Predictive Control Research Articles

Energy-based Analysis and Predictive Variable Frequency Control Scheme of a High-Voltage Solid State Pulse Generator for Space Applications

Energy-based Analysis and Predictive Variable Frequency Control Scheme of a High-Voltage Solid State Pulse Generator for Space Applications

DynamicPrint: A physics-guided feedforward model predictive process control approach for defect mitigation in laser powder bed fusion additive manufacturing

DynamicPrint: A physics-guided feedforward model predictive process control approach for defect mitigation in laser powder bed fusion additive manufacturing

Experimental Evaluation of a Proposed LQR-LU Optimal Grid Controller in the Applications of Grid-Tied PV Systems

This article aims to design a linear quadratic regulator (LQR)-based optimal current controller along with a Luenberger observer model in order to enhance power quality during the grid's dynamic conditions. To analyze the performance of the proposed control scheme, a three-phase photovoltaic-based gird-tied inverter is considered. A grid controller comprises a DC-link voltage controller to stabilize the capacitor voltage and a current controller to assure the output currents are well controlled according to the dynamic conditions of the grid as well as the source. In general, the capacitor-voltage and current controllers are regulated by traditional proportional Integral (PI) controllers which is associated with many demerits. In this work, a predictive LQR-based current controller with an observer algorithm and an adaptive capacitor voltage controller is proposed to improve the performance of the power grid at various dynamic conditions. The mathematical analogy is evaluated using MATLAB/SIMULINK software with different load and grid-disturbance conditions. Moreover, a laboratory prototype is designed to validate the simulation results. The outcomes prove that the proposed controller is dynamically robust to tackle any disturbance in load and grid.

Improved Predictive Current Controller for Variable Flux Memory Machine Drives Considering Magnetization State Manipulating Operations

Improved Predictive Current Controller for Variable Flux Memory Machine Drives Considering Magnetization State Manipulating Operations

Model-Free Predictive Current Control of PMSM Using Modified Extended State Observer

Model-Free Predictive Current Control of PMSM Using Modified Extended State Observer

Optimal Voltage Level-Based Sequential Predictive Current Control With Reduced Complexity for Multilevel Inverters

Optimal Voltage Level-Based Sequential Predictive Current Control With Reduced Complexity for Multilevel Inverters

Nonparametric Predictive Current Control for SPMSM With Adaptive Cascade Extended Noise State Observer

Nonparametric Predictive Current Control for SPMSM With Adaptive Cascade Extended Noise State Observer

Model Predictive Torque Control of Switched Reluctance Machines With Torque Sharing Function and PWM Control Signals Based on Linear Polynomial Fitting

Model Predictive Torque Control of Switched Reluctance Machines With Torque Sharing Function and PWM Control Signals Based on Linear Polynomial Fitting

Prediction-based rapid force control of a single-acting pneumatic cylinder under hysteresis nonlinearity.

Hysteresis characteristics widely affects the performance and reliability of pneumatic systems across various industrial applications. Addressing this challenge can significantly enhance system efficiency and precision. This paper aims to develop a rapid and accurate method for controlling the actuating force of a Single-Acting Pneumatic Cylinder (SAPC), considering hysteresis characteristic. To achieve these objectives, a Neural-Network-Prediction-based Proportional-Integral-Differential (NNP-PID) control strategy is introduced for the rapid prediction and precise control of the actuating force. Control experiments were conducted to elucidate the rapid control mechanism of the proposed NNP-PID strategy and assess its performance. Experimental results indicate that the developed neural network prediction model operates with a computational cost of 1.22 ms on an 8-bit microcontroller, thus meeting real-time control requirements. Compared to a conventional Proportional-Integral-Differential (PID) controller, the NNP-PID controller reduced control overshoot, rise time, settling time, and steady-state error by approximately 17.5 %, 65.9 %, 19.8 %, and 46.4 %, respectively.

