#5893. Fixed-time composite neural learning control of state-constrained nonlinear uncertain systems

July 2026publication date
Proposal available till 03-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Applied Mathematics;
Computer Networks and Communications;
Control and Systems Engineering;
Signal Processing;
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Abstract:
This paper presents a fixed-time composite neural learning control scheme for nonlinear strict-feedback systems subject to unknown dynamics and state constraints. To address the problem of state constraints, a new unified universal barrier Lyapunov function is proposed to convert the constrained system into an unconstrained one. Taking the unconstrained system, a modified fixed-time convergence state predictor is explored, enabling the prediction error for compensating the neural adaptive law to be obtained and improving the learning ability of online neural networks (NNs). Without employing fractional power terms or a complicated switching strategy to build the control law, a new method of constructing a smooth fixed-time dynamic surface control scheme is proposed. This overcomes the potential singularity problem and the explosion of complexity often encountered in fixed-time back-stepping designs.
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