Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay
By Dapeng Li, Yanjun Liu, Shaocheng Tong, Chun Lung Philip Chen, Dongjuan Li. IEEE Transactions on Cybernetics, 2019
341 citations recorded by the source corpus.
This paper addresses the problem of adaptive tracking control for a class of strict-feedback nonlinear state constrained systems with input delay. To alleviate the major challenges caused by the appearances of full state constraints and input delay, an appropriate barrier Lyapunov function and an opportune backstepping design are used to avoid the constraint violation, and the Pade approximation and an intermediate variable are employed to eliminate the effect of the input delay. Neural networks are employed to estimate unknown functions in the design procedure. It is proven that the closed-loop signals are semiglobal uniformly ultimately bounded, and the tracking error converges to a compact set of the origin, as well as the states remain within a bounded interval. The simulation studies are given to illustrate the effectiveness of the proposed control strategy in this paper.
Subfields: Computational Theory and Mathematics, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/f46906e53e0ecd6a1b43a64f3eac76d05ac48fd2
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In Computational Theory and Mathematics
In Control and Systems Engineering
Citations in the corpus
- Removing the Feasibility Conditions Imposed on Tracking Control Designs for State-Constrained Strict-Feedback Systems (2019, methodology): “, n − 1) as imposed in most existing works [9], [10], [12], [13], [15], [21], [28], [31].”