Conventional Model Predictive Control Research Articles

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

Real-time time-varying economic nonlinear model predictive control for wind turbines

Economic nonlinear model predictive control is a great choice to tackle the control issues appearing in the current wind energy industry. In this paper, instead of generator power, aerodynamic power is employed in the economic cost function to establish the required conditions of stability and convergence of the economic performance, such as turnpike and problem convexity. However, since aerodynamic power depends on time-varying wind speed, conventional time-invariant economic nonlinear model predictive controllers cannot guarantee the stability and convergence of economic performance. This paper proposes a new time-varying economic nonlinear model predictive controller for wind turbine control that considers an economic trajectory, instead of a steady-state, in its optimization problem. The proposed time-varying economic cost function directly considers aerodynamic power, the activity of pitch angle and generator torque, and fatigue loads on the shaft and tower. Therefore, this controller can maximize power extraction and reduce fatigue load on the tower, drivetrain, and actuators. Furthermore, a fast-parallel Newton-type method is used to implement the proposed controller in actual wind turbines. An accurate aeroelastic model is used to validate the performance of the proposed control scheme. The proposed controller is also compared with two tracking nonlinear model predictive controllers, the baseline controller, and a newly developed method, under fatigue and extreme load scenarios in the presence and absence of uncertainty. The simulation results show the superior economic performance of the proposed approach. Moreover, the real-time results verify the computational speed that the proposed controller requires to deploy actual wind turbines.

Learning-based model predictive secondary frequency control of PV-ESS-EV microgrid

Microgrids comprising photovoltaic (PV), energy-storage systems and electric vehicles have received increasing attention. However, the increase in renewable energy sources, such as PVs, will introduce uncertainty factors to the frequency response, resulting in frequency fluctuations that can reduce power quality. Hence, we propose an outstanding learning-based control approach that combines a model of physical principles with a Gaussian Process (GP) learning approach. The learning model corrects the physical principles model and estimates its uncertainty. Simulations are performed by comparing the proposed controller with conventional model-predictive controllers. The results indicate that the learning-based controller proposed can adjust the frequency with less overshooting and a lower root mean square error compared to the conventional MPC control method.

Systematic Development of Application-Oriented Operating Strategies for the Example of an Industrial Heating Supply System

The ongoing challenge to ensure a sustainable and affordable energy supply forces industrial companies to transform their energy system. This transformation usually leads to an increase in topological complexity, which in turn results in increased operational complexity. Existing approaches from the field of supervisory and optimal control are capable of mastering complex operational problems. However, due to the complex and non-transparent implementation, there is still no industrial penetration, which hinders the necessary transformation of energy systems. This work aims at establishing trust in these control approaches and presents a procedure model for the systematic development of application-oriented operating strategies for industrial energy heating systems. It combines research approaches from the fields of sequencing control and approximate MPC to extract rule-based operating strategies, which are inherently easy to understand and implementable. By splitting the procedure model into five phases, expert knowledge can be integrated in a targeted manner. The procedure model is validated by the exemplary application to an industrial heating supply system. As part of an optimization study, the operating strategy developed is compared with both an MPC strategy and a baseline strategy. While the conventional MPC approach represents the upper limit of optimality, the operating strategy developed is able to achieve comparable results. Compared to the baseline strategy, a relative reduction in operating costs of 5.4% to 37.0% is achieved.

Improved Model Predictive Speed Control of a PMSM via Laguerre Functions

This paper proposes a model predictive speed control strategy for a surface-mounted permanent magnet synchronous motor by applying Laguerre functions. The model predictive controller (MPC) incorporates an integrator. A quadratic programming procedure is applied to solve the constrained optimization problem online. The paper also provides a solution for stability. The performance efficiency of the proposed scheme is validated by comparing the results with the performance of an optimal linear quadratic regulator, conventional state-space model predictive control, and a simple MPC algorithm with integral action. Extensive simulation results confirm the efficacy of the proposed scheme, showing that it achieves good steady-state performance while maintaining a fast dynamic response.

