Fixed‐time fault‐tolerant control of uncertain MIMO switched interconnected systems subject to state delay, unknown control directions, and quantized nonlinear inputs

Summary This paper investigates design of an adaptive fixed‐time fault‐tolerant decentralized controller for a class of uncertain multi‐input multi‐output (MIMO) switched large‐scale non‐strict interconnected systems under arbitrary switching subject to unknown control directions, quantized nonlinea...

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Bibliographic Details
Published in:International journal of adaptive control and signal processing 2023-03, Vol.37 (3), p.666-693
Main Authors: Ghoreishee, Sayed Aref, Shahrokhi, Mohammad, Vafa, Ehsan, Moradvandi, Ali
Format: Article
Language:English
Subjects:
Actuator failure
actuator fault
Controllers
fixed‐time control
input nonlinearities
Liapunov functions
MIMO (control systems)
MIMO communication
MIMO switched systems
Neural networks
Nonlinear control
Nonlinearity
non‐strict feedback systems
Norms
quantized input
Switching
Systems stability
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Summary:Summary This paper investigates design of an adaptive fixed‐time fault‐tolerant decentralized controller for a class of uncertain multi‐input multi‐output (MIMO) switched large‐scale non‐strict interconnected systems under arbitrary switching subject to unknown control directions, quantized nonlinear inputs, actuator failures unknown external disturbances, and unmodeled dynamics. In addition to interconnected terms, time‐varying delayed interconnected terms have been considered in the system model which makes it more general than previous works in the literature. The proposed controller can handle switched systems with unknown switching signal and different types of input nonlinearities including, saturation, backlash, and dead‐zone. The singularity problem in designing the fixed time controller has been solved. The quantizer and actuators fault parameters are assumed to be unknown. The Razumikhin lemma has been used to deal with the delayed interconnected terms. To cope with the system unknown dynamics, neural networks (NNs) have been applied and by updating the maximum norms of the networks weight vectors the computational load has been reduced. The explosion of complexity occurring in the traditional back‐stepping technique has been avoided by applying dynamic surface control (DSC). Finally, by defining an appropriate common Lyapunov function (CLF), fixed‐time convergence of system outputs and the closed‐loop system stability have been established. The effectiveness of the proposed controller has been shown via simulation study.
ISSN:0890-6327
1099-1115
DOI:10.1002/acs.3541