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Summary: The LC filter integrated quasi‐Z source indirect matrix converter (QZS‐IMC) filters the input voltages and currents while highly expanding voltage boost ratio through the combined QZS network, avoiding the additional input filter while maintaining full‐silicon solution. The traditional proportional‐integral (PI) control method of IMC is realized through the PI regulators with pulse width modulation (PWM), which is complicated due to elaborated vectors computation and duty cycle composition of IMC and extra control loops design is mandatory. This paper proposes a model predictive control (MPC) for the LC filter integrated QZS‐IMC. Based on the established discrete models of circuit voltages and currents, the proposed control method measures the present circuit variables and predicts the variables in the next sampling period. Then, a predefined cost function is evaluated with the predictions and references at each active switching states of the IMC. An optimal switching state that ensures the smallest value of the cost function is applied to the power devices at the beginning of next sampling time. Simulation and experiment are carried out to demonstrate effectiveness of the proposed MPC for the LC filter integrated QZS‐IMC. [ABSTRACT FROM AUTHOR]

Model 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 m+1 switching vectors (where m= 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. [ABSTRACT FROM AUTHOR]

Finite-set model predictive control (FS-MPC) is an easy and intuitive control technique. However, parametric uncertainties reduce the accuracy of the prediction. Classical MPC requires many calculations; therefore, the calculation time generates a considerable time delay in the actuation. This delay deteriorates the performance of the system and generates a significant current ripple. This paper proposes a finite-set integral sliding modes predictive control (FS-ISMPC) for a permanent magnet synchronous motor (PMSM). The conventional decision function is replaced by an integral sliding cost function, which has several advantages, such as robustness to parameter uncertainties, and convergence in finite time. The proposed decision function does not require the inductance and resistance parameters of the motor. In addition, the proposal includes compensation for the calculation delay of the control vector. The proposed control strategy was compared with traditional predictive control with delay compensation using a real-time hardware-in-the-loop (HIL) simulation. The results obtained from the comparison indicated that the proposed controller has a lower THD and computational burden. [ABSTRACT FROM AUTHOR]

The traditional Model Predictive Control (MPC) algorithm, grounded in precise mathematical models, faces challenges attributed to uncertainties in vehicle model parameters and modeling errors. These limitations result in suboptimal accuracy of the path tracking controller, particularly in complex driving environments. To address this issue, a tube-based model predictive control strategy (Tube MPC) is proposed. Initially, nominal dynamic modeling of intelligent vehicles is conducted, followed by linearized derivations. Leveraging the resulting linearized nominal model, the MPC cost function is solved to derive the control law for the nominal system. Subsequently, an error model between the actual and nominal systems is established, integrating various target constraints for path tracking and formulating a robust objective function. Utilizing the linear matrix inequality optimization method, a state feedback-assisted control law is derived to mitigate steady-state lateral position error. This approach ensures a more precise alignment between the actual and nominal states, ultimately enhancing tracking accuracy. Finally, experimental validations are conducted in the Carsim/Simulink simulation environment, encompassing diverse vehicle speed conditions and driving scenarios to assess the proposed controller against traditional Model Predictive Control. The experimental results demonstrate that, while ensuring vehicle stability, this control method exhibits superior tracking accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a single objective variable double voltage vector model predictive control (SOV‐DVVMPC) to lower the computational burden for single‐phase cascaded H‐bridge (CHB) converters is proposed. The proposed algorithm is based on a hierarchical structure to control the grid‐connected current and capacitor voltage without a weighting factor. Firstly, layer I controls the grid‐connected current to select the optimal region, the cost function related to the voltage region is designed and the optimal state is calculated directly by analyzing the linear relationship between the divided region and its adjacent voltage levels, which can reduce the computational burden. Correspondingly, the optimal vectors are symmetrically distributed over the entire control cycle by a modulation principle, fulfilling the fixed switching frequency (FSF) of the system. Then, layer II uses redundant switching states to maintain capacitor voltage balancing. Finally, a single‐phase CHB converter experiment setup is constructed, the results show that compared with DVV‐MPC, the execution time of SOV‐DVVMPC is reduced by 26.4%, and compared with traditional MPC, the switching frequency is fixed and the output current quality is improved. [ABSTRACT FROM AUTHOR]

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS. [ABSTRACT FROM AUTHOR]

This work studies the application of a sample-based model Monte Carlo Model Predictive Control (MCMPC) to address discontinuous events, such as collisions. The inclusion of contact dynamics in the prediction steps of MCMPC renders it advantageous for the operation of underwater snake robots in restricted environments during rescue missions. The Curvature Derivative Control (CDC) method is employed for the purpose of controlling the joints, with the exception of the head joint. The snake robot body demonstrates adaptability to various environments with the assistance of the CDC. Results obtained from the simulation demonstrate that the snake robot is capable of autonomously navigating from its beginning position outside the region to a pipe as the entrance in order to get inside. The robot successfully traverses through various internal structures within the pipe and ultimately reaches the intended destination, guided by a predetermined cost function of MCMPC. By using the capabilities of CUDA GPU parallel computing, the computational time is substantially lowered. [ABSTRACT FROM AUTHOR]