Dynamic safety control of offshore wind turbine based on model predictive control

Offshore wind power is a clean energy source with excellent development prospects that are conducive to realising China's goal of carbon neutrality. However, offshore wind turbines (WTs) are prone to failure and damage owing to their long-term operation in marine environments with high humidity, high wind speeds, and frequent extreme weather. To ensure the safe operation of offshore WTs, a modified dynamic model predictive control (MPC) method was proposed in this study. First, a dynamic model of an offshore WT was established and its nonlinear state-space equations were obtained. These state-space equations were subsequently linearised and discretised, then fast trajectory tracking of the state variables was performed according to the conventional MPC principle. Finally, a control strategy was designed to realise the safe operational control of offshore WT using the safety index as a constraint condition in the MPC. Simulation results confirmed that the proposed MPC strategy using the safety index-based constraint exhibited superior tracking of the set trajectory and achieved the minimum energy loss under varying wind speeds.

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

An Angle-Based Virtual Vector Model Predictive Current Control for IPMSM Considering Overmodulation

The conventional model predictive control method suffers from large current and torque harmonics due to the limited voltage vectors. To increase the control performance, in many papers, the methods of extending the predictive control set by adding virtual vectors are proposed. However, constructing virtual vectors will bring extra computational burden for vector optimization. To solve this problem, this paper proposes an angle-based virtual vector model predictive current control (ABVV-MPCC) method for interior permanent magnet synchronous motor (IPMSM). In this method, a hexagonal coordinate system is introduced to define the virtual vectors. After obtaining the composite relationship between the reference voltage vector and the adjacent basic vector, the vector optimization range is reduced from six voltage sectors to one voltage sector. And the optimal voltage vector is directly obtained by searching for the closest candidate voltage vector. After adopting the proposed method, the exhaustive optimization is avoided and the calculation burden is independent from the virtual voltage vectors number. Therefore, virtual voltage vectors number can be set arbitrarily, which greatly reduces current harmonics. Besides, a simple overmodulation dealing method by finding the optimal reachable voltage vector is proposed. And the dynamic performance under overmodulation is increased without adding additional control module.

A comprehensive high‐level automated driving assistance system with integrated multi‐functionality

AbstractAdvanced Driver Assistance Systems (ADAS) have gained substantial attention in recent years. However, the integration mechanism of multiple functions within ADAS remains unexplored, and the full potential of its functionality remains underutilised. This paper presents a novel multi‐functional integrated High‐level Automated Driving Assistance System that combines the Cruise Control (CC), Adaptive Cruise Control (ACC), Automated Emergency Brake (AEB), and Automated Lane Change (ALC) functions. The presented system utilises a hierarchical framework. The extension multi‐mode switch strategy is established as the superior module and the Event‐Triggered Model Predictive Controller (ETMPC) is designed as the inferior controller. The CC, ACC, and ALC functions are effectively utilised to enhance traffic efficiency, while the AEB function ensures driving safety. To address the time constraints of conventional Model Predictive Control, an event‐trigger mechanism is proposed to reduce computational load. Simulations are conducted using the CarSim and Matlab platforms. The study results demonstrate significant improvements in both safety and traffic efficiency compared to conventional ADAS strategies. Furthermore, the proposed ETMPC method significantly reduces the frequency of solving Optimisation Problems and decreases online computation costs.

