Premium
Multi‐objective optimal motion control of a laboratory helicopter based on parallel simple cell mapping method
Author(s) -
Qin ZhiChang,
Xin Ying,
Sun JianQiao
Publication year - 2020
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.2040
Subject(s) - control theory (sociology) , nonlinear system , optimal control , subdivision , pareto principle , simple cell , set (abstract data type) , multi objective optimization , controller (irrigation) , pid controller , mathematical optimization , simple (philosophy) , control system , computer science , control engineering , control (management) , engineering , mathematics , artificial intelligence , philosophy , epistemology , temperature control , receptive field , agronomy , physics , civil engineering , electrical engineering , quantum mechanics , biology , programming language
This paper presents a study of multi‐objective optimal design of nonlinear control systems and has validated the control design with a twin rotor model helicopter. The gains of the porportional integral differential (PID) control are designed in the framework of multi‐objective opitmization. Eight design paramaters are optimized to minimize six time‐domain objective objective functions. The study of multi‐objective optimal design of feedback control with such a number of design paramaters and objective functions is rare in the literature. The Pareto optimal solutions are obtained by the proposed parallel simple cell mapping method consisting of a robust Pareto set recovery algorithm and a rolling subdivision technique. The proposed parallel simple cell mapping algorithm has two features: the number of cells in the invariant set grows linearly with the rolling subdivisions, and the Pareto set is insensitive to the inital set of seed cells. The current control design is compared with the classical LQE control for linear systems, and is also experimentally validated. The current design provides improved control performance as compares with the LQR control, and is applicable to complex nonlinear systems.