Voltage instability in power systems arises due to the shortage of reactive power and may cause abnormally low bus voltages leading to a partial or complete blackout. In order to maintain the system voltages within a safe limit, voltage control techniques such as shunt capacitor banks, Static VAR Compensators (SVCs), load shedding, and transformer tap-changer blocking, are employed. In this paper, a novel receding-horizon Model Predictive Control (MPC)-based voltage controller is proposed, which, by optimally controlling generator reactive power and SVC output, maintains the voltage stability of a power system. For this, a sensitivity-based analysis is performed to design a state-space model of the power system. The frequency and voltage dependency of load and generation are considered in the system equations. The voltage control is done step-wise, and the optimal control action in each step is calculated by minimizing a cost function subject to a set of relevant constraints. Different Voltage Stability Indices (VSIs) are used as a measure of voltage stability and also used in the constraints for the optimization problem. The performance of the proposed controller is evaluated on IEEE 9-, 39- and 118-bus systems, considering different types of loads and contingencies. [ABSTRACT FROM AUTHOR]

his research focuses on a three-phase Active Front End (AFE) converter that uses Model Predictive Control (MPC) for its switching operations. The MPC technique forecasts the AFE voltages and currents by integrating the voltage vectors from the converter switches across eight distinct switching states into a discrete state space model. Simulation results reveal a rapid dynamic response to changes in load voltage and minor fluctuations in current and wave values, which increase after connecting to an unbalanced load. Testing the effect of sampling time shows that a 5µs sampling time achieves the lowest Total Harmonic Distortion (THD), with 0.02% for voltage and 0.13% for current. This indicates that smaller sampling times improve the accuracy of reference voltage predictions, thereby enhancing electricity quality. Furthermore, the impact of nonlinear loads on voltage demonstrates that as the load resistance significantly increases from 20 Ω to 45 Ω, inversely proportional to the load inductance value, the THD remains relatively constant, ranging from 0.03% to 0.04%. In the 780th prediction analysis using MPC with a 25µs sampling time, the voltage reference value is determined. The cost function value is 0.0127 in the 8th switching state, with predicted voltage values of Vsa = -51.3829 V, Vsb = -2.53489 V and Vsc = 3.04872 V [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The propulsion system for underwater vehicles, driven by a counter-rotating permanent magnet synchronous motor (CRPMSM), can enhance the operational stability and efficiency of the vehicle. Due to the influence of complex underwater flows, the load imbalance of CRPMSM's dual counter-rotating rotors may lead to severe issues of dual-rotor desynchronization rotation. Combining traditional vector control (VC) with master-slave control strategies can address the desynchronization problem when CRPMSM's load changes. However, it results in significant speed fluctuations and a long transition time during the transition from load disturbance to synchronous rotation. This paper introduces a model predictive control (MPC) strategy to effectively resolve this issue. The incremental MPC model is established based on the mathematical model of CRPMSM in the dq coordinate system. The predictive control system forecasts the d- and q-axis components of stator currents for the next four control cycles. It selects the optimal control increments to minimize the cost function based on current predictions and different inverter voltage states. The obtained optimal d- and q-axis components of stator voltage are used to control CRPMSM under unbalanced load disturbances. Simulation results demonstrate that, compared to the VC strategy, CRPMSM utilizing the MPC strategy exhibits better dynamic performance with faster speed response and reduced torque fluctuations during load and speed variations. [ABSTRACT FROM AUTHOR]

This work develops a new robust multi‐objective model predictive control (MoMPC) strategy for constrained non‐linear systems with bounded disturbances. The multiple objectives are always contradictory, and the presence of disturbances may result in the violation of state constraints and instability of the controlled system. Firstly, the conflict between multiple objectives is reconciled by minimizing the distance of the cost function vector to the vector of independently minimised objectives obtained by solving a set of steady‐state optimisation problems. Then the contractive state set, which takes into account the effect of disturbances on system states, is constructed to guarantee the robust satisfaction of state constraints. Finally, a stability constraint updated online by solving an auxiliary optimization problem is established to ensure the robust stability of the system under MoMPC. An isothermal chemical reactor system is employed to verify the effectiveness of the controller proposed here. [ABSTRACT FROM AUTHOR]

This research focuses on a methodology for connecting the linear induction machine (LIM) using model predictive flux control (MPFC) fed by Kyocera solar cells. Additionally, it incorporates a fuzzy logic regulator (FLR) for maximum power point tracking (MPPT) control. The boost converter's characteristics are regulated using MPPT, allowing it to achieve a voltage gain of up to twice the input voltage. The finite state-model predictive flux control (FS-MPFC) is employed to synthesize the three-phase voltage source converter switching states. The paper proposes an improved FS-MPFC strategy for LIMs, eliminating the cost function's need for a weighting parameter. This is achieved by unifying its terms of traditional control methods and reducing the number of calculation steps. The validity and effectiveness of the introduced system are confirmed by extensive simulation results. The findings revealed that the system can accurately extract maximum power from the Kyocera solar cells and track the preset speed values. In addition, it can enhance performance by minimizing the thrust and primary flux under varying speed and load conditions. • The fuzzy logic regulator (FLR) is discussed to achieve MPPT. • A Linear Machine (LM) based on model predictive flux control (MPFC) fed by PV is provided. • The MPFC is proposed to avoid the WF and reduce the ripples compared to the MPTC method. • Extensive simulation analysis is performed in different cases via MATLAB-SIMULINK. [ABSTRACT FROM AUTHOR]

