We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Adaptive stabilization of constrained stochastic nonlinear systems with input saturation: A combined BLF and NN approach.
- Authors
Min, Huifang; Shi, Shang; Feng, Hongyan
- Abstract
This paper investigates the adaptive control for a class of constrained stochastic nonlinear systems with parametric uncertainty and input saturation. Based on a novel radial basis function neural networks (RBF NNs), the nonlinearities are tackled without the prior knowledge of NN nodes and weights. The approximate coordinate coordination is combined with an auxiliary system to attenuate the effects generated by input saturation. Then, an opportune backstepping design procedure is presented using the barrier Lyapunov function (BLF) and RBF NN. Based on this design procedure, an adaptive state–feedback controller is constructed, which makes the closed-loop system semi-globally uniformly ultimately bounded, the tracking error bounded in a compact set of the origin, and the full-states not violated. Finally, a stochastic single-link robot arm system is simulated to demonstrate the effectiveness of the proposed scheme.
- Subjects
ADAPTIVE control systems; NONLINEAR systems; STOCHASTIC systems; RADIAL basis functions; BACKSTEPPING control method; CLOSED loop systems
- Publication
Transactions of the Institute of Measurement & Control, 2024, Vol 46, Issue 8, p1520
- ISSN
0142-3312
- Publication type
Article
- DOI
10.1177/01423312231200040