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Disturbance observer‐based two‐Layer control strategy design to deal with both matched and mismatched uncertainties
Author(s) -
Shafei Hamid Reza,
Bahrami Mohsen,
Talebi Heidar Ali
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5356
Subject(s) - control theory (sociology) , sliding mode control , nonlinear system , robustness (evolution) , computer science , riccati equation , lyapunov function , robust control , algebraic riccati equation , state observer , controller (irrigation) , adaptive control , exponential stability , lyapunov stability , control engineering , control system , variable structure control , control (management) , mathematics , engineering , differential equation , artificial intelligence , mathematical analysis , chemistry , biology , biochemistry , quantum mechanics , agronomy , physics , gene , electrical engineering
This study presents a novel control strategy for managing nonlinear systems in the presence of mismatched uncertainties. Dealing with the uncertainties that do not satisfy the so‐called matching conditions is an ongoing issue in control engineering. In this regard, and for the first time, a disturbance observer (DO)‐based hybrid control system, which considers robustness as well as control signal optimality, is developed in this article. For this purpose, and with the aim of robustly managing uncertain nonlinear systems and achieving optimized control effort, an optimal control law based on the state‐dependent Riccati equation is integrated with a DO‐based second‐order sliding mode controller. The Lyapunov stability theory is applied to verify the asymptotic stability of the designed control system. Computer simulations are performed to demonstrate the efficacy and the superiority of our novel controller over two other existing methods (DO‐based sliding mode control [SMC] and DO‐based adaptive SMC). The simulation results show that the presented method is capable of managing uncertain nonlinear systems robustly and with much less control effort than the two mentioned methods.