Adaptive neural control for stochastic nonholonomic systems with full-state constraints and unknown covariance noise

Du, Qinghui; Wang, Lixia; Zhao, Chaofeng

doi:10.1080/00036811.2021.2011242

Cited by 1 publication

(1 citation statement)

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“…The authors of [29,30] focused on the tracking controllers for two classes of state-constrained nonholonomic systems. The authors of [31] investigated the adaptive neural control for stochastic nonholonomic systems suffered from unknown covariance noise and full-state constraints. The authors of [32] elaborated upon the MPC-based cooperative controller design for nonholonomic system mobile agents with constraints and disturbances.…”

Section: Introductionmentioning

confidence: 99%

Robust and Adaptive Stabilization Controllers of State-Constrained Nonholonomic Chained Systems: A Discontinuous Approach

Zhang,

Hu,

Gao

et al. 2023

Mathematics

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In this paper, two systematic control design strategies are proposed for strict-feedback nonholonomic systems with full-state constraints to solve stabilization and adaptive stabilization problems. The stabilization schemes involve the introduction of state scaling, the barrier Lyapunov function (BLF), the integrator backstepping method, and the tuning function approach. In addition, a discontinuous switching control strategy is proposed to achieve the control goal if the first system state’s initial state is confined to zero. In both stabilization and adaptive stabilization control, the system states can be regulated at the origin, and meanwhile, the full-state constraints are realized. Finally, it is shown that the simulation results are consistent with the theory analysis results, which further demonstrates the effectiveness of the proposed control schemes.

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