Modulated Model Predictive Control Research Articles

A Hybrid Multiobjective Control Strategy Based on Combination of Modulated Model Predictive Control and Phase‐Shifted Modulation for a Unidirectional Five‐Level Rectifier

ABSTRACTThe unidirectional multilevel rectifier has immense potential in high‐power medium‐voltage industrial applications. This paper proposes a multiobjective control method based on the combination of optimal‐vector‐pair modulated model predictive control (OVP‐M2PC) and a phase‐shifted modulation strategy for a unidirectional five‐level rectifier. This strategy takes both of the inductor current tracking and capacitor voltage balancing as the control objectives. First, an optimal vector pair is determined based on the AC‐side current variation rate, and then, through combining the calculated action duration of OVP with carrier phase‐shifted modulation, inductor current tracking is achieved. Second, capacitor voltage balancing control is carried out by compensating for the duty cycles which also decouples the input current tracking and output capacitor voltage balance control. Compared with traditional FCS‐MPC, under the proposed control strategy, complex cost function calculation and weighting factor tuning are not required, which much reduced computational burden. Multiobjective control is achieved with better performance in both steady‐state and dynamic conditions. The superiority of the proposed strategy over traditional FCS‐MPC are validated through simulations and hardware experiments.

New stability theory of model predictive control: modified stage cost approach

This paper presents a promising new approach to establish the stability of finite receding horizon control with a terminal cost. Departing from the traditional approaches of using the property of the terminal cost or relaxed Lyapunov inequalities, this paper establishes its stability based on the property of a modified stage cost. First, we rotate the stage cost with the terminal cost. Then a one-step optimisation problem is defined based on this augmented stage cost. It is shown that a slightly modified Model Predictive Control (MPC) algorithm is stable if the value function of the augmented one-step cost (OSVF) is a Control Lyapunov Function (CLF). Stability for MPC algorithms with zero terminal cost or even negative terminal cost can be unified with this new approach. Combining it with the existing MPC stability theories, we are able to significantly relax the stability requirement on MPC and extend the stabilising MPC design space to the region that no existing MPC stability theories can cover. The proposed stage cost-based approach will help to further reduce the gap between stability theory and practical applications of MPC and other optimisation-based control methods.

Motion/force coordinated trajectory tracking control of nonholonomic wheeled mobile robot via LMPC-AISMC strategy

Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is developed to implement this motion/force coordinated control objective in this paper. Firstly, the outer-loop motion controller is presented using Laguerre functions modified model predictive control (LMPC). Optimised solution condition is introduced to reduce the number of LMPC solutions. Secondly, an inner-loop force controller based on adaptive integral sliding mode control (AISMC) is constructed to ensure the desired velocity tracking and output driving torques by combining second-order nonlinear extended state observer (ESO) with the estimation of dynamic uncertainties and external disturbances during WMR travelling process. Then, Lyapunov stability theory is utilised to guarantee the consistent final boundedness of the designed controller. Finally, the system is numerically simulated and practically verified. The results show that the double-closed-loop control strategy devised in this paper has better control performance in terms of complex trajectory tracking accuracy, system resistance to strong interference and computational timeliness, and is able to realise effective coordinated control of WMR motion/force.

Discontinuous Modulated Model Predictive Control for Low Inductance High-Speed Electric Drive Applications

Discontinuous Modulated Model Predictive Control for Low Inductance High-Speed Electric Drive Applications

Lagrange Multipliers Aided Modulated Model Predictive Control Technique for PMSM Drives

The modulated model predictive control (MMPC) method utilizes multiple voltage vectors in sequence within one switching cycle, achieving a fixed switching frequency. However, the control performance of using the MMPC technique still suffers from imprecise vector duration derivation and high computational burden. To address this, this paper proposes an improved MMPC technique aided by the Lagrange multiplier method to accurately calculate the duration of each vector, and then efficiently select the optimal voltage vector. Compared to the existing MMPC techniques, the proposed one exhibits a lower computational burden and superior steady‐state performance. Those benefits have been proven by the experimental results of a 1.5 k rpm permanent magnet synchronous motor drive. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Optimized Field-Weakening Operation of PMSM Modulated Model Predictive Control Using Predictive Current Error Margin

