scholarly journals Model Predictive Control of Uninterruptible Power Supply with Robust Disturbance Observer

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2871 ◽  
Author(s):  
Yahya Danayiyen ◽  
Kyungsuk Lee ◽  
Minho Choi ◽  
Young Il Lee
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Disturbance Observer ◽  
Controller Design ◽  
Linear Matrix ◽  
Control Input ◽  
Continuous Control ◽  
Model Uncertainties ◽  
Three Phase ◽  
Uninterruptible Power

This paper presents a robust continuous control set model predictive control (CCS-MPC) method to control the output voltage of a three-phase inverter in uninterruptible power supplies (UPS). A robust disturbance observer (DOB) is proposed to estimate the load current of the three-phase UPS without a steady-state error, taking the effect of model uncertainties into account. A CCS-MPC is designed using the DOB for reference voltage tracking purpose, and input constraints are considered in the controller design to calculate the optimal control input. Model uncertainties are defined using polytopic uncertainty class, and a linear matrix inequality (LMI) optimization method is used to compute the optimal observer gain matrix. Another robust controller (RC) is designed based on the DOB and compared with CCS-MPC. The effectiveness of the proposed method (the DOB based CCS-MPC) is evaluated for resistive, inductive, and nonlinear loads then compared with other control methods using a three-phase 5-KVA laboratory experiment UPS system.

Mathematical modelling and model predictive controller design of a quad tiltrotor UAV

2020 ◽  
pp. 095440622097133
Author(s):  
Jingxian Liao ◽  
Xiaodong Song
Keyword(s):  
Mathematical Model ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Controller Design ◽  
Control Input ◽  
Random Disturbance ◽  
Effect Analysis ◽  
Input Variables ◽  
Induced Velocity

A novel convertible unmanned aerial vehicle (UAV) with four tiltable rotors and a tandem-wing system has been developed. Considering the aerodynamic effect caused by the rotor-induced velocity, a mathematical model that contains the traditional free airstream analysis and rotor-induced effect analysis is proposed, from which the precise equilibrium point of the control inputs and states can be derived. Moreover, a control allocation algorithm is designed to provide the mapping relationship between traditional input variables and specific input variables of the UAV, so that the complicated mathematical model can be linearized for the design of model predictive control (MPC) system. In order to handle the control input constraints of the UAV system, an MPC system is applied for the trajectory tracking during the cruising phase. The simulation results demonstrate that the proposed model predictive control system has stability, accuracy without a random disturbance and quick response capabilities with a random disturbance during cruising trajectory tracking, which are in high demand for the quick UAV flight system.

Robust model predictive control synthesis for state-delayed systems with randomly occurring input saturation nonlinearities

2016 ◽  
Vol 40 (1) ◽  
pp. 179-190 ◽  
Author(s):  
Langwen Zhang ◽  
Wei Xie ◽  
Zhaozhun Zhong ◽  
Jingcheng Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Feedback ◽  
Controller Design ◽  
Input Saturation ◽  
Time Instant ◽  
Control Synthesis ◽  
Linear Matrix ◽  
Model Parameters ◽  
Delayed Systems

In this paper, a model predictive control algorithm is presented for linear parameter varying systems with both state delays and randomly occurring input saturation. The input saturation is assumed to be occurred randomly with Bernoulli-distributed white sequences. A constant sate feedback law is designed at each time instant to ensure the robust stability of the closed-loop system with respect to polytopic uncertainties. The optimization of model predictive controller is cast into solving a linear matrix inequalities optimization problem. Then, the results are extended to gain-scheduled approach in which a set of state feedback laws are designed for each vertex of the system model. The state feedback law is scheduled by the time varying model parameters to achieve less conservatism in controller design. Finally, two examples are employed to show the effectiveness of the proposed algorithms.

scholarly journals A Comparison of Finite Control Set and Continuous Control Set Model Predictive Control Schemes for Model Parameter Mismatch in Three-Phase APF

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianfeng Yang ◽  
Yang Liu ◽  
Rui Yan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Parameter Uncertainty ◽  
Prediction Error ◽  
Continuous Control ◽  
Power Electronic ◽  
Parameter Mismatch ◽  
Three Phase ◽  
Finite Control ◽  
Control Set

Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny.

