scholarly journals A Comparison of Model-Free and Model Predictive Control for Price Responsive Water Heaters

2020 ◽  
Author(s):  
David J. Biagioni ◽  
Xiangyu Zhang ◽  
Peter Graf ◽  
Devon Sigler ◽  
Wesley Jones
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Water Heaters ◽  
Model Free

Payload Drop Application Using an Unmanned Quadrotor Helicopter Based on Gain-Scheduled PID and Model Predictive Control

2014 ◽  
Vol 02 (01) ◽  
pp. 39-52 ◽  
Author(s):  
Iman Sadeghzadeh ◽  
Mahyar Abdolhosseini ◽  
Youmin Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Performance Comparison ◽  
Control Techniques ◽  
Unmanned Quadrotor Helicopter ◽  
Quadrotor Helicopter ◽  
Model Free ◽  
And Performance ◽  
Gain Scheduled

Two useful control techniques are investigated and applied experimentally to an unmanned quadrotor helicopter for a practical and important scenario of using an Unmanned Aerial Vehicle (UAV) for dropping a payload in circumstances where search and rescue and delivery of supplies and goods is dangerous and difficult to reach environments such as forest or high building fires fighting, rescue in earthquake, flood and nuclear disaster situations. The two considered control techniques for such applications are the Gain-Scheduled Proportional-Integral-Derivative (GS-PID) control and the Model Predictive Control (MPC). Both the model-free (GS-PID) and model-based (MPC) algorithms show a very promising performance with application to taking-off, height holding, payload dropping, and landing periods in a payload dropping mission. Finally, both algorithms are successfully implemented on an unmanned quadrotor helicopter testbed (known as Qball-X4) available at the Networked Autonomous Vehicles Lab (NAVL) of Concordia University for payload dropping tests to illustrate the effectiveness and performance comparison of the two control techniques.

scholarly journals Broad Learning Aided Model Predictive Control With Application to Continuous Stirred Tank Heater

2021 ◽  
Vol 2 ◽  
Author(s):  
Mo Tao ◽  
Tianyi Gao ◽  
Xianling Li ◽  
Kuan Li
Keyword(s):  
Model Predictive Control ◽  
Predictive Model ◽  
Predictive Control ◽  
Learning Algorithm ◽  
Stirred Tank ◽  
Process Data ◽  
Computational Load ◽  
Model Free ◽  
Predictive Controller ◽  
Continuous Stirred Tank

This paper presents a data-driven predictive controller based on the broad learning algorithm without any prior knowledge of the system model. The predictive controller is realized by regressing the predictive model using online process data and the incremental broad learning algorithm. The proposed model predictive control (MPC) approach requires less online computational load compared to other neural network based MPC approaches. More importantly, the precision of the predictive model is enhanced with reduced computational load by operating an appropriate approximation of the predictive model. The approximation is proved to have no influence on the convergence of the predictive control algorithm. Compared with the partial form dynamic linearization aided model free control (PFDL-MFC), the control performance of the proposed predictive controller is illustrated through the continuous stirred tank heater (CSTH) benchmark.

A model-free approach for auto-tuning of model predictive control

2014 ◽  
Vol 47 (3) ◽  
pp. 2189-2194 ◽  
Author(s):  
Quang N Tran ◽  
Joni Scholten ◽  
Leyla Ozkan ◽  
A.C.P.M. Backx
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Model Free ◽  
Model Free Approach ◽  
Auto Tuning
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