Optimized Field-Weakening Operation of PMSM Modulated Model Predictive Control Using Predictive Current Error Margin

Modified Model Predictive Control for Coordinated Signals along an Arterial under Relaxing Assumptions

This paper proposes modified model predictive control (MMPC) for coordinated signals, aiming to enhance a model’s fidelity to the realistic traffic environment by relaxing typical assumptions. We focus on the arterial, where every intersection is equipped with a dual-ring-barrier signal controller that complies with the standards of the National Electric Manufacturers Association. MMPC employs the store-and-forward model to describe traffic flow, thereby transforming the signal control problem into a model-based rolling-horizon optimization problem, in which the prediction horizon is composed of several future sample intervals, commonly equal to the cycle length. A radar detector is used to collect vehicle data upstream of the stop line at every sampling instant. The optimization problem is solved to minimize the number of vehicles within the prediction horizon, and the next timing plan is determined based on the optimization results. Constraints are added and modified in order to incorporate the typical relaxed assumptions in the optimization process. For this purpose, MMPC introduces a transition-free ring-barrier structure, vehicle distribution ratio, and percent arrival before the end of green. Simulation results indicate that coordination can be maintained by MMPC without the need for transitions, and the estimation of current and future traffic states can be improved with the assistance of modified constraints. Compared with benchmark techniques, MMPC offers superior vehicle progression for coordinated movement and significant improvements in delays, number of stops, and total travel time from a system-wide perspective, with an acceptable small increase in runtime.

Online Learning and Control for Data-Augmented Quadrotor Model

The ability to adapt to changing conditions is a key feature of a successful autonomous system. In this work, we use the Recursive Gaussian Processes (RGP) for identification of the quadrotor air drag model online, without the need to precollect training data. The identified drag model then augments a physics-based model of the quadrotor dynamics, which allows more accurate quadrotor state prediction with increased ability to adapt to changing conditions. This data-augmented physics-based model is utilized for precise quadrotor trajectory tracking using the suitably modified Model Predictive Control (MPC) algorithm. The proposed modelling and control approach is evaluated using the Gazebo simulator and it is shown that the proposed approach tracks a desired trajectory with a higher accuracy compared to the MPC with the non-augmented (purely physics-based) model.

Improved Modulated Model Predictive Control Technique for PMSM Drives Using the Vector Projection

Compared to the model predictive control (MPC) technique, the modulated MPC (M2PC) method applies multiple voltage vectors in order within one switching cycle, enabling a fixed switching frequency and better motor control performance. However, the M2PC technique suffers from a significant computational burden and imprecise allocation method of the vector action time, affecting the control performance. To address these two issues, this paper proposes an improved M2PC method that utilizes the information of projection of the reference vector on the selected voltage vectors for efficiently selecting the optimal active vectors and also accurately calculating their action time. Compared to the conventional M2PC, the proposed technique demonstrates both excellent dynamic performance with reduced execution time by 15.3% and superior steady‐state performance in terms of lower torque/speed ripple as well as fewer current harmonics. Those benefits have been proven by the results of a 2.5k rpm permanent magnet synchronous motor drive. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Predictive Control for a Single-Phase to Three-Phase Converter with Two-Parallel Single-Phase Rectifiers


 This paper presents a parallel single-phase to three-phase drive system, using two single-phase rectifiers. The rectifiers are not isolated from the grid by low-frequency transformers, so the reduction of the circulation current is an important objective for the control strategy. Modulated Model Predictive Control regulates the grid current and minimizes the circulation current between the parallel rectifiers. Furthermore, the analyzed predictive control consists of two strategies, where the first has the application of two vectors in a sampling period while the second has three vectors. The addition of a vector allows the reduction of the computational cost due to the reduction of tests performed in the predictive control and reduces the harmonic distortion in the electrical grid due to the greater application of vectors in a sampling period. Simulation and experimental results are presented in order to validate the control strategy.
 