Fast frequency and phase synchronization of high-stability oscillators with 1 PPS signal from satellite navigation systems

Abstract In this paper, we propose a novel algorithm for fast frequency and phase synchronization of high-stability oscillators synchronized with 1 PPS signal from satellite navigation systems. The algorithm uses a model of a control object in the space of state variables and controls the frequency of an oscillator operating in a phase-locked loop. A new element is the introduction to the theoretical analysis and the design process, the time of entering synchronization. Currently, the literature lacks theoretical analysis and design methodology that considers the impact of the synchronization time on the choice of the steering algorithm and its parameters. All the data needed to determine the numerical values of the model were found experimentally for three different classes of control objects. Short synchronization times, a detailed description of the design methodology, and the use of values measured in the real system distinguish the proposed algorithm from the solutions described in the literature. The effect of optimization was achieved thanks to the algorithm’s two-stage operation. In the first stage, the algorithm aims to minimize the phase error quickly. The best solution for this stage is Sliding Mode Control (SMC). In the second stage, the algorithm strives to maximize the control quality, understood as minimizing the values of Maximum Time Interval Error (MTIE) and Time Deviation (TDEV). The Model Predictive Control (MPC) and Linear-Quadratic Regulator (LQR) optimal control algorithms were used at this stage. The paper also investigated the influence of the tuning parameters of these algorithms (weights as a function of cost) on the long-term behavior of the control system.

Optimization modeling of automatic crowd regulation at bottlenecks of subway system: A model predictive control approach

Optimization modeling of automatic crowd regulation at bottlenecks of subway system: A model predictive control approach

K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles

K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles

Practical strategies for managing resistance heating in heat pump water heater predictive control

Practical strategies for managing resistance heating in heat pump water heater predictive control

An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertainties

A novel hybrid adaptive control method is presented for trajectory tracking of remotely operated underwater vehicles (ROVs) that addresses unknown disturbances and model uncertainties in this paper. Traditional nonlinear control methods struggle to handle external disturbances and uncertainty in system model. To address the trajectory tracking control needs of ROVs in complex underwater environments, a kinematic and dynamic model is first developed for a fully actuated ROV with six degrees of freedom (6-DOF). The trajectory tracking problem is formulated as an online, nonlinear receding horizon optimization process. Control increments are computed as inputs to this nonlinear optimization problem. An L1 adaptive control method (L1AC) is then developed, incorporating a state observer, adaptive control law, and time filter. The framework retains the rolling optimization process of nonlinear model predictive control (NMPC) while integrating the L1 adaptive component for instant compensation of unknown disturbances and model parameter mismatches. Numerical simulations were conducted to compare the trajectory tracking performance of the proposed hybrid adaptive method with the NMPC method under various disturbances, including ocean currents, waves, random forces, and model uncertainties. The results confirm that the proposed hybrid adaptive control scheme is more effective and robust than the standalone NMPC approach across various scenarios.

Digital grid-connected control technology for more electric aircraft power generation system based on model predictive control

Digital grid-connected control technology for more electric aircraft power generation system based on model predictive control

Parallelized robust distributed model predictive control in the presence of coupled state constraints

In this paper, we present a robust distributed model predictive control (DMPC) scheme for dynamically decoupled nonlinear systems which are subject to state constraints, coupled state constraints and input constraints. In the proposed control scheme, all subsystems solve their local optimization problem in parallel and neighbor-to-neighbor communication suffices. The approach relies on consistency constraints which define a neighborhood around each subsystem’s reference trajectory where the state of the subsystem is guaranteed to stay in. Contrary to related approaches, the reference trajectories are improved consecutively. In order to ensure the controller’s robustness against bounded uncertainties, we employ tubes. The presented approach can be considered as a time-efficient alternative to the well-established sequential DMPC. In the end, we briefly comment on an iterative extension. The effectiveness of the proposed DMPC scheme is demonstrated with simulations, and its performance is compared to other DMPC schemes.

Efficient data-driven predictive control of nonlinear systems: A review and perspectives

Efficient data-driven predictive control of nonlinear systems: A review and perspectives

Decentralized Model Predictive Control for Offshore Wind-Powered Seaport DC Microgrids with Electric Vehicle Stations

Decentralized Model Predictive Control for Offshore Wind-Powered Seaport DC Microgrids with Electric Vehicle Stations

Learning-Based Modeling and Predictive Control for Unknown Nonlinear System With Stability Guarantees

Learning-Based Modeling and Predictive Control for Unknown Nonlinear System With Stability Guarantees

A novel container-based approach for integrating solar forecast in real-time simulation and model predictive control

A novel container-based approach for integrating solar forecast in real-time simulation and model predictive control