Stochastic Control of Battery Energy Storage System with Hybrid Dynamics

This paper addresses the control of load demand and power in a battery energy storage system (BESS) with Boolean-type constraints. It employs model predictive control (MPC) tailored for such systems. However, conventional MPC encounters computational challenges in practical applications, including battery storage control, and requires dedicated, mostly licensed solvers. To mitigate this, a solver-free yet efficient, suboptimal method is proposed. This approach involves generating randomized control sequences and assessing their feasibility to ensure adherence to constraints. The sequence with the best performance index is then selected, prioritizing feasibility and safety over optimality. Although this chosen sequence may not match the exact MPC solution in terms of optimality, it guarantees safe operation. The optimal control problem for the BESS is outlined, encompassing constraints on the state of charge, power limits, and charge/discharge modes. Three distinct scenarios evaluate the proposed method. The first prioritizes minimizing computational time, yielding a feasible solution significantly faster than the optimal approach. The second scenario strikes a balance between computational efficiency and suboptimality. The third scenario aims to minimize suboptimality while accepting longer computation times. This method’s adaptability to the user’s requirements in various scenarios and solver-free evaluation underscores its potential advantages in environments marked by stringent computational demands, a characteristic often found in BESS control applications.

Enhanced Model Predictive Control for Induction Motor Drives in Marine Electric Power Propulsion System

Marine electric propulsion is an important topic in the research of modern ships and underwater vehicles. The propulsion motor drives based on model predictive control (MPC) are becoming increasingly popular in marine propulsion systems as an emerging technology. However, the multi-objective optimization in conventional MPC requires cumbersome weighting factor tuning. The relatively large computational cost is also detrimental to the industrial application of MPC. Aiming at reducing the computational complexity of multi-objective optimization without weighting factors, this paper proposes an enhanced ranking-based MPC method for induction motor drives in marine electric power propulsion. The presented control set pre-optimization aims to reduce the computational complexity of enumeration and ranking. Based on the sign of torque prediction deviation, the proposed method avoids enumerating all fundamental voltage vectors. Consequently, the number of candidate elements in the initial control set are reduced to four without excessively excluding feasible solutions. By converting predicted numerical errors into ranking results, the proposed MPC seeks the optimal solution among the candidates through improved ranking evaluation. Considering the situation of simultaneous optimal ranking, the normalization error judgment is developed to further optimize the optimal solution selection process. The simulation and experimental results confirm that the proposed MPC is simple and effective. Without the involvement of tuning the weighting factors, the proposed method achieves good performance.

Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties

Doubly fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input.

Linear time-varying fractional-order model predictive attitude control for satellite using two reaction wheels

Achieving precise attitude control in satellites equipped with two reaction wheels remains a significant challenge. During the last decade, model predictive control has emerged as a promising technique for satellite attitude control. However, this paper takes a step by introducing the novel concept of linear time-varying fractional-order model predictive control specifically tailored for satellites with dual reaction wheels, enabling them to achieve desired orientations more effectively. This study begins by elucidating the nonlinear dynamics and kinematic equations governing the satellite's behavior. To facilitate control design, the satellite linear time-varying model is derived by linearizing the nonlinear equations around the operating point. Leveraging this linearized representation, a linear time-varying fractional-order model predictive control method is designed, leveraging the benefits of fractional-order cost functions. Finally, the simulation results show the remarkable performance of the linear time-varying fractional-order model predictive control approach, particularly when confronted with input constraints. Notably, the proposed method outperforms the traditional linear time-varying model predictive control method by achieving an 80% improvement in tracking speed, while simultaneously reducing the applied torque by 67%. Additionally, the proposed approach decreases the mean absolute error of the satellite attitude angles by over 80% compared to the conventional linear time-varying model predictive control method. This research not only addresses the pressing challenge of attitude control in satellites with dual reaction wheels but also presents a novel and highly effective approach that surpasses existing methods in terms of performance and efficiency. As such, it holds immense potential for the field of satellite attitude control, making it an indispensable read for engineers and researchers working in the domain of aerospace engineering and control systems.