This paper studies model predictive control (MPC) with control barrier function to solve the stability and safety problems of switched systems subject to input and state constraints. Considering that the system state is unmeasurable and disturbed, an observer-based MPC $ H_\infty $ H ∞ method is adopted to estimate the system state and suppress disturbances. A cost function is designed to optimise the state and control input of switched systems by minimising its upper bound, and the optimisation problem is further transformed into a feasibility problem. Moreover, a control barrier function is introduced to constrain the controller gains, which ensures that the initial state of switched systems converges to the steady state without entering the specified unsafe region. Furthermore, a set of matrix inequality conditions is given to express the input and state constraints. Then, the Lyapunov function and average dwell time (ADT) approach are used to obtain sufficient conditions for the asymptotic stability of switched systems. Finally, two simulation examples are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to harness a greater share of wind energy resources, offshore wind energy projects are venturing into deep-sea locations. In this progression, the issue of grid integration control becomes increasingly challenging. Traditional Model Predictive Control (MPC) has been introduced in offshore wind energy grid integration control due to its merits, such as not requiring modulators, dispensing with decoupling, incorporating constraint handling, and facilitating online optimization. However, it heavily relies on a model and consequently experiences a substantial loss of control effectiveness in the face of system parameter variations. In light of this, this study presents an active-disturbance-rejection-based three-vector sequence model predictive control approach. This method effectively mitigates the influence caused by changes in system parameters, endowing the system with self-disturbance rejection capabilities and enhancing its fault recovery abilities. The method employs self-disturbance control to track voltage reference values and employs the concept of sequence control to eliminate weighting factors in the cost function. Furthermore, it employs three-vector control to achieve error-free operation. The simulation results confirmed that the proposed method effectively minimizes voltage and power transients. It demonstrated superior control effectiveness and shorter response times, enabling the system to rapidly restore to a stable operational state following disturbances. [ABSTRACT FROM AUTHOR]

The conventional finite control set model predictive current control (FCS-MPCC) suffers from suboptimal steady-state performance, primarily due to the limited selection of only eight basic voltage vectors in each control cycle. To overcome this limitation, the proposed extended control set MPCC (ECS-MPCC) utilizes an control set consisting of 818 selectable vectors, enabling a more refined voltage output and achieving a deadbeat response for current control by minimizing the cost function. To mitigate the computational burden resulting from the substantial increase in voltage vectors, a simplified search strategy is devised, which can be extended to other multi-objective cost functions. Remarkably, based on the inherent parallelism of the algorithm, the ECS-MPCC is implemented on an FPGA, further reducing the overall control time of the current loop to an impressive 0.61 μs. Through simulation and experimental tests on a laboratory PMSM driver, the effectiveness of the proposed ECS-MPCC strategy is validated. The experimental results demonstrate a significant reduction of 79% in the total harmonic distortion of phase currents compared to the conventional FCS-MPCC approach. This improvement underscores the superiority of the ECS-MPCC in enhancing the performance of PMSM drives, thereby illustrating its potential for practical implementation in real-world applications. [ABSTRACT FROM AUTHOR]

Purpose: Dual-thrust hybrid unmanned aerial vehicle (UAV) technology offers a highly robust and efficient system that incorporates the take-off and landing capabilities of rotary-wing aircraft with the endurance capacities of fixed-wing aircraft. The purpose of this study is to model and control a hybrid UAV in three distinct flight modes: rotary-wing, fixed-wing and over-actuated model. Design/methodology/approach: Model predictive control (MPC) along with linear models are applied to design controllers for the rotary-wing or vertical take-off and transition to the fixed-wing flight. The MPC algorithm is implemented with two approaches, first in its usual form and then in a new form with the help of tracking error variables as state variables. Findings: Because the tracking error variables are more compatible with the cost function used in MPC, the results improve significantly. This is especially important for a safe and stable transition from rotary-wing to fixed-wing flight, which should be done quickly. The authors also propose a control allocation strategy with MPC algorithm to exploit the thrust and control inputs of both rotary-wing and fixed-wing systems for the transition phase. As the control system is over-actuated, the proposed algorithm distributes the control signal among the actuators better than the MPC alone. The numerical results show that the flight trajectory is also improved. Originality/value: The research background is reviewed in the introduction section. The other sections are originally developed in this paper to the best of the authors' knowledge. [ABSTRACT FROM AUTHOR]

There are issues with flexible DC transmission system such as a lack of control freedom over power flow. In order to tackle these issues, a DC power flow controller (DCPFC) is incorporated into a multi-terminal, flexible DC power grid. In recent years, a multi-port DC power flow controller based on a modular multi-level converter has become a focal point of research due to its simple structure and robust scalability. This work proposes a model predictive control (MPC) strategy for multi-port interline DC power flow controllers in order to improve their steady-state dynamic performance. Initially, the mathematical model of a multi-terminal DC power grid with a multi-port interline DC power flow controller is developed, and the relationship between each regulated variable and control variable is determined; The power flow controller is then discretized, and the cost function and weight factor are built with numerous control objectives. Sub module sorting method and nearest level approximation modulation regulate the power flow controller; Lastly, theMATLAB/Simulink simulation platformis used to verify the correctness of the establishedmathematicalmodel and the control performance of the suggestedMPC strategy. Finally, it is demonstrated that the control strategy possesses the benefits of robust dynamic performance, multiobjective control, and a simple structure. [ABSTRACT FROM AUTHOR]

The paper considers the model predictive current control (MPCC) of an induction motor (IM) drive and evaluates five IM models of different complexities—from conventional to magnetic saturation, iron losses, and stray-load losses—for the MPCC design. The validity of each considered IM model and the corresponding MPCC algorithm is evaluated by comparison of the following performance metrics: the total harmonic distortion of the stator current, the average switching frequency, the rotor flux magnitude error, the rotor flux angle error, and the product of the first two metrics. The metrics' values are determined in wide ranges of the rotor speed (0.1–1 p.u.) and load torque (0–1 p.u.) through simulations performed in the MATLAB Simulink environment. The obtained results allow us to identify the IM model that offers the best tradeoff between the practicability and accuracy. Furthermore, a control effort penalization (CEP) is suggested to reduce the average switching frequency and, hence, the power converter losses. This involves constraining the simultaneous switching to a maximum of two branches of the three-phase power converter, as well as inclusion of the weighted switching penalization term in the cost function. Finally, the performance—both steady-state and dynamic—of the proposed MPCC system with CEP is compared with that of the analogous field-oriented controlled (FOC) IM drive. The inverter switching frequency is reduced more than twice by including the frequency-dependent iron-loss resistance in the MPCC. It is additionally reduced by implementing the proposed CEP strategy without sacrificing many other performance metrics, thus achieving a performance comparable to the FOC IM drive. [ABSTRACT FROM AUTHOR]