Enhancing the Performance and Efficiency of Two-Level Voltage Source Inverters: A Modified Model Predictive Control Approach for Common-Mode Voltage Suppression

In this paper, a new modified model predictive control is proposed to improve the performance of the model predictive control for two-level voltage source inverters by alleviating computational burden and the disadvantages associated with the conventional model predictive control strategy. The objective of the proposed method is to reduce the number of candidate voltage vectors in each sector, thereby improving the overall performance of the control system, as well as achieving common-mode voltage reduction for two-level voltage source inverters. Two strategies are introduced to achieve this objective. Firstly, an algorithm is developed based on statistical computational processes to pre-define the candidate voltage vectors. This strategy involves ranking and considering the most frequently used voltage vectors. Consequently, by reducing the computational burden, the search space for the optimal voltage vectors is reduced. Furthermore, based on statistical results, a strategy is proposed to divide the sectors into three sectors instead of the six sectors in the conventional method. This approach effectively reduces the number of candidate voltage vectors. The modified model predictive control strategy aims to improve the efficiency of the control system by reducing the computational burden, and suppressing the common-mode voltage. The simulation and experiments are carried out to verify the effectiveness of the proposed strategy under various operational conditions. The results demonstrate that the modified model predictive control approach significantly reduces the computational burden and complexity of the control system while effectively suppressing the common-mode voltage; this contributes to improving the performance of two-level voltage source inverters and enhancing their applicability for connecting the renewable energies to the grid.

Dual-Layer Modulated Model Predictive Control Scheme for the Cascaded H-Bridge Converter

To simultaneously achieve fast dynamics and fixed switching frequency, a dual-layer modulated model predictive control scheme is proposed for the cascaded H-bridge (CHB) converter in this article. For the output current control layer, firstly, each phase cluster is considered as a whole, and only six nonzero vectors are evaluated for output current prediction. Then, two optimal vectors are selected, and their dwell times are calculated based on the current prediction results. As a result, fast output current dynamics can be obtained with a greatly reduced number of evaluated control options. For the cluster voltage balancing control layer, instead of fundamental-frequency zero-sequence voltage (ZSV), the adaptive ZSV is injected according to the predicted cluster voltage differences. As a result, the active power flows are adjusted in each control period, and the cluster voltage balancing speed can be significantly improved. By the proposed scheme, complicated weight factor design is avoided, and fixed switching frequency is obtained through the integration of pulsewidth modulation. Experimental results verify that the proposed scheme can achieve fast output current and cluster voltage balancing dynamics with fixed switching frequency.

Geometrical Modulated Model Predictive Control With Current Limiting for Power Converters

Geometrical Modulated Model Predictive Control With Current Limiting for Power Converters

Modulated model predictive current control technique for single phase nine‐level T‐type packed U‐cell inverter topology

SummaryThis paper proposes a new floor function single carrier‐based modulated model predictive current control technique (FF‐SC‐M2PC2T) by considering conventional nine‐level T‐type packed U‐cell (9L‐TPUC) inverter topology. To get desired peak value of load current with low harmonic distortion, the finite control set‐based model predictive control technique (FCS‐MPCT) has been reported in the literature considering load dynamics and minimization of the cost function. However, inherently it suffers from variable switching frequency (VSF) of operation. To diminish this issue, an M2PC2T has been implemented and it provides a constant switching frequency (CSF) of operation. Especially coming to the modulation stage, most authors generally use level‐shifted pulse width modulation (PWM) techniques which inherently require more carriers, if‐else conditions, and additional logical gate blocks to generate particular level output. Hence, the control complexity increases as going to higher‐level inverter topologies. In this manuscript, a new simple floor function single carrier‐based pulse width modulation (FF‐SC‐PWM) technique has been proposed which applies to any single‐phase switched capacitor type multilevel inverter topology and it can be easily integrated with a predictive control algorithm to regulate the inverter output current (IOC). Finally, to check the effectiveness of the proposed control technique (PCT) in a closed loop, a laboratory prototype has been employed on 9L‐TPUC inverter topology with different static and dynamic case studies considering RL‐Load.

