Proposed Model Predictive Control Method Research Articles

Model predictive control based on time-varying efficiency for hydraulic hub-motor driving vehicle

A model predictive feedback control strategy based on time-varying efficiency is investigated and applied to a hydraulic hub-motor auxiliary system (HHMAS) in this paper. Adding HHMAS to a traditional heavy commercial vehicle can improve fuel economy and traction performance on roads with low adhesion coefficients. However, the hydraulic drive system experiences serious disturbance imposed by time-varying parameters and external conditions. Model predictive feedback control based on time-varying efficiency offers a solution for HHMAS to cope with the disadvantage of the hydraulic drive system and improve the environmental adaptability of the vehicle controller. In this study, the control law of hydraulic variable pump (HVP) target displacement is established based on temperature compensation in consideration of the influence of multiple factors on pump target displacement. For coordinated power distribution of HHMAS, the minimum wheel speed difference and the reduction in system impact are regarded as optimal control targets in adjusting the engine torque and HVP displacement and designing the model predictive controller. Simulation results show that the proposed model predictive control method can reduce the speed difference between front and rear wheels by up to 64% and can achieve the wheel speed following effect faster than the traditional proportional-integral-derivative algorithm. Given that the control parameters do not need to be calibrated in the proposed method, the calibration time is saved, and the actual development process of the hydraulic hub-motor driving vehicle is remarkably improved.

Dynamic Characteristic Improvement of Phase-Shift Full-Bridge Center-Tapped Converters Using a Model Predictive Control

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.

An Effective Model Predictive Control Method With Self-Balanced Capacitor Voltages for Single-Phase Three-Level Shunt Active Filters

This paper presents an effective model predictive control (MPC) method for single-phase three-level T-type inverter-based shunt active power filters (SAPFs). The SAPF using T-type inverter topology has not been reported in the literature yet. Contrary to most of the existing MPC methods, the proposed MPC method eliminates the need for using weighting factor and additional constraints required for balancing dc capacitor voltages in the cost function. The design of cost function is based on the energy function. Since the factor used in the formulation of the energy function does not have any adverse influence on the performance of the system, the cost function becomes weighting factor free. The weighting factor free based MPC brings simplicity in the practical implementation. The effectiveness of the proposed MPC method has been investigated in steady-state as well as dynamic transients caused by load changes. The theoretical considerations are verified through experimental studies performed on a 3 kVA system.

Model Predictive Control Method Based on Deterministic Reference Voltage for Single-Phase Three-Level NPC Converters

When single-phase three-level neutral-point-clamped (NPC) converters operate, there are two main control objectives that need to be met for correct operation. First, the ac source current must be controlled to be sinusoidal. Second, the dc capacitor voltages must be balanced. In original model predictive control (MPC) methods for NPC converters, an optimization process involving an empirical weighting factor design is required to meet both of these objectives simultaneously. This study proposes an MPC approach developed for single-phase three-level NPC converters to meet these objectives using a single reference voltage consisting of a difference-mode term and a common-mode term in each phase. The difference-mode term and the common-mode term are responsible for sinusoidal ac source current synthesis and dc capacitor voltage balancing, respectively. Then, a single cost function compares the adjusted reference voltage with possible voltage candidates to select an optimal switching state, resulting in the smallest cost function value. Different from the conventional MPC method, the proposed approach avoids the selection of weighting factors and the attendance of various control objectives. Thanks to the deterministic approach, the proposed MPC method is straightforward to implement and maintain fast transient performance while guaranteeing the control objectives. Finally, the effectiveness and feasibility of the proposed approach for single-phase three-level NPC are verified through comprehensive experimental results.

Model predictive control with fixed weighting factor for three‐phase four‐switch inverter‐fed PMSM drives considering capacitor voltage offset suppression

This study proposes a model predictive control (MPC) method with a fixed weighting factor for three-phase four-switch inverter-fed permanent magnet synchronous motor drives to suppress the capacitor voltage offset. The prediction model of the capacitor voltage difference is obtained based on the relationship between capacitor voltage and phase current. Then by analysing the relationship between basic voltage vector, electromagnetic torque and flux amplitude, the control of electromagnetic torque and flux in the model predictive torque control is transformed into the control of the voltage vector. The dimension of the capacitor voltage difference is the same as that of the voltage vector, hence the cost function with the fixed weighting factor is defined with the consideration of the capacitor voltage offset suppression. The effectiveness of the proposed MPC method is validated by the simulation and experiment.

