. Simulations of a MMC drive with an induction motor are carried out using the Matlab/Simulink. The simulation waveforms demonstrate that the drive system achieves satisfied steady and dynamic performance.

부스트 PFC (Power Factor Correction)컨버터는 AC 입력 전류의 단일 역률과 낮은 THD (Total Harmonic Distortion)를 달성하기 위해 다양한 제어기법들이 연구되고 있다. 그중 인덕터 전류의 평균값을 전류지령에 추종하도록 제어하는 평균전류 모드 제어가 있으며 가장 널리 사용되고 있다. 하지만, 오늘날 디지털 프로세서의 발달로 고도화된 디지털 제어가 가능해지면서 부스트 PFC 컨버터의 예측제어가 관심을 받고 있다. 예측제어에는 예측 알고리즘으로 듀티를 미리 생성하는 예측전류 모드 제어 및 모델을 기반으로 한 비용함수를 선정하여 스위칭 동작을 하는 모델예측제어로 분류된다. 따라서 본 논문에서는 부스트 PFC 컨버터의 평균전류 모드 제어, 예측전류 모드 제어, 모델예측 전류 제어를 간단히 설명한다. 또한, 시뮬레이션을 통해 전체 부하 및 다양한 외란 조건에서의 전류 제어를 비교 분석한다.

For boost PFC (Power Factor Correction) converters, various control methods are being studied to achieve unity power factor and low THD (Total Harmonic Distortion) of AC input current. Among them, average current mode control, which controls the average value of the inductor current to follow the current reference, is the most widely used. However, nowadays, as advanced digital control becomes possible with the development of digital processors, predictive control of boost PFC converters is receiving attention. Predictive control is classified into predictive current mode control, which generates duty in advance using a predictive algorithm, and model predictive current control, which performs switching operations by selecting a cost function based on a model. Therefore, this paper simply explains the average current mode control, predictive current mode control, and model predictive current control of the boost PFC converter. In addition, current control under entire load and disturbance conditions is compared and analyzed through simulation.

This paper proposes a fault-tolerant control strategy with finite control set model predictive control (FCS-MPC) based on hierarchical optimization for five-level H-bridge neutral-point-clamped (5L-HNPC) inverter-fed induction motor drives. Fault-tolerant operation is analyzed, and the fault-tolerant control algorithm is improved. Adopting FCS-MPC based on hierarchical optimization, where the voltage is used as the controlled objective, called model predictive voltage control (MPVC), the postfault controller is simplified as a two layer control. The first layer is the voltage jump limit, and the second layer is the voltage following control, which adopts the optimal control strategy to ensure the current following performance and uniqueness of the optimal solution. Finally, simulation and experimental results verify that 5L-HNPC inverter-fed induction motor drives have strong fault tolerant capability and that the FCS-MPVC based on hierarchical optimization is feasible.

This paper proposes a new method of Model Predictive Control (MPC) of nonlinear process by us-ing the measured Volterra kernels as the nonlinear model. A nonlinear dynamical process is usually de-scribed as Volterra kernel representation, In the authors' method, a pseudo-random M-sequence is ar plied to the nonlinear process, and its output is measured. Taking the crosscorrelation between the input and output, we obtain the Volterra kernels up to 3rd order which represent the nonlinear characteristics of the process. By using the measured Volterra kernels, we can construct the nonlinear model for MPC. In applying Model Predictive Control to a nonlinear process, the most important thing is, in general, what kind of nonlinear model should be used. The authors used the measured Volterra kernels of up to 3rd order as the process model. The authors have carried out computer simulations and compared the simulation results for the linear model, the nonlinear model up to 2nd Volterra kernel, and the nonlinear model up to 3rd order Vol-terra kernel. The results of computer simulation show that the use of Valterra kernels of up to 3rd order is most effective for Model Predictive Control of nonlinear dynamical processes.

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

A subspace-based identification method of the Wiener model, consisting of a state-space linear block and a polynomial static nonlinearity at the output, is used to retrieve from discrete sample data the accurate information about the nonlinear dynamics. Wiener model may be incorporated into model predictive control (MPC) schemes in a unique way which effectively removes the nonlinearity from the control problem, preserving many of the favorable properties of linear MPC. The control performance is evaluated with simulation studies where the original first-principles model for a continuous MMA polymerization reactor is used as the true process while the identified Wiener model is used for the control purpose. On the basis of the simulation results, it is demonstrated that, despite the existence of unmeasured disturbance, the controller performed quite satisfactorily for the control of polymer qualities with constraints.

This paper presents a finite control set model predictive control (FCS-MPC) strategy for the AC/DC matrix converter used in grid-connected battery energy storage system (BESS). First, to control the grid current properly, the DC current is also included in the cost function because of input and output direct coupling. The DC current reference is generated based on the dynamic relationship of the two currents, so the grid current gains improved transient state performance. Furthermore, the steady state error is reduced by adding a closed-loop. Second, a Luenberger observer is adopted to detect the AC input voltage instead of sensors, so the cost is reduced and the reliability can be enhanced. Third, a switching state pre-selection method that only needs to evaluate half of the active switching states is presented, with the advantages of shorter calculation time, no high dv/dt at the DC terminal, and less switching loss. The robustness under grid voltage distortion and parameter sensibility are discussed as well. Simulation and experimental results confirm the good performance of the proposed scheme for battery charging and discharging control.

In this paper, we consider a robust output feedback model predictive controller(MPC) design for Wiener model. Nonlinearities that couldn't be represented in static nonlinearity block of Wiener model are regarded as uncertainties in linear block. An dynamic output feedback controller design method is presented for Wiener MPC. According to MPC algorithm, the control law is computed based on linear matrix inequality(LMI)at each sampling time by solving convex optimization. Also, a new parameter dependent Lyapunov function is proposed to get a less conservative condition. The results are illustrated with numerical example.

This paper proposes a model predictive control based on the discrete Lyapunov function to improve the performance of power electronic converters. The proposed control technique, based on the finite control set model predictive control (FCS-MPC), defines a cost function for the control law which is determined under the Lyapunov stability theorem with a control error compensation. The steady state and dynamic performance of the proposed control strategy has been tested under a single phase AC/DC voltage source rectifier (S-VSR). Experimental results demonstrate that the proposed control strategy not only offers global stability and good robustness but also leads to a high quality sinusoidal current with a reasonably low total harmonic distortion (THD) and a fast dynamic response under linear loads.