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Sliding mode control of multi-agent systems with switching topology and input constraints using IT2FNN

agents-and-multi-agent-systems
autonomous-systems
robotics
neural-networks

Abstract

The research analyzes nonlinear multi-agent systems (MASs) with time delays and uncertain dynamics under input saturation constraints by creating an adaptive control protocol that effectively solves these difficulties. Real-time control parameter updates within an RGT2FNN create estimates of unknown non-linear functions to produce stability-assuring control signals during system operation. A novel advanced BBO optimization algorithm handles parameter estimation for RGT2FNN information since their values remain unidentified in the control design process. The control framework incorporates a robustness-enhancing LMI-based compensator that guarantees stability under uncertainty conditions as well as time-varying delays and input saturation constraints since these realistic limitations are directly accounted for through formal prevention strategies. The proposed approach showcases its practical capabilities when used with unknown dynamics and input saturation in a simulation where it demonstrates superior performance in bounded control applications.
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