Abstract
In this paper, a novel adaptive sliding mode controller (SMC) was designed based on a robust law considering disturbances and uncertainties for autonomous ground vehicle (AGV) longitudinal dynamics. The robust law was utilized in an innovative method involving the upper bounds of disturbances and uncertainties. Estimating this lumped uncertainty upper limit based on a neural network approach allowed its online knowledge. It guided the controller to withstand the disturbance and to compensate for the uncertainties. A stability analysis, according to Lyapunov, was completed to confirm the asymptotic convergence of the states to the desired state. The effectiveness and benefits of the planned approach were scrutinized by simulations and comparative studies.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference46 articles.
1. El Hajjami, L., Mellouli, E.M., and Berrada, M. (2020, January 16–19). Neural network based sliding mode lateral control for autonomous vehicle. Proceedings of the 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco.
2. Simulation research on emergency path planning of an active collision avoidance system combined with longitudinal control for an autonomous vehicle;Cao;Proc. Inst. Mech. Eng. Part J. Automob. Eng.,2016
3. Automated vehicle control developments in the path program;Shladover;IEEE Trans. Veh. Technol.,1991
4. Autonomous road vehicles: Recent issues and expectations;Skrickij;IET Intell. Transp. Syst.,2020
5. Robust finite-time tracking control based on disturbance observer for an uncertain quadrotor under external disturbances;Hassani;J. Robot.,2022
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献