Improved Modulated Model Predictive Control of TLIMC-PMSM Based on Virtual Vector Prediction

Improved Modulated Model Predictive Control of TLIMC-PMSM Based on Virtual Vector Prediction

A new floor function single‐carrier‐based modulated model predictive current control technique for single‐phase PUC5 inverter topology

SummaryIn this paper, a new floor function (F2) single‐carrier (SC)‐based modulated model predictive current control technique (M2PC2T) has been proposed by considering the conventional single‐phase five‐level packed U‐cell (PUC5) inverter topology. Generally, to regulate the output current, the finite control set‐based MPC technique without a modulator inherently operates with variable switching frequency, and it is unsuitable for industrial applications. To alleviate this, authors have implemented M2PC2T, which provides constant switching frequency of operation. In this technique, generally, the predictive algorithm has been integrated with any modulation stage. However, while the implementation of pulse width modulation (PWM) techniques, most authors have used conventional PWM techniques which further increases the control complexity as the level number increases. In this manuscript, a simple and generalized F2‐SC‐PWM technique has been proposed and integrated with the predictive algorithm. This PWM technique implementation process is very simple as compared with all conventional PWM techniques since even if the level number increases, the number of modulating and carrier signals is always only one. Finally, the complete closed‐loop control technique is called a generalized F2‐SC‐M2PC2T which is suitable for all stiff direct current (dc) sources and switched capacitor‐type multilevel inverter (MLI) topologies. The proposed control technique (PCT) has been experimentally verified with different transient and steady‐state case studies by considering resistive‐inductive load.

A new single modulating and single carrier‐based predictive current control technique for single‐phase quadruple boost multilevel inverter topology

SummaryA new floor function single carrier‐based modulated model predictive current control (F2‐SCM2PC2) technique has been proposed in this paper by considering traditional single‐phase quadruple boost switched capacitor‐based multilevel inverter (QB‐SCMLI) topology. Conventionally, the implementation process of single modulating and single carrier‐based pulse width modulation (PWM) techniques are very much complex and finite control set‐based model predictive control (FCS‐MPC) technique inherently operates with variable switching frequency (VSF) operation, which is not suitable for industrial applications. To alleviate the aforementioned issues, a new generalized closed‐loop F2‐SCM2PC2 technique has been proposed. It consists of a simple PWM implementation process and can easily be integrated with a predictive algorithm, thereby providing a constant switching frequency (CSF) of operation. The particular single modulating and single carrier‐based PWM technique with floor function is also suitable for low and high switching frequency (LSF‐HSF) operations. The validation of floor function‐based PWM technique in open‐loop and closed‐loop F2‐SCM2PC2 techniques has been effectively verified through experimentation on QB‐SCMLI topology with consideration of resistive–inductive load.

A Detailed dSPACE-Based Implementation of Modulated Model Predictive Control for AC Microgrids.

Microgrids represent a promising energy technology, because of the inclusion in them of clean and smart energy technologies. They also represent research challenges, including controllability, stability, and implementation. This article presents a dSPACE-control-platform-based implementation of a fixed-switching-frequency modulated model predictive control (M2PC) strategy, as an inner controller of a two-level, three-phase voltage source inverter (VSI) working in an islanded AC microgrid. The developed controller is hierarchical, as it includes a primary controller to share the load equally with the other power converter with its own local modulated predictive-based controller. All details of the implementation are given for establishing the dSPACE-based implementation of the control on a dSPACE ds1103 control platform, using MATLAB/Simulink for the controller design, I/O implementation and configuration with the embedded dSPACE's real-time interface in Simulink, and then using the ControlDesk software for monitoring and testing of the real plant. The latter consists of the VSI operating with LCL filters, and sharing an RL load with a paralleled VSI with exactly the same controller. Finally, the obtained experimental waveforms are shown, with our respective conclusions representing this work, which is a very valuable tool for helping microgrid researchers implement dSPACE-based real-time simulations.

Synchronous frame phase compensation for applying modulated model predictive control to high-speed motors

Synchronous frame phase compensation for applying modulated model predictive control to high-speed motors

Energy-Optimal Adaptive Control Based on Model Predictive Control.

Energy-optimal adaptive cruise control (EACC) is becoming increasingly popular due to its ability to save energy. Considering the negative impacts of system noise on the EACC, an improved modified model predictive control (MPC) is proposed, which combines the Sage-Husaadaptive Kalman filter (SHAKF), the cubature Kalman filter (CKF), and the back-propagation neural network (BPNN). The proposed MPC improves safety and tracking performance while further reducing energy consumption. The final simulation results show that the proposed algorithm has a stronger energy-saving capability compared to previous studies and always maintains an appropriate relative distance and relative speed to the vehicle in front, verifying the effectiveness of the proposed algorithm.