Model Predictive Control of DC–DC SEPIC Converters With Autotuning Weighting Factor

In this article, a model predictive control (MPC) method for dc–dc single-ended primary-inductor converters with autotuning weighting factor capability is presented. The conventional MPC requires retuning of the weighting factor when the operating condition of the converter is changed. The effect of this change is observed as inability of the controller in maintaining the switching frequency constant. The weighting factor avoids excessive switching frequency in the dc–dc converters. Based on the relation between the inductor current ripple and switching frequency, an autotuning weighting factor based MPC is proposed. The effect of the weighting factor on the switching frequency is investigated. The proposed MPC eliminates the need for retuning the weighting factor when the operating point of the converter is changed. The proposed control strategy is verified experimentally under input voltage, load, and parameter variations. The results obtained from conventional and proposed MPC methods are compared. It is shown that the proposed MPC method controls the average switching frequency when the operating mode of the converter is changed. Furthermore, parameter mismatch results are presented for both conventional and proposed MPC methods.

Optimized Model Predictive Control With Dead-Time Voltage Vector for PMSM Drives

In order to improve the current steady-state control performance of the model predictive control (MPC), a dead-time voltage-vector based MPC method is proposed in this article. First, the effect of the dead-time on MPC is introduced, and the formation process of the dead-time voltage vector existed in MPC is analyzed. Furthermore, the beneficial dead-time voltage vector and nonbeneficial dead-time voltage vector for the current steady-state control performance of MPC is distinguished, and the advantage of the beneficial dead-time voltage vector is analyzed. Then, the MPC method based on dead-time voltage vector is presented, which optimizes the action time of dead-time. Finally, the experimental results prove that the current steady-state control performance of the proposed MPC method is better than the conventional MPC method without increasing the switching frequency.

Two-Stage Model Predictive Control of Neutral-Point-Clamped Inverter-Fed Permanent-Magnet Synchronous Motor Drives Under Balanced and Unbalanced DC Links

Controlling a three-level neutral-point-clamped (3L-NPC) inverter-fed permanent-magnet synchro-nous machine (PMSM) drive is a challenging task, especially when the dc-link voltage is unbalanced. The unbalanced dc-link voltages, which are with some of the voltage vectors varying in magnitude and phase, lead to highly distorted stator current. In this article, a two-stage model predictive control (MPC) is proposed for the 3L-NPC inverter-fed PMSM drive. In the proposed MPC method, control of the 3L-NPC inverter is decoupled into two stages. In the first stage, three switching states are determined by small hexagon selection using finite control set MPC. The rest switching states are controlled using the multiple vector MPC in the second stage. In the two-stage MPC, the number of switching state evaluations can be significantly reduced, and the nonlinearity caused by the unbalanced dc links can be included in the duty cycle optimization process, leading to improved output current quality under unbalanced dc links and reduced computational burden. Moreover, the output switching frequency is fixed due to the employment of the multiple vector MPC. A thorough experimental evaluation of the proposed MPC method with the deadbeat control with the pulsewidth modulation strategy has been conducted to validate the superiority of the proposed method.

Two-Stage Optimization-Based Model Predictive Control of 5L-ANPC Converter-Fed PMSM Drives

Balancing the dc-link voltage and three flying-capacitor (FC) voltages while maintaining very fast control of the motor is always a challenging task in five-level active neutral-point-clamped (5L-ANPC) converter-fed permanent-magnet synchronous motor drives, especially when the FC is small. Besides, the fundamental-frequency operation of the low-frequency cell (LFC) is also required to reduce the losses of the converter. This article proposed a two-stage optimization-based model predictive control (MPC) scheme to achieve these goals. In the first stage, the switching states of the LFC are selected based on the sign of the desired output voltage through a deadbeat approach for its fundamental-frequency operation; in the second stage, the optimal duty cycles for the high-frequency cell are calculated by the multiple-vector MPC and generated by phase-shifted pulsewidth modulation for its fixed-switching frequency operation. The FC and dc-link capacitor voltage balance can be managed by the flexibility of the inherent redundancy in the 5L-ANPC converter. Instead of 512, only six switching vectors must be considered, which significantly simplifies the computational burden of MPC. A thorough experimental evaluation has been conducted to validate the effectiveness of the proposed MPC method.

Grid-Voltage Sensorless Model Predictive Control of Three-Phase PWM Rectifier Under Unbalanced and Distorted Grid Voltages

The sensors in a three-phase pulse width modulation (PWM) rectifier control system are the basis of high-performance methods. Line voltage sensors are utilized to obtain information about the grid voltage amplitude and phase. To eliminate the grid voltage sensors and to decrease the cost of PWM rectifier control systems, various grid-voltage sensorless methods have been developed. In this article, a virtual-flux-based grid voltage estimation method is utilized in the model predictive control (MPC) of a PWM rectifier. The extended reactive power is introduced in the proposed MPC method; the MPC is then able to realize the control targets of eliminating input-side power oscillations and ensuring sinusoidal currents under unbalanced network conditions without any power compensation terms. The cascaded delayed signal cancellation (CDSC) method is applied to obtain the fundamental rectifier voltages in the virtual flux-based grid-voltage sensorless method. The proposed method can operate under unbalanced and distorted network conditions. Moreover, a startup current suppression method is applied in this method to avoid over-currents during the startup process. Experimental tests are performed to verify the effectiveness of the proposed method.

