Earticle

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.

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix Ar by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

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.

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.

The bottom hole pressure should be controlled during the pipe connection procedure in a drilling operation to prevent the possibility of oil well blow out, to avoid the collapse of the well and to prevent the fracturing of the bore walls. It should be maintained within a window defined by the collapse pressure as the lower limit and by the fracture pressure as the upper limit. This paper describes the use of a nonlinear model predictive controller (NMPC) to maintain the bottom hole pressure. In addition to a topside choke valve, backpressure pump is also used as a control input. While connecting an additional pipe segment, the injection of the drill fluid is completely stopped. The drill fluid pulse telemetry system that is used in the measurement and transmission of the bottom hole pressure may not function properly and there will be absence of signal while the pipe is being connected. A nonlinear estimator, the unscented Kalman filter (UKF) is used for the continuous estimation of the fluid flows and pressures at different sections of the well being drilled. Simulation results show that the bottom hole pressure can be managed effectively during the drilling of an oil well with nonlinear estimation and control.

Electric vehicles (EVs) charging algorithms considering a photovoltaic (PV) system have been studied in this paper. The power needed to charge the EVs comes from grid-connected photovoltaic generation or the utility. This work includes the issues such as how to maximize photovoltaic generated power and reduce energy cost of EVs charging, that is important for the integration of efficient EVs charging stations in smartgrids. A model predictive control (MPC) with linear programming (LP) is used for optimal control, and the time-of-use (TOU) price is included to calculate the energy costs. Simulation results show that the reductions of energy cost can be achieved using the proposed algorithms

The paper presents a model predictive control method for continuous-time systems based on subspace identification. It’s developed by reformulating the continuous-time systems using Laguerre filters to obtain the subspace prediction output. Then, the subspace predictors are derived by QR decomposition from input-output and Laguerre matrices. The subspace predictive controller is designed with the subspace predictors. The process control simulations of a fermentation bioreactor system show the effectiveness of the proposed method.

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.

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 proposes a model predictive control (MPC) algorithm designed for nine-switch converter with output filter (NSCOF) to control two three-phase AC loads independently. A state function of each leg of nine-switch converter (NSC) is established to characterize the internal conditions of the NSC because the traditional model of the NSCOF does not involve the interior model of the NSC. Based on the NSCOF model with the state function of each leg, a prediction model of NSCOF is constructed. The two AC terminals of NSCOF are treated as one module when the MPC is designed. The proposed MPC achieves controlling two three-phase AC loads working under different frequency modes. Furthermore, this algorithm is independent of the modulation method for reducing the difficulty of the controller design and its limits. The simulation and experimental results reveal that the steady-state and dynamic response performance of NSCOF are substantially improved using MPC.

A typical battery energy storage system consists of a combination of battery packs and a grid-tied power conversion system. The control algorithm of the power conversion system plays an important role when interfacing the DC energy stored in battery packs with the conventional AC grid to generate an obedient bidirectional power flow. Finite control set model predictive control is believed to be one of the most effective choices for controlling power conversion systems. However, the performance of such a control strategy heavily depends on the accuracy of the predictive model. Parameter mismatch in the model leads to prediction error, which deteriorates the overall power quality performance of the power conversion system. Therefore, this paper studies a robust finite control set model predictive control method based on a discrete disturbance observer to eliminate the negative effects caused by model inaccuracy and uncertainty. The stability issue of the additional observer is discussed from the perspective of closed-loop poles. Parameter scan is performed to provide assistance in designing the feedback matrix. Finally, simulations and experimental results obtained from a downscaled prototype rated at 4.2 kVA are conducted as a validation of the presented control algorithm.

Output voltage control is an important subject in magnetically coupled wireless power transfer (MC-WPT) applications. Conventional control methods for MC-WPT systems use the PI controller. However, this method suffers from three issues: time-consuming tuning work of the PI parameters, increased control complexity due to the needs of the modulator, and poor dynamic performance. To provide an attractive alternative to the PI controller, a novel output voltage regulation method based on finite-control-set model predictive control (FCS-MPC) has been proposed for a MC-WPT system. The proposed method has inherent advantages such as a very intuitive concept, no need for a modulator, and fast dynamic response. Moreover, it can achieve soft switching by constructing pulse-density-modulation-based voltage pulse sequences as the control set. The design and implementation of the proposed controller are discussed in this paper. The proposed control method has been tested on a series-series-compensated MC-WPT system, and experimental results demonstrate the effectiveness of the proposed control method in comparison with PI control methods.

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.