Model-free Predictive Control Research Articles

Model-Free Predictive Control based on the Execution Time of Active Vectors Using Ultra-Local Model

Model-Free Predictive Control based on the Execution Time of Active Vectors Using Ultra-Local Model

Continuous Control Set Predictive Control with Affine Registration Technique for Permanent Magnet Synchronous Motor Drive

This article introduces an affine registration (AR) algorithm to improve the performance of ultra-local (UL) model-free predictive control (MFPC) methods. The UL model is extensively utilized in continuous control set (CCS)-MFPC to estimate lumped parameters. However, in these cases, variations in inductance during operation can significantly impact the input gain coefficient of the UL model. Little work has focused on this aspect, which makes the motivation and novelty of this paper. The AR technique, derived from image processing methodologies, is employed to establish a mapping between predicted states and actual states during the commissioning process. Subsequently, during actual operation, the resolved voltage reference is modified based on the AR matrix to compensate for errors in the UL model. The theoretical control model is rigorously derived, and experimental results are presented to validate the superior performance of the proposed method.

ENHANCED MODEL-FREE PREDICTIVE CONTROL FOR VOLTAGE SOURCE INVERTERS USING AN ADAPTIVE OBSERVER

In this paper, a free model predictive control based on the active vector execution time (AVET-MFPC) using an adaptive observer is proposed for two-level voltage source inverters. The traditional model-free predictive control (MFPC) uses the sampling period to select one voltage vector for all candidate vectors according to the minimizing cost function principle. With the proposed control, two vectors are selected at one sampling period. The first vector is an active vector that uses the execution time of the active vector to select it, while the second one is a zero vector as it is applied after the active vector. The execution time is calculated using the ultra-local model (ULM) equation. In the traditional MFPC, the factor in the ULM is chosen with approximate values ranging between ±50 % of the nominal value. This paper proposes an adaptive sliding mode observer (ASMO) with an improved design to observe the variation of this factor, especially in case of mismatch parameters and during a step change in the reference signal. Combining the proposed ASMO observer and the AVET-MFPC controller gives faster system response, good tracking results, and less computational burden. Finally, the effectiveness of the proposed control model is approved and confirmed under various conditions, as well as the simulations carried out and the results obtained.

Enhanced Model-Free Predictive Speed Control Without Weighting Factors for PMSM Based on Newly Designed Cost Function

Enhanced Model-Free Predictive Speed Control Without Weighting Factors for PMSM Based on Newly Designed Cost Function

A Novel Modulated Model-Free Predictive Control for LC-Filtered Grid-Forming Inverters with Double-Difference Updating

A Novel Modulated Model-Free Predictive Control for LC-Filtered Grid-Forming Inverters with Double-Difference Updating

A Switched Ultra-Local Model-Free Predictive Controller for PMSMs

A Switched Ultra-Local Model-Free Predictive Controller for PMSMs

Robust Sequential Model-Free Predictive Control of a Three-Level T-Type Shunt Active Power Filter

Robust Sequential Model-Free Predictive Control of a Three-Level T-Type Shunt Active Power Filter

Improved Model-Free Predictive Control of a Three-Phase Inverter

Model predictive control (MPC) performance depends on the accuracy of the system model. Moreover, the optimization algorithm of MPC requires numerous online computations. These inherent limitations of MPC hinder its application in power electronics systems. This paper proposes a two-part solution for these challenges for a three-phase inverter with an output LC filter. The first part of the control scheme is a linear and modified model-free approach based on the auto-regressive structure (ARX) with exogenous input. The second part is the computationally efficient optimization algorithm based on the active set method to solve the optimization problem of the MFPC. The objective of the control scheme is to regulate the output voltages of the inverter in the presence of constraints. The constraints are the maximum admissible filter current and optimal duty cycle to avoid any damage to the system. To validate the performance of the proposed control scheme, simulations and hardware-in-loop (HIL) real-time investigations have been performed, comparing the results of the proposed approach with the model-based predictive control. The results showcase the computational efficiency and effectiveness of the MFPC approach, demonstrating its potential for overcoming the limitations of traditional MPC in power electronics systems.

A solution on parameter fluctuation of integrated on‐board battery chargers based on recursive least square algorithm

SummaryIntegrated on‐board battery chargers (IOBCs) are receiving increasing academic attention due to their low cost and low weight advantages. However, IOBCs suffer from periodic saturation, which causes periodic changes in inductance, which may lead to deterioration in control performance. Model‐free predictive control (MFPC) is a strongly robust control method that is suitable for solving the periodic saturation problem of IOBCs with pulsating magnetic fields. In this paper, a common single‐phase IOBC topology is used as an example, the causes of inductance fluctuations are analyzed, and finite element analysis (FEM) of motor inductance with different motor qualities is carried out. A recursive least squares (RLS)‐based solution method is proposed, and its convergence is analyzed. The feasibility of the proposed control scheme is experimentally verified.

