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Tensor product model transformation‐based control for fractional‐order biological pest control systems
Author(s) -
Boonyaprapasorn Arsit,
Kuntanapreeda Suwat,
Sa Ngiamsunthorn Parinya,
Pengwang Eakkachai,
Sangpet Teerawat
Publication year - 2021
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.2492
Subject(s) - controller (irrigation) , tensor product , control theory (sociology) , nonlinear system , population , computer science , fractional calculus , mathematical optimization , pest analysis , process (computing) , order (exchange) , representation (politics) , mathematics , control engineering , control (management) , engineering , artificial intelligence , biology , sociology , operating system , quantum mechanics , agronomy , physics , demography , pure mathematics , law , political science , botany , finance , politics , economics
As environmental pollution and safety of human and creatures have been an issue of concern, pest management based on nonchemical use is preferable. Biological pest control can satisfy the regulation of pest population without causing environmental problems. Moreover, representation of dynamical systems in the form of fractional‐order dynamic models can explain several phenomena better than the integer‐order dynamic models can. The aim of this study is to determine the biological pest control policy for the fractional‐order pest control systems, which is a complex nonlinear multiple input and multiple output control problem. Here, the system is represented in the form of a fractional‐order Lotka–Volterra model. In order to avoid complexity in the nonlinear feedback controller design process, the tensor product (TP) model transformation was applied to automatically transform the biological pest control system to the TP polytopic model which allows the designer to synthesize the controller under linear framework. Moreover, the design process can be easily carried out by solving an linear matrix inequality (LMI) problem, which is very well applicable to high‐dimensional multiple input and multiple output control problems. Simulation studies were conducted to demonstrate the ease of the TP‐based design process. The simulation results showed that the designed control policy effectively manipulates the pest populations of the fractional‐order Lotka–Volterra model to the desired level which is economically viable.