In the field-weakening region, the traditional field-weakening method for induction motor drives based on model predictive control (MPC) is to take a no-load operation as the premise and adjust the flux reference in the cost function proportional to the inverse of the rotor speed, which leads to poor torque output. This paper presents a novel field-weakening method for IM drives based on MPC. Considering the induction motor field-weakening limiting conditions and according to the speed adaptive field-weakening strategy with a voltage closed-loop, the speed adaptive field-weakening controllers were designed to optimize the references of the excitation current and torque current. In the rotor field-orientation d–q coordinate system, the stator flux amplitude and torque reference values were optimized by the optimal distribution current. Then, according to the dead-beat control principle, they were converted into an equivalent stator flux vector reference. Moreover, the stator voltage vector reference can be obtained. For an induction motor fed by a three-level neutral point clamped (3L-NPC) inverter, the cost function was constructed by combining all the constraints, including the voltage vector, the neutral potential balance, and the switching frequency. In this way, the high-performance field-weakening operation for the induction motor based on a model predictive control can be realized. The simulation and experiment results show that the proposed method can increase the torque output by 22% in the field-weakening region; at the same time, the steady characteristics and the dynamic response performance can be maintained well. [ABSTRACT FROM AUTHOR]

For the dual active bridge (DAB) DC‐DC converters, parameter uncertainty (circuit parameters, input and/or load perturbations) affect their performance, such as the dynamic performance and backflow power. Here, to deal with the uncertainties, a robust model predictive control (RMPC) for the DAB DC‐DC converter under extended‐phase‐shift (EPS) control is proposed to improve dynamic performance and reduce the backflow power of the DAB for the first time. First, a cost function consisting of backflow power and the output voltage is established. Second, considering the uncertainty of the circuit parameters, a robust term is added to the prediction model to increase the robustness. Finally, the effectiveness and superiority of the proposed method are verified by experiments and comparisons with existing methods. The proposed RMPC method shows superior performance in the sense of improved dynamic performance and reduced backflow power. [ABSTRACT FROM AUTHOR]

Traditional model predictive control (MPC) for the induction motor fed by a three-level inverter needs to explore 27 voltage vectors to obtain the optimal one, which leads to high switching frequency and requires too much computation. To solve this issue, a low switching frequency model predictive control with partition optimization is proposed. First, the reference voltage vector can be gained from the prediction model at the next time, and the space voltage vector plane is divided into 12 sectors for further vector choice. Furthermore, considering inverter constraints, the candidate voltage vectors are determined according to the sector location of the reference voltage vector. In this way, the candidate vectors can be reduced to 3 at most. Then, a boundary circle limit is designed to avoid unnecessary switch changes. If the reference voltage vector is within the boundary limit, the switches do not act, which can reduce the system switching frequency without introducing the extra weight coefficient into the cost function. These selected voltage vectors are substituted into the cost function to determine the optimal one. Finally, the neutral point voltage deviation is controlled by the positive and negative redundant small vectors to realize the multi-objective constraint without weighting coefficients. The simulation results show that the proposed control method can significantly reduce the switching frequency; at the same time, both the dynamic and steady performances can be maintained well, and the cost function has no weight coefficients. [ABSTRACT FROM AUTHOR]

The model predictive control (MPC) tries to find the best control output by optimizing among the predictions. In this context, determining the objective function of optimization is a critical process. This study discusses the velocity control of the mini brushed direct current (BDC) motor used in the actuator with MPC. Four different functions have been defined to design the most appropriate cost function for MPC by considering the parameters that may be effective in the motor dynamics such as velocity, current, power, and switching states. Software in the Loop (SIL), one of the modeling-based testing methods, was used within the scope of testing the algorithms. After the SIL tests, it has been observed that the mini BDC motor can be successfully controlled with simple adjustments to the cost function of the MPC. As a result of the analysis obtained the best results with the objective function consisting of velocity error, estimated current, and the difference between the two estimated velocity values. With this controller, the BDC motor can be controlled without overshoot and with a steady-state error of less than 2% under load. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the problem of a fully actuated hexarotor performing a physical interaction with the environment through a rigidly attached tool is considered. A nonlinear model predictive impedance control (NMPIC) method is proposed to achieve the goal in which the controller is able to simultaneously handle the constraints and maintain the compliant behavior. The design of NMPIC is the combination of a nonlinear model predictive control and impedance control based on the dynamics of the system. A disturbance observer is exploited to estimate the external wrench and then provide compensation for the model which was employed in the controller. Moreover, a weight adaptive strategy is proposed to perform the online tuning of the weighting matrix of the cost function within the optimal problem of NMPIC to improve the performance and stability. The effectiveness and advantages of the proposed method are validated by several simulations in different scenarios compared with the general impedance controller. The results also indicate that the proposed method opens a novel way for interaction force regulation. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive current control (MPCC) method for a three-leg two-phase voltage source inverter to reduce the ripple of the output current. The proposed MPCC method is based on discrete space vector modulation, which is implemented by eliminating nonlinear modulation regions and generating virtual voltage vectors based on the stationary reference voltage vector axis. In addition, based on a voltage angle estimated using load parameters, the candidate voltage vectors in a specific region are generated, which reduces the calculation burden. Subsequently, the cost function is calculated using these candidate voltage vectors, and the optimal voltage vector selected using the proposed MPCC method is more accurate than the vector selected using the conventional MPCC method. Therefore, by applying the proposed method to increase the number of virtual voltage vectors, it is possible to obtain a delicate reference voltage. Consequently, the total harmonic distortion of the output current is reduced, resulting in an increase in power quality. The effectiveness and feasibility of the proposed method are verified through simulations and experiments. [ABSTRACT FROM AUTHOR]