Dynamic Task Priority Planning for Null-Space Behavioral Control of Multi-Agent Systems

The task priority planning problem is addressed in the task supervisor of null-space behavioral (NSB) control for multi-agent systems. Traditional methods rely on pre-defined logic-based or fuzzy rules to adjust task priority. In this work, a novel task supervisor is proposed using model predictive control (MPC) techniques. At each sampling instant, the task priority planning problem is formulated as a switching mode optimal control problem (OCP), which can be solved by efficient mixed-integer optimal control algorithms. The optimal task priority order is obtained based on current and predictive information of agents, without the need for a pre-defined rule. By explicitly introducing slack variables into constraints, the proposed MPC method is flexible to cope with dynamic obstacles in unknown environments. Simulations with static and dynamic obstacles show that the proposed method can provide significantly better control performance than the traditional logic-based method using less priority switchings.

Double-Vector Model Predictive Control for Single-Phase Five-Level Actively Clamped Converters

In this article, a double-vector-based model predictive control (MPC) method with efficient computation for single-phase five-level active neutral-point clamped (5L-ANPC) converters is proposed. It uses double voltage vectors per control cycle rather than the single in the conventional MPC method, resulting in reduced output current ripples and fixed switching frequency. First, the MPC method based on the Lyapunov function is optimized to select two voltage sectors rather than 25, reducing the calculation complexity significantly. Then, two current tracking algorithms based on double voltage vectors are presented, which are using the current cost function and current slope, respectively. Finally, a fast voltage balancing scheme for the dc-link and flying capacitors is developed and embedded in the MPC method. Both simulated and experimental results are used to validate the correctness and feasibility of the proposed MPC method.

Modified Increased-Level Model Predictive Control Methods With Reduced Computation Load for Modular Multilevel Converter

It is considerable to reduce the computation load while keeping a good total harmonic distortion (THD) performance for the modular multilevel converter (MMC) with model predictive control (MPC). In this paper, the discrete-time mathematical model of the MMC is derived and the output voltage ripples of the cascaded submodules (SMs) are analyzed in detail. Accordingly, a modified increased-level MPC method A (IL-MPC-A) is proposed with 2 N + 1 ac-side output voltage levels. The computation load is significantly reduced without losing the harmonic circulating currents suppression and capacitor voltage ripples compensation capabilities. In this method, the cost function minimization step and capacitor voltage sorting algorithm are combined to determine the optimal control option. In addition, to avoid the selection of weighting factors and further reduce the computation load as the increasing of the SM number, another modified IL-MPC-B is proposed by directly calculating the optimal ac-side output voltage level and arm summation voltage according to the discrete-time mathematical model. As a result, the ac-side output current and circulating current controls are decoupled. Furthermore, the capacitor voltages are balanced with a proposed control scheme by adjusting the circulating current reference. The effectiveness of the proposed MPC methods is verified by simulation and experimental results.

A Control Strategies of PV System based on VOC, SMC and MPC Algorithms

Objectives: Due to the stochastic behavior of the climatic conditions, a comparative study of grid- connected PV inverter based on Voltage Oriented Control (VOC), Sliding Mode Control (SMC) and the proposed Model Predictive Control (MPC) method is required to enhance the power quality that is affected by the weather fluctuations, and to ensure unremitting operation at the maximum power point in order to inject to grid the totality of the generated electrical energy. Methodology: The PV power extracted from the PV array is fed to grid via a three phase inverter interface by maintaining constant DC-link voltage. To evaluate the MPC algorithm efficiency for controlling this power inverter, it\'s necessary to compare its performances to already studied techniques. Comparative case studies often introduce qualitative criteria such as waveforms, current Total Harmonic Distortion (THD), power distortion and switching frequency. Findings: The simulation results under Psim environment prove the MPC-based algorithm robustness to control the photovoltaic inverter, particularly, the lower overshoots, the accuracy and the minimum current THD. Improvement: The obtained Psim simulations validate the effectiveness of the proposed MPC method against parameter variations. Keywords: Grid, PV System, MPC, VOC, SMC

Model Predictive Control for Electrochemical Impedance Spectroscopy Measurement of Fuel Cells Based on Neural Network Optimization

Electrochemical impedance spectroscopy (EIS) is a key specification of fuel cells, which can reflect the healthy state. EIS could be measured by using the ripple modulation of dc/dc converter connected to fuel cells. This method has the advantage of no external excitation sources and low volume. In ripple modulation, the mixed signals composed of direct current and alternate current are difficult to track accurately, so a model predictive control (MPC) method with favorable stability and dynamic response is proposed. Considering that the traditional optimization algorithm takes a long time to calculate, a recurrent neural network (RNN) optimization is used to find a solution of the quadratic programming (QP) problem in order to reduce online computation time. Moreover, field programmable gate array (FPGA) is employed to implement the proposed MPC method. Experimental results demonstrate that the proposed method could measure the EIS of fuel cell effectively.

Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.

MPC with Constant Switching Frequency for Inverter-Based Distributed Generations in Microgrid Using Gradient Descent

Variable switching frequency in the finite control set model predictive control (FCS-MPC) method causes a negative impact on the converter efficiency and the design of the output filters. Several studies have addressed the problem, but they are either complicated or require heavy computation. This study proposes a new model predictive control (MPC) method with constant switching frequency, which is simple to implement and needs only a small computation time. The proposed MPC method is based on the gradient descent (GD) method to find the optimal voltage vector. Since the cost function of the MPC method is represented in the strongly convex function, the optimal voltage vector could be found quickly by using the GD method, which reduces the computation time of the MPC method. The design of the proposed MPC method based on GD (GD-MPC) is shown in this study. The feasibility of the proposed GD-MPC is evaluated in the real-time simulation using OPAL-RT technologies. The performance of the proposed method in the case of single inverter operation or parallel inverter operation is shown. A comparison study on the proposed GD-MPC and the MPC with the concept of the virtual state vector (VSV-MPC) is presented to demonstrate the effectiveness of the proposed predictive control. Real-time simulation results show that the proposed GD-MPC method performs better with a low total harmonic distortion (THD) value of output current and short computation time, compared to the VSV-MPC method.

Model Predictive Control of a Grid-Connected Inverter with LCL Filter using Robust Disturbance Observer

Abstract This paper proposes a model predictive control (MPC) method using a robust disturbance observer to control the current output of a grid-connected inverter. Firstly, the parameter uncertainties are expressed as possible ranges of resistor, inductor and capacitor values. Secondly, a robust disturbance observer is designed to estimate the lumped disturbance term caused by model uncertainties and the grid voltage disturbance. In addition, the disturbance observer gain, and a weight matrix of the MPC cost function are obtained by applying the Linear matrix inequality (LMI) optimization method. Thirdly, a reference state, which satisfies the control objective of the currents output regulation are derived. Finally, the effectiveness of the proposed MPC method is proven by simulation result.

Ecological and safe driving: A model predictive control approach considering spatial and temporal constraints

Road transport plays an important role in the economy but generates huge costs in terms of road trauma and greenhouse gas emissions. The vast majority of previous research efforts have focused on driving behaviours that either minimise fuel consumption, or advise on safe driving behaviours. However, a fuel-optimal behaviour is often difficult to implement due to traffic conditions and may lead to unsafe behaviour without proper constraints. While a range of optimisation techniques are currently available, most lack the ability to both consider time and distance dependent constraints necessary for computationally efficient determination of optimal behaviour. In this paper, a methodology to find an ecological and safe (EcoSafe) driving behaviour by formulating and solving an optimal control problem for the minimum fuel driving behaviour while respecting key safety constraints was developed. We first propose a modified distance-based dynamic programming (MDDP) technique that can treat distance and time inequality constraints while optimising. Then we propose a model predictive control (MPC) structure based on MDDP and Pontryagin’s maximum principle by considering inter vehicle time and time-to-collision as hard safety constraints. In contrast to existing approaches, the proposed MPC method does not require ad hoc tuning of constraint penalty weights in the cost function to ensure safe following distances. Simulation results of the proposed method show that the proposed algorithm produces lower vehicle fuel consumption compared to existing approaches when safety constraints are limiting. Thus, the results demonstrate the importance of considering safety constraints within an eco-driving system.

Model Predictive Control to Autonomously Approach a Failed Spacecraft

In this study, a model predictive control (MPC) method is developed for a servicer spacecraft autonomously approaching a tumbling failed spacecraft at an ultraclose range. Flight safety and collision avoidance are basic requirements during the approach. Two types of a failed spacecraft with complex configurations are considered, and a double-ellipsoid composite envelope strategy is designed to model their keep-out zones. Given the keep-out zone of the servicer, two expanded ellipsoids are subsequently introduced to determine the collision and sufficient conditions for collision avoidance are derived by using the form of concave constraint. The tumbling motion of the target is considered, and a CW-based translational dynamics and derived attitude dynamics of the target are formulated to predict the motion of the docking point and keep-out zone. The MPC is formulated to drive the servicer tracking the docking point with collision avoidance and handle constraints including control input saturation and relative velocity bound. Convexification of the collision avoidance constraint and sequential convex programming are adopted for the implementation of MPC. Scenarios on the servicer with different initial positions approaching the target with different angular velocities are simulated, and the simulation results indicate that the proposed MPC method is effective.