Model Predictive Virtual Flux Control Method for Low Switching Loss Performance in Three-Phase AC/DC Pulse-width-Modulated Converters

Three-phase AC/DC pulse-width-modulated (PWM) converters have been widely employed in various renewable energy systems and industrial applications, which require a high-efficiency power converter operation. This article proposes a technique to reduce switching loss in AC/DC converters by integrating a voltage vector preselection strategy to model predictive virtual flux control. The voltage vector preselection strategy preselects available voltage vectors corresponding to switching states that lead to minimum switching loss in the phase leg, which conducts the highest current. By using preselected voltage vectors, clamping intervals are generated at every fundamental period to maintain the present switching states of the power switches, resulting in the reduction in switching loss. Additionally, by using virtual flux control, the proposed approach can effectively be used under both ideal and distorted source voltage conditions. The proposed method is compared with the conventional model predictive current control and the conventional model predictive virtual flux control. Both a simulation and experiment are performed to validate the correctness and effectiveness of the proposed method, which has been found to decrease the switching loss of an AC/DC converter by up to 15% compared to conventional control schemes at a negligible increase in the input current total harmonic distortion and DC output voltage ripple.

Arm‐current sensorless model predictive control for grid‐interfacing modular multilevel converter with reduced switching frequency

AbstractModel predictive control (MPC) emerges as an attractive alternative for the control of modular multilevel converters (MMCs) owing to its superior dynamic performance and ease of implementation. Nevertheless, conventional MPC's performance could be further improved by reducing dependence on weighting factors, switching frequency, and computational burden. To achieve these objectives, an arm‐current sensorless MPC is developed for a grid‐interfacing MMC in this article. The MPC is integrated with an arm‐current sensorless reduced switching frequency voltage balance algorithm that requires only switching vectors (where number of submodules per arm) to optimize the cost function. This method does not require knowledge of arm‐current direction to decide the charging and discharging state of the submodule capacitors. The developed control not only reduces the switching frequency of devices but also eliminates the need to sense the arm currents. As a result, it reduces the system's cost, complexity, computational burden, switching losses and provides a more reliable control method for MMCs with superior dynamic performance. Further, the effect of parameter mismatch on the developed MPC's dynamic performance and stability is studied. The developed MPC's effectiveness is validated through both simulation and experimental results and also compared with existing methods under different conditions.

Busbar voltage-based control strategy for energy flexibility in farmhouse coupled to photovoltaic systems

Control strategy is a crucial technology for solving the mismatch between supply and demand in photovoltaic (PV) systems. Model predictive control (MPC) has high accuracy but high system complexity and cost, thus limiting its application. This study proposes a busbar-voltage-based control strategy and applies it to a farmhouse equipped with a PV system in Northern China. The core of the busbar-voltage-based control strategy is that the converter in the system automatically adjusts the operating conditions to ensure that the busbar voltage is stabilized within a specific range. Field test results revealed that the busbar voltage-based control strategy improved the energy flexibility of the farmhouse PV system. We developed a predictive model for the control system and analyzed its performance during different seasons. The simulation results confirmed that the farmhouse PV system's energy flexibility, self-satisfaction rate, and self-consumption rate under the proposed control strategy were similar to those under the conventional MPC approach. This study provides a novel control approach that enables MPC-level performance without the need for communication systems and controllers, which reduces costs and helps promote PV systems.

Explicit hybrid MPC for the lateral stabilization of electric vehicle system

This paper presents a hybrid Model Predictive Control (hMPC) approach to improve Electric Vehicle (EV) stability when cornering or in high-risk driving conditions. By using approximations of tire force characteristics, the EV system proposed in this study is considered as a Piecewise Affine (PWA) system which is a class of Hybrid Dynamical Systems (HDSs). First, a hybrid modeling of the studied system is developed within the PWA framework. Then, the synthesis of an explicit hybrid MPC law, based on the integration of the Active Front-wheel Steering control method (AFS) and Direct Yaw moment Control method (DYC), can be established. The purpose of the PWA-MPC is to force the yaw rate and sideslip angle of the EV system to follow the given references to guarantee lateral stability. The calculation of the control laws of this proposed approach is performed offline using multiparametric programming. Finally, this paper investigates the effectiveness of the designed controller by performing a set of simulations to analyze the efficiency and robustness of the proposed approach. A comparative study is addressed in this work to demonstrate the efficiency of the proposed hybrid MPC controller compared to the conventional MPC approach which considers the linear model of the system. Another simulations are carried out to assess the robustness of the proposed control, important disturbances and uncertainties were incorporated such as the change of the additional mass, the change of terrain and the high velocity. The simulation results show that the PWA EV system model with the designed controller is able to ensure lateral stability by keeping the sideslip angle and yaw rate tracking the ideal values.