We present a Model Predictive Control (MPC) framework to simulate movement in interaction with computers, focusing on mid-air pointing as an example. Starting from understanding interaction from an Optimal Feedback Control (OFC) perspective, we assume that users aim at minimizing an internalized cost function, subject to the constraintsimposed by the human body and the interactive system. Unlike previous approaches used in HCI, MPC can compute optimal controls for nonlinear systems. This allows to use state-of-the-art biomechanical models and handle nonlinearitiesthat occurin almost any interactive system. Instead of torque actuation, our model employs second-order muscles acting directly at the joints. We compare three different cost functions and evaluate the simulation against user movements in a pointing study. Our results show that the combination of distance, control, and joint acceleration cost matches individual users’ movements best, and predicts movements with an accuracy that is within the between-user variance. To aid HCI researchers and designers in applying our approach for different users, interaction techniques, or tasks, we make our SimMPC framework, including CFAT, a tool to identify maximum voluntary torques in joint-actuated models, publicly available, and give step-by-step instructions. [ABSTRACT FROM AUTHOR]

Motion planning, decision making, and control are vital functions in autonomous driving for accomplishing the desired driving task while considering passenger comfort, road infrastructure, and surrounding traffic participants. Model predictive control (MPC) is a promising method for simultaneously realizing these functions. However, formulating a single MPC that can run through all driving scenarios is difficult, and previous research has often been conducted to design an MPC for a specific driving task. To extend the availability of MPC for all driving tasks, smooth switching between different MPCs designed for each driving task must be addressed. One of the difficulties in switching between MPCs is guiding the state to a feasible set of optimization problems after switching. In this paper, we present a new framework to realize the smooth connection of MPCs, that is, to reduce the optimization infeasibility at the time of MPC switching. In our proposed method, two general nonlinear MPCs with different state spaces, cost functions, constraints, and formulations can be systematically switched via automatically generated intermediate‐MPCs without requiring any particular alterations. This can help reduce the system complexity of the hybrid MPC system. [ABSTRACT FROM AUTHOR]

This paper addresses a non-fragile robust model predictive control design for a class of continuous-time uncertain systems with multiple state-delay and constrained control signals. The parameters of the system and the control gain are assumed to have perturbation in the additive form. The Lyapunov–Krasovskii functional approach is employed to derive sufficient conditions for determining a non-fragile robust state-feedback controller for all admissible uncertainties by minimising the upper bound of the defined quadratic cost function with respect to some linear matrix inequalities (LMIs). An additional inequality condition is imposed to address the constraint associated with the control signals. Numerical simulations are provided to assess the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

The low frequency ripple of the input side current of the single-phase inverter will reduce the efficiency of the power generation system and affect the overall performance of the system. Aiming at this problem, this paper proposes a two-modal modulation method and its MPC multi-loop composite control strategy on the circuit topology of a single-stage boost inverter with a buffer unit. The control strategy achieves the balance of active power on both sides of AC and DC by controlling the stable average value of the buffer capacitor voltage, and provides a current reference for inductance current of the DC input side. At the same time, the MPC controller uses the minimum inductor current error as the cost function to control inductor current to track its reference to achieve low frequency ripple suppression of the input current. In principle, it is expounded that the inverter using the proposed control strategy has better low frequency ripple suppression effect than the multi-loop PI control strategy, and the conclusion is proved by the simulation data. Finally, an experimental device of a single-stage boost inverter using MPC multi-loop composite control strategy is designed and fabricated, and the experimental results show that the proposed research scheme has good low frequency ripple suppression effect and strong adaptability to different types of loads. [ABSTRACT FROM AUTHOR]

Model predictive control has been wildly used in the modern power electronic converter with the advantages of the multi‐objective optimization, no additional modulator is required and small grid current harmonic. However, the switching operation is not constant and higher sampling frequency must be required. A novel direct power control using model predictive control (DPC‐MPC) strategy is presented in this paper, which achieve the optimal vector prediction scheme with constant switching frequency operation. The calculation process is greatly simplified due to the optimal voltage vector generated directly, then, it applied to the control of a three‐phase three‐level T‐type grid converter. A cost function is first built to balance the neutral‐point‐potential voltage. After then, the cost function is updated by considering the delay caused by the calculation, sampling and gate driver. Finally, a comprehensive comparative study with the conventional DPC‐MPC strategy is introduced to verify the superiority of the presented DPC‐MPC. The simulation and experimental results show that the steady and the transient performance of the grid current with the proposed DPC‐MPC control strategy are effectively improved. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposes a self-triggered model predictive control algorithm for a class of discrete-time Markov jump linear systems with parametric uncertainties. A dual-mode design framework is employed to guarantee the optimality, feasibility and robust stability. The online control law is obtained by minimising the worst case of a novel cost function, which contains a self-triggered mechanism. Finally, a numerical example verifies the effectiveness and resource-saving capacity of the developed approach. [ABSTRACT FROM AUTHOR]

A new approach to nonlinear Model Predictive Control (MPC) is discussed in this work. A custom user-defined cost function is used in place of the typically considered quadratic norm. An approximator of the cost function is applied to obtain a computationally simple procedure and linearization of two trajectories is carried out online. The predicted output trajectory of the approximator and the predicted trajectory of the manipulated variable, both over the prediction horizon, are repeatedly linearized online. It yields a simple quadratic programming task. The algorithm is implemented for a simulated neutralization benchmark modeled by a neural Wiener model. The resulting control quality is excellent, identical to that observed in the MPC scheme with nonlinear optimization. Validity of the described MPC algorithms is demonstrated when only simple box constraints are considered on the process input variable and in a more demanding case when additional soft limitations are put on the predicted output. Two structures of the approximator are compared: polynomial and neural; the advantages of the latter one are shown and stressed. • Computationally simple MPC algorithms that use a user-defined custom cost function are described. • An approximator of the cost-function is used; efficiency of polynomial and neural approximators is compared. • Two advanced trajectory linearizations are performed online to yield a simple quadratic programming task. • Effectiveness of the presented approaches is discussed for a pH process. • Different simulation scenarios are considered, including soft constraints imposed on the process output. [ABSTRACT FROM AUTHOR]