Model-free Predictive Trajectory Tracking Control and Obstacle Avoidance for Unmanned Surface Vehicle With Uncertainty and Unknown Disturbances via Model-free Extended State Observer

Model-free Predictive Trajectory Tracking Control and Obstacle Avoidance for Unmanned Surface Vehicle With Uncertainty and Unknown Disturbances via Model-free Extended State Observer

Model-Free Predictive Current Controller for Voltage Source Inverters Using ARX Model and Recursive Least Squares

Model-Free Predictive Current Controller for Voltage Source Inverters Using ARX Model and Recursive Least Squares

Robust Predictive Control of Grid-Connected Converters: Sensor Noise Suppression With Parallel-Cascade Extended State Observer

Model predictive control (MPC) is a constrained optimization control method with superior performance than linear methods for multi-variable and multi-objective control of power converters. Nonetheless, its performance is limited by model uncertainties and measurement noise. This study tackles this challenge by proposing a new hybrid parallel-cascade extended state observer (PC-ESO) with two key advantages, viz.: (i) higher disturbance rejection than the conventional linear ESO and cascade ESO (CESO) at low bandwidth, and (ii) better noise suppression than the conventional ESO. PC-ESO's time-domain structure, and comprehensive frequency-domain analysis are presented. Furthermore, PC-ESO is applied to improve the transient disturbance rejection of CESO through a novel structurally-adaptive ESO (SAESO) algorithm. The proposed SAESO provides both high-frequency noise-suppression and better disturbance rejection than CESO and cascade-parallel ESO. Finally, the proposed methods are experimentally validated by the model-free predictive control of a grid-connected power converter.

Model-Free Predictive Control for Grid-Connected Converters With Flexibility in Power Regulation: A Solution for Unbalanced Conditions

Model-Free Predictive Control for Grid-Connected Converters With Flexibility in Power Regulation: A Solution for Unbalanced Conditions

Bidirectional DC-DC converters for distributed energy resources: Robust predictive control with structurally-adaptive extended state observers

Distributed energy resources (DER) are integrated into a microgrid through dc-dc power electronic converters. The bidirectional dc-dc converter regulates charging and discharging operations of ESS. Model predictive control (MPC), is a high-performance control technique for these converters, but it is limited in robustness to parameter mismatch, model uncertainties and sensor measurement noise. Therefore, in this paper, an improved hybrid cascade-parallel extended state observer (CP-ESO) is proposed, for model-free predictive control, to guarantee both robustness to parameter/model uncertainties and measurement noise suppression. A novel structurally-adaptive ESO scheme is also proposed to improve the disturbance rejection of CP-ESO during transient response. These results are supported by analysis and design guidelines for the selection of optimal sub-frequencies. Experimental results, for a bidirectional dc-dc boost converter, validate the effectiveness of the proposed methods against model uncertainties, external disturbances from variable input voltage and load, as well as measurement noise.

Predictive Control for Unknown Dynamics With Observation Loss: A Temporal Game-Theoretic Approach

This paper is concerned with a model-free predictive control problem on systems with unknown dynamics. Different from existing predictive control, the predictive feedback strategy is designed to take control inputs to cover the infinite horizon when the system exists the observation loss. To predict future control inputs, a temporal game-theoretic approach is presented to model such a predictive control issue as an optimization problem and ensure the optimality of the system performance. Moreover, a predictive algebraic Riccati equation (PARE) is constructed to solve such an optimization problem. By leveraging offline datasets and the real-time data of state and input, a data-driven parallel computational framework is developed to iteratively solve the PARE. In this way, the prior knowledge of the systems is avoided and the computational complexity of the proposed algorithm is reduced. Finally, numerical and practical examples are presented to show the applicability of the proposed results.

Event-Triggered Distributed Secondary Control With Model-Free Predictive Compensation in AC/DC Networked Microgrids Under DoS Attacks.

This article presents an event-triggered distributed secondary control with predictive compensation based on the model-free predictive control under Denial-of-Service (DoS) attacks in ac/dc-networked microgrids. First, models of ac/dc networked microgrids in both electric network and communication network are established. On this premise, event-triggered distributed secondary control is proposed to solve the problems of strong communication burden and low-power distribution accuracy. Besides, aiming at the impact of DoS attacks on distributed secondary control, a compensation algorithm based on model-free predictive control is designed to estimate the control variables when the DoS attack occurs, which can improve the control performance and maintain the stable operation without a specific system structure system. Then, the convergence of event-triggered distributed secondary control with the condition of whether DoS attacks happen are analyzed. Finally, the effectiveness of the proposed control is verified on the hardware-in-loop (HIL) simulation platform consisting of the RT-LAB simulator, MATLAB/Simulink simulation model, and DSP controller.

Multiple-Voltage-Vector Model-Free Predictive Deadbeat Control with Updated Reference Voltage Vector for PMSM Drive

Multiple-Voltage-Vector Model-Free Predictive Deadbeat Control with Updated Reference Voltage Vector for PMSM Drive

Multi-vector-based Model-Free Predictive Speed Control with Disturbance Suppression and Weighting Factor Elimination for SPMSM

Multi-vector-based Model-Free Predictive Speed Control with Disturbance Suppression and Weighting Factor Elimination for SPMSM

Model-Free Predictive Current Controller for Common Mode Voltage Stabilization by Finite odd Virtual Vector set

Model-Free Predictive Current Controller for Common Mode Voltage Stabilization by Finite odd Virtual Vector set

Model-Free Predictive Control for Improved Performance and Robustness of Three-Phase Quasi Z-Source Inverters

Model-Free Predictive Control for Improved Performance and Robustness of Three-Phase Quasi Z-Source Inverters