Multirate Modeling and Predictive Control for WBG-Device-Based High-Switching-Frequency Power Converters

With the growth of wide bandgap devices, it is necessary to exploit the high switching frequency benefits to improve the power converter's performance, thus requiring a higher sampling/interrupt frequency in digital signal processors. However, such a short interrupt time duration imposes big computational difficulty in the execution of programming code, especially using the model predictive control (MPC). Thus, most of the existing MPCs are applied with switching frequencies below 20 kilohertz, which cannot exploit the full potential of the wide bandgap device-based power converters. To solve this challenge, this article proposes a multi-rate MPC scheme, where the trigger of interrupt and switching device transition can be performed at different rates. Compared to conventional MPCs, the main uniqueness of the proposed multi-rate MPC is that the high-dimension control sequence is solved and applied within each interrupt interval. Therefore, the increased switching frequency objective can be easily achieved with a low sampling/interrupt frequency configuration, which also significantly relieves the digital execution of the heavy interrupt tasks. The proposed method shows a more optimized control input and higher computational efficiency over the multi-rate finite-control-set MPC counterpart. A silicon carbide inverter-fed AC motor drive system is used to verify the proposed multi-rate MPC. The results show the improved system performance with the combined advantages of both the high switching frequency and MPC strategy.

A Low-Complexity Double Vector Model Predictive Current Control for Permanent Magnet Synchronous Motors

Compared to the conventional finite control set model predictive control (FCS-MPC), the double vector model predictive current control (DVMPCC) for permanent magnet synchronous motors (PMSMs) has a better steady-state performance without significantly increasing the switching frequency. However, determining optimal vectors with their dwell times requires a high computational burden. A low-complexity DVMPCC in the steady state was proposed in this study to address this problem. Firstly, the operating state of the motor was judged according to the speed error. During steady-state operation, the first optimal active vector was selected from three candidate vectors adjacent or identical to the active vector applied in the previous control period, reducing the number of comparisons by half. Next, the second optimal vector was selected from the other two active vectors, and the zero vector, the second optimal vector with the duty cycle, was determined according to the deadbeat condition of the q-axis current and cost function minimization. Finally, simulation and experimental results proved that the proposed low-complexity DVMPCC for surface-mounted permanent magnet synchronous motors is practical and feasible.

Current sensorless model predictive control for LC-filtered voltage source inverters based on sliding mode observer

Abstract In recent years, model predictive control (MPC) has gained significant traction within the realm of LC-filtered voltage source inverters (VSIs). In order to reduce the number of current sensors in conventional MPC, this paper proposes a MPC scheme without current sensors for LC-filtered VSIs. Firstly, this paper analyzes the mathematical model of LC-filtered VSIs and specifies the predictive control strategy based on capacitive current. On the basis of considering only the fundamental wave components of the capacitance current, this paper establishes a new sliding mode observer to observe the capacitance current. In order to ensure the stability of the system, the stability of the proposed sliding mode observer is analyzed using Lyapunov stability criterion, and the influence of the sliding mode gain on the current and voltage observations is also analyzed according to the closed-loop transfer function, giving the design method of the sliding mode gain. In addition, the parameter sensitivities of the newly proposed method and the conventional method are analyzed in detail in this paper. The results show that the method proposed in this paper does not depend on the filter inductance and the observed capacitance current have smaller errors and stronger parameter robustness than the conventional method. The comparative experimental study verifies the feasibility of the control scheme proposed in this paper.