In this article, the estimation of fatigue is implemented in the cost function of a gradient‐based model predictive controller (MPC). This is a challenging problem, because calculating fatigue leads to a non‐standard and discontinuous cost function. Based on a brief previous publication, in the present work the method is derived, explained, and assessed in detail. The key enablers of the proposed method are a sequential implementation of MPC, the periodic substitution of discontinuous aspects of the cost function by linear functions, and the assumption of a sufficiently infrequent switching of this substitution. Fatigue cost gradients are obtained efficiently by automatic differentiation. The method is implemented in an economic nonlinear model predictive controller (ENMPC), which optimally balances revenue and fatigue cost. This novel formulation is applicable to a very wide range of domains, and it is demonstrated here on the control of a wind turbine. The proposed ENMPC is fully parameterized by physical and monetary variables, and outperforms a conventional ENMPC based on the literature. The method is assessed by considering various metrics, including frequency spectra, damage estimation, switching, and gradient dynamics, which together provide useful insight into its main characteristics and an initial assessment of its performance. [ABSTRACT FROM AUTHOR]

A cabin climate control system, often referred to as a heating, ventilation, and air conditioning (HVAC) system, is one of the largest auxiliary loads of an electric vehicle (EV), and the real‐time optimal control of HVAC brings a significant energy‐saving potential. In this article, a linear‐time‐varying (LTV) model predictive control (MPC)‐based approach is presented for energy‐efficient cabin climate control of EVs. A modification is made to the cost function in the considered MPC problem to simplify the Hessian matrix in utilizing quadratic programming for real‐time computation. A rigorous parametric study is conducted to determine optimal weighting factors that work robustly under various operating conditions. Then, the performance of the proposed LTV‐MPC controller is compared against a rule‐based (RB) controller and a nonlinear economic MPC (NEMPC) benchmark. Compared with the RB controller benchmark, the LTV‐MPC reaches the target cabin temperature at least 69 s faster with 3.2% to 15% less HVAC system energy consumption, and the averaged cabin temperature difference is 0.7°C at most. Compared with the NEMPC, the LTV‐MPC controller can achieve comparable performance in temperature regulation and energy consumption with fast computation time: the maximum differences in temperature and energy consumption are 0.4°C and 2.6%, respectively, and the computational time is reduced 72.4% on average with the LTV‐MPC. [ABSTRACT FROM AUTHOR]

Due to the nonminimum phase characteristics and nonlinearity of boost converters, the control design is always a challenging issue. A novel model predictive control strategy is proposed for the boost converter in this work. First, the Super-Twisting algorithm is applied to current control, and the input–output plant for voltage control is derived based on the linearization technique. All the model uncertainties are defined as lumped disturbances, and a generalized proportional integral observer is designed to estimate the lumped disturbance. Second, a composite predictive approach is developed on the basis of the predictive model and disturbance estimations. By solving the cost function directly, the optimal control law is derived explicitly. Lastly, the effectiveness of the proposed control strategy is verified by both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Virtual vector is applied in neutral‐point‐clamped (NPC) three‐level converter finite control set model predictive control (FCS‐MPC) strategy due to its good performance of less current ripples and high power efficiency. However, in the process of adding virtual vectors, some redundant virtual vectors will be selected. Excessive virtual vector will increase the computational burden and affect grid‐tied current performance. A virtual vector optimal selection strategy for model predictive control to determine the vectors is proposed. The strategy calculates the fluctuation of grid‐tied current through theoretical analysis. Based on the analysis of amplitude fluctuation of grid‐tied current, the graphics of coverage angle visualization can be obtained under different virtual vector. The angle of the required virtual vectors is obtained through the graphics of coverage angle visualization. The number of virtual vector can be determined when the error current angle range covers the entire vector graph. The proposed control strategy was compared with the conventional control strategies, including excessive virtual vectors or with no any virtual vector. Experimental results show that the proposed control strategy can significantly reduce THD, grid‐tied current error and computational burden. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this study, first a model predictive control (MPC) solution is utilized to control the quadrotor model in both fault-free and faulty modes by employing a cost function. For this aim, the obtained non-linear continuous model has been discretized after linearization around an equilibrium point. Then, a fault detection and diagnosis (FDD) and fault tolerant control (FTC) strategy is proposed based on MPC in order to neutralize the effect of fault. The method of FDD and FTC designing is based on analytical redundancy relations (ARRs) which detects and tolerates the fault by producing a residual signal. Therefore, this method presents a complete FTC solution to fault issue without using observer. In addition, this solution has robustness against the uncertainties and disturbances which may challenge the model in practical situations. The discussed fault in this paper is a sensor fault which is modeled as an undesired function which affects the sensor of roll angle. However, the proposed FTC can tolerate all types of faults. The results of implementation of this method are validated under various simulation scenarios. [ABSTRACT FROM AUTHOR]

