Premium
Adaptive neural control of MIMO uncertain nonlinear systems with unmodeled dynamics and output constraint
Author(s) -
Zhang Tianping,
Liu Heqing,
Xia Meizhen,
Yi Yang
Publication year - 2018
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/acs.2939
Subject(s) - control theory (sociology) , adaptive control , controller (irrigation) , nonlinear system , lyapunov function , mimo , bounded function , constraint (computer aided design) , artificial neural network , block (permutation group theory) , computer science , mathematics , control (management) , artificial intelligence , computer network , mathematical analysis , channel (broadcasting) , physics , geometry , quantum mechanics , agronomy , biology
Summary In this paper, an adaptive dynamic surface control (DSC) method is investigated for a class of multiple‐input–multiple‐output block‐structure uncertain nonlinear systems with unmodeled dynamics and output constraint. Modified DSC is used to construct the vector virtual control and control strategy in recursive each step. A dynamic signal is used to handle unmodeled dynamics in the system. Radial basis function neural networks are used to estimate the unknown continuous black‐box functions. By introducing the symmetric barrier Lyapunov functions, the constraint requirement of each element in the output is guaranteed in the designed adaptive control system. Using the defined compact set in the theoretical analysis and the proof characteristics of DSC, the introduced input uncertain term is effectively deal with in the controller design. The stability analysis shows that all the signals in the closed‐loop control system are semiglobally uniformly ultimately bounded. A numerical simulation example with the two‐link flexible robotic system is provided to verify the effectiveness of the constructed control scheme.