Intelligent optimization in model-predictive control with risk-sensitive filtering

2021 ◽  
pp. 1-11
Author(s):  
Yenming J. Chen ◽  
Jinn-Tsong Tsai ◽  
Wei-Tai Huang ◽  
Wen-Hsien Ho
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Real World ◽  
World System ◽  
Unexpected Events ◽  
System Parameters ◽  
Control Rules ◽  
Risk Sensitive ◽  
Computation Algorithm ◽  
Intelligent Model

The uncertainty issue in real-work optimization affects the level of optimization significantly. Because most future uncertainties cannot be foreseen in advance, the optimization must take the uncertainties as a risk in an intelligent way in the process of computation algorithm. Based on our risk-sensitive filtering algorithm, this study adopts a model-predictive control to construct a risk-averse, predictable model that can be used to regulate the level of a real-world system. Our model is intelligent in that the predictive model needs not to identify the system parameters in advance, and our algorithm will learn the parameters through data. When the real-world system is under the disturbance of unexpected events, our model can still maintain suitable performance. Our results show that the intelligent model designed in this study can learn the system parameters in a real-world system and minimize unexpected real-world disturbances. Through the learning process, our model is robust, and the optimal performance can still be retained even the system parameters deviate from expected, e.g., material shortage in a supply chain due to earthquake. When parameter error risks occur, the control rules can still drive the overall system with a minimal performance drop.

Model Predictive Control of a Forecasting Production System with Deteriorating Items

2015 ◽  
Vol 6 (4) ◽  
pp. 19-37 ◽  
Author(s):  
Abdelhak Mezghiche ◽  
Mustapha Moulaï ◽  
Lotfi Tadj
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Production System ◽  
Objective Function Value ◽  
Inventory Level ◽  
Optimal Production ◽  
Control Approach ◽  
System Parameters ◽  
Demand Rate ◽  
Optimal Inventory Level

The authors consider in this paper an integrated forecasting production system of the tracking type. The demand rate during a certain period depends on the demand rate of the previous period. Also, the demand rate depends on the inventory level. Items on the shelves are subject to deterioration. Using a model predictive control approach, the authors obtain the optimal production rate, the optimal inventory level, the optimal demand rate, and the optimal objective function value, explicitly in terms of the system parameters. A numerical example is presented.

Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit

2016 ◽  
Vol 207 ◽  
pp. 287-299 ◽  
Author(s):  
Yang Zheng ◽  
Jianzhong Zhou ◽  
Wenlong Zhu ◽  
Chu Zhang ◽  
Chaoshun Li ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Intelligent Model ◽  
Multi Mode

Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building

2013 ◽  
Vol 6 (3) ◽  
pp. 199-219 ◽  
Author(s):  
Peter T. May-Ostendorp ◽  
Gregor P. Henze ◽  
Balaji Rajagopalan ◽  
Charles D. Corbin
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Mixed Mode ◽  
Building Control ◽  
Control Rules

Improving the NOx reduction performance of an Euro VI d SCR System in real-world condition via nonlinear model predictive control

2021 ◽  
pp. 146808742110662
Author(s):  
Alberto Petrillo ◽  
Maria Vittoria Prati ◽  
Stefania Santini ◽  
Francesco Tufano
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Real World ◽  
Nox Reduction ◽  
Nonlinear Model Predictive Control ◽  
Feed Ratio ◽  
Real World Data ◽  
Ammonia Slip ◽  
Scr System

This paper deals with the possibility of improving the urea dosage control for the Selective Catalytic Reduction Systems (SCR) of an Euro VI d diesel light commercial vehicle in order to increase [Formula: see text] after-treatment reduction performance. To this aim, first, we assess the effective emissions abatement performance for the appraised diesel vehicle via real-world experimental campaign, carried out according to the Real Driving Emissions (RDE) tests on urban, extra-urban and motorway road sections in Naples, Italy. Based on these real-world data, we derive a parameterized control-oriented model for the SCR system which is, then, exploited for the designing of an alternative urea injection logic which could be able to maximize the [Formula: see text] reduction efficiency while minimizing tailpipe ammonia slip. Specifically, the optimal AdBlue injection rate is designed according to a Nonlinear Model Predictive Control Approach which allows obtaining a proper trade-off between the [Formula: see text] abatement and the urea overdosing problem. The effectiveness of the proposed controller is evaluated by comparing the performance assessed for the appraised SCR system during the experimental tests with the ones achievable if the Euro VI diesel would be equipped with the proposed control strategy. Numerical simulation discloses the effectiveness of the NMPC controller in ensuring improved [Formula: see text] reduction with performance complying with the emissions norms, main in avoiding excessive ammonia slip and in guaranteeing a reduced feed ratio w.r.t. to the standard industrial SCR controller mounted on the vehicle.

scholarly journals Field-Ready Implementation of Linear Economic Model Predictive Control for Microgrid Dispatch in Small and Medium Enterprises

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3921
Author(s):  
Tobias Kull ◽  
Bernd Zeilmann ◽  
Gerhard Fischerauer
Keyword(s):  
Renewable Energy ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Real World ◽  
Economic Model ◽  
Small And Medium Enterprises ◽  
Energy System ◽  
System Operation ◽  
Economic Model Predictive Control ◽  
Medium Enterprises

The increasing share of distributed renewable energy resources (DER) in the grid entails a paradigm shift in energy system operation demanding more flexibility on the prosumer side. In this work we show an implementation of linear economic model predictive control (MPC) for flexible microgrid dispatch based on time-variable electricity prices. We focus on small and medium enterprises (SME) where information and communications technology (ICT) is available on an industrial level. Our implementation uses field devices and is evaluated in a hardware-in-the-loop (HiL) test bench to achieve high technological maturity. We use available forecasting techniques for power demand and renewable energy generation and evaluate their influence on energy system operation compared to optimal operation under perfect knowledge of the future and compared to a status-quo operation strategy without control. The evaluation scenarios are based on an extensive electricity price analysis to increase representativeness of the simulation results and are based on the use of historic real-world measurements in an existing production facility. Due to real-world restrictions (imperfect forecast knowledge, implementation on field hardware, power fluctuations), between 72.2% and 85.5% of the economic optimum (rather than 100%) is reached. Together with reduced operation cost, the economic MPC implementation on field-typical industrial ICT leads to an increased share of renewable energy demand.

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