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In order to improve the control accuracy and robustness of permanent magnet synchronous motor (PMSM) servo system, a new non‐cascade predictive control strategy is proposed to replace the traditional three‐closed‐loop servo control in this paper. The design process is as follows. First, the state variables and a discrete‐time state space model of motor are defined; meanwhile, the lumped term of disturbance is included in the motor model. Second, the optimal cost function is chosen to design the model predictive controller (MPC). Then, according to the uncertain parameters and external disturbance, the sliding mode observer (SMO) is introduced to estimate and compensate the disturbance. Finally, the comparative experimental result proves that the proposed predictive control method not only has less regulation parameters but also has high position tracking accuracy and strong anti‐interference ability even under different conditions. [ABSTRACT FROM AUTHOR]

This paper presents a stability analysis tool for model predictive control (MPC) where control action is generated by optimising a cost function over a finite horizon. Stability analysis of MPC with a limited horizon but without terminal weight is a well-known challenging problem. We define a new value function based on an auxiliary one-step optimisation related to stage cost, namely optimal one-step value function (OSVF). It is shown that a finite horizon MPC can be made to be asymptotically stable if OSVF is a (local) control Lyapunov function (CLF). More specifically, by exploiting the CLF property of OSFV to construct a contractive terminal set, a new stabilising MPC algorithm (CMPC) is proposed. We show that CMPC is recursively feasible and guarantees stability under the condition that OSVF is a CLF. Checking this condition and estimation of the maximal terminal set are discussed. Numerical examples are presented to demonstrate the effectiveness of the proposed stability condition and corresponding CMPC algorithm. [ABSTRACT FROM AUTHOR]

In order to extend the speed range and reduce power electronics devices of the permanent-magnet linear motor traction system, a half-centralized open-end winding topology is studied in this article, in which two movers are supplied by three voltage-source-inverters (VSIs). All these VSIs share a common dc-bus voltage, and the zero-sequence current becomes a challenge. In order to solve cope with this challenge, two phase model predictive voltage controls (PMPVCs) are proposed. Referring to the traditional finite-control-set MPVC (FCS-MPVC), an FCS-PMPVC scheme is proposed and the optimal phase voltage group is determined by minimizing three independent phase cost functions. For FCS-PMPVC, 21 phase voltage group are evaluated while the evaluation number of the traditional FCS-MPVC is 512. In order to reduce the computation burden furthermore, a new concept phase voltage vector (PVV) is defined, and the optimal PVV is directly determined by the nearest distance principle instead of the time-consuming evaluation. The second PMPVC is named as geometrical-location PMPVC (GL-PMPVC). Both PMPVCs have same performances while the computation burden of GL-PMPVC is nearly 47% of that of FCS-PMPVC. The effectiveness of the proposed PMPVCs have been verified by experimental results. [ABSTRACT FROM AUTHOR]

The conventional model predictive control (MPC) is an attractive control scheme for the regulation of multiphase electric drives, since it easily exploits their inherent advantages. However, as the number of phases increases, the MPC's complexity increases exponentially, posing a high computational burden. Additionally, the MPC still presents other issues related to the weighting factor design in the cost function. Accordingly, this paper proposes a low-complexity hysteresis model predictive current control (HMPCC) that can significantly reduce the computational burden, improve the motor's performance, and completely avoid the weighting factor design. The proposed method is a hybrid control method, consisting of two distinct controls that complement one another. The hysteresis control is used to reduce the number of iterations per sampling period, thereby reducing the computational effort required to choose the voltage vector that actively produces torque/flux, and nullifying the weighting factor requirement. Finally, the MPC is used to improve the torque and current quality. The effectiveness of the proposed method is verified through experimental data, and the results emphasize the improvement of the proposed HMPCC scheme. [ABSTRACT FROM AUTHOR]

In this paper, a guidance and control system for multi-satellite assembly missions in proximity operations is proposed. Utilizing the Tschauner-Hempel relative motion equations, the system is developed within the framework of model predictive control. Linear inequality constraints are employed to handle control input saturation of thrusters and to ensure collision avoidance between satellites. Additionally, a new line-of-sight constraint method is introduced to facilitate smooth docking while simplifying the optimization problem. The proposed algorithm aims to minimize a predefined cost function, which is a weighted sum of state errors and control efforts. At each sampling time, an online quadratic optimization problem is solved to determine the optimal control action that satisfies all mission constraints. Nonlinear numerical simulations in MATLAB are used to demonstrate that the proposed guidance and control system achieves the desired performance. The algorithm is tested for various multi-satellite assembly configurations (cubic and hexagonal) in elliptic orbits of arbitrary eccentricity. A comparative study is conducted between the proposed algorithm and a linear model predictive control algorithm based on the Clohessy-Wiltshire linear model. The results show that the proposed algorithm achieves superior performance in terms of constraint satisfaction and control efforts. [ABSTRACT FROM AUTHOR]

Grid faults are found to be one of the major issues in renewable energy systems, particularly in wind energy conversion systems (WECS) connected to the grid via back-to-back (BTB) converters. Under such faulty grid conditions, the system requires an effective regulation of the active (P) and reactive (Q) power to accomplish low voltage ride through (LVRT) operation in accordance with the grid codes. In this paper, an improved finite-control-set model predictive control (FCS-MPC) scheme is proposed for a PMSG based WECS to achieve LVRT ability under symmetrical and asymmetrical grid faults, including mitigation of DC-link voltage fluctuation. With proposed predictive control, optimized switching states for cost function minimization with weighing factor (WF) selection guidelines are established for robust BTB converter control and reduced cross-coupling amid P and Q during transient conditions. Besides, grid voltage support is provided by grid side inverter control to inject reactive power during voltage dips. The effectiveness of the FCS-MPC method is compared with the conventional proportional-integral (PI) controller in case of symmetrical and asymmetrical grid faults. The simulation and experimental results endorse the superiority of the developed FCS-MPC scheme to diminish the fault effect quickly with lower overshoot and better damping performance than the traditional controller. [ABSTRACT FROM AUTHOR]