A Control System for the Ball Mill Grinding Process Based on Model Predictive Control and Equivalent-Input-Disturbance Approach

2016 ◽  
Vol 20 (7) ◽  
pp. 1152-1158 ◽  
Author(s):  
Mingxing Fang ◽  
◽  
Dezhi Zheng ◽  
Xiaoxiao Qiu ◽  
Youwu Du
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Ball Mill ◽  
Controller Design ◽  
Grinding Process ◽  
Control Input ◽  
Performance Simulation ◽  
Input Disturbance ◽  
Equivalent Input Disturbance ◽  
Stable Control

Stable control of the ball mill grinding process is very important to reduce energy losses, enhance operation efficiency, and recover valuable minerals. In this work, a controller for the ball mill grinding process is designed using a combination of model predictive control (MPC) with the equivalent-input-disturbance (EID) approach. MPC has been researched and applied widely as one of the multi-variable control algorithms for grinding. It is used to decouple in real time. The controller design does not deal with the disturbances directly. However, strong disturbances such as those caused by ore hardness and feed particle size exist in the ball mill grinding. EID estimates the equivalent disturbance of the grinding circuit in the control input channel and integrates this disturbance directly into the control law in order to suppress disturbances promptly and effectively. This results in good disturbance suppression performance. Simulation results demonstrate that the combination of MPC with EID for controlling the ball mill grinding circuit yields better performance in terms of disturbance rejection, rapid response, and strong robustness as compared to the performance of the MPC and proportional-integral (PI) decoupling control.

scholarly journals Model Predictive Control of Piecewise Affine System with Constrained Input and Time Delay

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Zhilin Liu ◽  
Lutao Liu ◽  
Jun Zhang ◽  
Xin Yuan
Keyword(s):  
Time Delay ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Infinite Horizon ◽  
Linear Matrix ◽  
Control Input ◽  
Piecewise Affine ◽  
Operating Region ◽  
Current Operating ◽  
Constrained Input

A model predictive control (MPC) is proposed for the piecewise affine (PWA) systems with constrained input and time delay. The corresponding operating region of the considered systems in state space is described as ellipsoid which can be characterized by a set of vector inequalities. And the constrained control input of the considered systems is solved in terms of linear matrix inequalities (LMIs). An MPC controller is designed that will move the PWA system with time delay from the current operating point to the desired one. Multiple objective functions are used to relax the monotonically decreasing condition of the Lyapunov function when the control algorithm switches from a quasi-infinite horizon to an infinite horizon strategy. The simulation results verify the effectiveness of the proposed method. It is shown that, based on LMI constraints, it is easy to get the MPC for the PWA systems with time delay. Moreover, it is suitable for practical application.

scholarly journals Model Predictive Control for Paralleled Uninterruptible Power Supplies with an Additional Inverter Leg for Load-Side Neutral Connection

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2270
Author(s):  
Tiago Oliveira ◽  
Luís Caseiro ◽  
André Mendes ◽  
Sérgio Cruz ◽  
Marina Perdigão
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Power Distribution ◽  
Power Supplies ◽  
Load Power ◽  
Circulating Current ◽  
Three Phase ◽  
Wide Range ◽  
Uninterruptible Power Supplies ◽  
Uninterruptible Power

Uninterruptible Power Supplies (UPS) have been demonstrated to be the key technology in feeding either single- and three-phase loads in a wide range of critical applications, such as high-tier datacenters and medical facilities. To increase the overall system power capacity and resilience, UPS systems are usually connected in parallel. When UPS systems are parallel connected, a circulating current can rise, inhibiting correct system operation. Moreover, having a controlled load power distribution is another fundamental requirement in paralleled UPS systems. However, strategies to ensure these two topics have not been explored to date for UPS systems with a load-side neutral connection. This paper proposes an innovative Finite Control Set Model Predictive Control (FCS-MPC) strategy that ensures circulating current elimination and controlled load power distribution for paralleled UPS systems that use an additional inverter leg for load neutral point connection. Additionally, a system topology based on two parallel-connected UPS systems that can simultaneously supply single- and three-phase critical loads is proposed. Experimental results show the effectiveness and robustness of the proposed control techniques even when different types of loads are connected to the UPS systems.

Dual-Loop Continuous Control Set Model Predictive Control for a Three-phase Unity Power Factor Rectifier

2021 ◽  
pp. 1-1
Author(s):  
Carlos Arturo Alfaroaragon ◽  
Ramon Guzman ◽  
Jose Luis Garcia de Vicuna ◽  
Miguel Castilla ◽  
Jaume Miret
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Power Factor ◽  
Continuous Control ◽  
Unity Power Factor ◽  
Three Phase ◽  
Control Set
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