Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Dynamic adjustment neural network-based cooperative control for vehicle platoons with state constraints

Wang Ping, Gao Min, Li Junyu, Zhang Anguo

International Journal of Applied Mathematics and Computer Science

2024

Vol. 34, no. 2

211--224

This paper addresses the challenge of managing state constraints in vehicle platoons, including maintaining safe distances and aligning velocities, which are key factors that contribute to performance degradation in platoon control. Traditional platoon control strategies, which rely on a constant time-headway policy, often lead to deteriorated performance and even instability, primarily during dynamic traffic conditions involving vehicle acceleration and deceleration. The underlying issue is the inadequacy of these methods to adapt to variable time-delays and to accurately modulate the spacing and speed among vehicles. To address these challenges, we propose a dynamic adjustment neural network (DANN) based cooperative control scheme. The proposed strategy employs neural networks to continuously learn and adjust to time varying conditions, thus enabling precise control of each vehicle’s state within the platoon. By integrating a DANN into the platoon control system, we ensure that both velocity and inter-vehicular spacing adapt in response to real-time traffic dynamics. The efficacy of our proposed control approach is validated using both Lyapunov stability theory and numeric simulation, which confirms substantial gains in stability and velocity tracking of the vehicle platoon.

Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties

Peng Cheng, Zhang Anguo, Li Junyu

International Journal of Applied Mathematics and Computer Science

2021

Vol. 31, no. 4

635--645

The consensus problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty is studied. We design an online-update radial basis function (RBF) neural network based distributed adaptive control protocol, where the sliding model control method is also applied to eliminate the influence of the external disturbance and system uncertainty. System consensus is verified by using the Lyapunov stability theorem, and sufficient conditions for cooperative uniform ultimately boundedness (CUUB) are also derived. Two simulation examples demonstrate the effectiveness of the proposed method for both homogeneous and heterogeneous MASs.