Ensuring safety while retaining maximum performance is a basic requirement for automatic cyber-physical systems, especially for safety-critical applications. A quadratic programming optimization framework called MPC-CBF has recently been presented, which directly unifies model predictive control (MPC) with control barrier functions (CBFs) over the prediction time horizon. However, the conservative nature of CBFs can lead to feasibility problems in real applications. Based on the analysis of the role of the decay rate and the conservative accumulation phenomenon in standard CBF formulations, this paper proposes to directly optimize CBF constraints within the MPC framework. By regarding CBFs as a safety restriction level indicator and an optimizable constraint within the MPC framework, the trade-off between feasibility and safety can be adaptively optimized. The proposed Optimizable CBF (OCBF) model removes the hyper-parameters selection problem in standard CBFs and can adaptively adjust the safety restriction level and increase behavior diversity by adding the corresponding objects in the cost function in MPC. To eliminate the accumulation effects of actual values of the CBF constraints in previous time steps, this paper further proposes a General OCBF (GOCBF) formulation. Compared with existing formulations, the safety margin defined in our GOCBF has intuitive physical meanings and thus provides a more flexible and intuitive mechanism to compromise different objects in terms of ensuring safety while not undermining basic feasibility. Experimental results demonstrate that our algorithm provides a more flexible and intuitive mechanism to achieve this, thus improving feasibility and adding behavior diversity in the MPC-CBF framework. [ABSTRACT FROM AUTHOR]

In this paper, an artificial neural network (ANN) based system-level model predictive control framework is established for a variable air volume (VAV) system to improve its robustness and energy efficiency. The VAV system consists of three processes: the zone temperature process, the damper process and the supply air volume process of the air handling unit connecting the ductwork for all of the boxes. An ANN-based predictive controller is designed for the zone temperature process, and a PI controller is used for the damper process; they operate in a cascaded manner. Another ANN predictive controller tracks the total supply air volume subject to the cooling load constraints from the lower level VAV boxes and minimizes the fan energy consumption. The ANN controllers are optimized online by the Lagrange variational approach (LVA) to minimize the system cost function and implement a feedback receding horizon optimal control. With the LVA based gradient descent training algorithm, the weight convergence of the ANN predictive controllers and stability of the control system is obtained with small computational and storage needs. The modeling methodology, based on ANNs for the above processes, is developed and calibrated with actual data measured from a site. All of the data processing and control strategy implementations are based on extensive measurements collected from a laboratory building located in the campus of Beijing University of Civil Engineering and Architecture , Beijing , China. The experimental results show that an additional 6.12% energy savings is achieved. This is accomplished by introducing the fan control signal into the system cost function as the performance index of energy consumption with a 0.2 penalty coefficient. In the presence of un-modeled disturbances and modeling errors, the framework also reduces the temperature oscillation at least by approximately 60% compared with PI control. • A neural network-based predictive control strategy for VAV systems is designed. • Cascade predictive control is implemented for zone temperature and air flow rate. • The duct air volume process achieves total airflow predictive control with savings in fan energy consumption. • The predictive controller using neural network implementation, easy to achieve nonlinear system optimization. • The control strategy is robust to un-modeled disturbances and modeling errors. [ABSTRACT FROM AUTHOR]

This article proposes a new control strategy to regulate the six‐phase induction machine fed by a nine‐switch converter. The traditional model predictive control method is a favorable feedback control routine in variable‐drive applications. However, the lack of the term that assess the closed‐loop stability in the conventional cost function formulation may cause potential closed‐loop instability and be incapable of tolerating the parameter mismatch. Furthermore, the conventional model predictive control formulation cannot ensure global optimality in case of deviation from the nominal operating point. In this paper, the closed‐loop stability is enhanced by including a sliding mode stability term in the objective function. The traditional cost function is redeveloped as a sliding mode control stability term, and the selection of the control input that satisfies the closed‐loop loop stability condition is ensured. The robustness performance of the system is improved, and a better steady‐state performance is achieved. The proposed control strategy is proved using a hardware setup including a six‐phase 30° spatially shifted induction motor and nine‐switch converter prototype. The experimental results demonstrate that a secured six‐phase induction motor operation is guaranteed while improving the closed‐loop robustness. [ABSTRACT FROM AUTHOR]

The method of a designed trajectory tracking for an underactuated unmanned surface vehicle (USV) in the presence of ocean disturbances is addressed in this paper; the differential flatness theory is applied to get the reference inputs and speed states at the reference position trajectory. Second, a transition process is arranged for the reference trajectory to reduce the overshoot of the actuator, which caused by the large deviation in the initial tracking. Third, the nonlinear disturbance observer is designed to obtain the estimated values of unknown disturbances in the ocean. Then, a controller-based model predictive control (MPC) and terminal cost function is designed for the nominal system. The inherent robustness of the controller and estimates of the observer are used to resist and compensate disturbances. Finally, the simulation experiments of linear trajectory and sinusoidal trajectories are carried out to prove the effectiveness and reliability of the control algorithm designed. [ABSTRACT FROM AUTHOR]

Self-triggered and event-triggered control have been widely implemented in various fields recently, especially in networked control systems. In order to save communication resources, trigger control conducts aperiodic control strategies instead of traditional time-driven scheme. Based on the self-triggered control mechanism, this paper investigates self-triggered strategy in model predictive control for nonlinear continuous-time networked system, selects aperiodic samples to ensure system performance while reducing computing energies and improving the resource usage efficiency. The usage of communication resources is quantified as a damping parameter that can characterize the communication effects in total cost function, such that the optimal sampling time and input trajectory can be obtained synchronously. In theoretical results, the feasibility of the proposed self-triggered strategy and the asymptotic stability of the nonlinear system are guaranteed. Furthermore, a simulation is given to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]