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Self‐tuning predictive control with forecasting factor for control of pneumatic lumber handling systems
Author(s) -
Wang Xiaochun George,
Kim Chris
Publication year - 2000
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/1099-1115(200008)14:5<533::aid-acs612>3.0.co;2-k
Subject(s) - model predictive control , control theory (sociology) , controller (irrigation) , dual (grammatical number) , control (management) , range (aeronautics) , function (biology) , forgetting , control engineering , engineering , computer science , artificial intelligence , art , linguistics , philosophy , literature , evolutionary biology , agronomy , biology , aerospace engineering
A newly modified predictive control strategy is proposed for controlling systems with severe non‐linearity, such as pneumatic systems. This strategy is based on a modification to the cost function used in generalized predictive control. In particular, a forecast factor is defined and incorporated into the cost function of controller design in order to consider increased inaccuracies of prediction as prediction range increases. This forecast factor has a dual property with that of the forgetting factor used for estimation and this duality between estimation using past data and predictive control for future output can also be considered an extension to the duality principle of estimation and control. The control strategies are implemented for a project to improve the control of heavy industrial log handling machines and it is shown that this new self‐tuning predictive control scheme can achieve much improved performance over other control schemes. Copyright © 2000 John Wiley & Sons, Ltd.