Virtual Leader Based Formation Control of Multiple Unmanned Ground Vehicles (UGVs): Control Design, Simulation and Real-Time Experiment

2007 ◽  
pp. 221-230 ◽  
Author(s):  
Wenchuan Cai ◽  
Liguo Weng ◽  
R. Zhang ◽  
M. Zhang ◽  
Y. D. Song
Keyword(s):  
Real Time ◽  
Formation Control ◽  
Control Design ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Virtual Leader

scholarly journals Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network

2022 ◽  
Vol 12 (2) ◽  
pp. 682
Author(s):  
Yuzhan Wu ◽  
Chenlong Li ◽  
Changshun Yuan ◽  
Meng Li ◽  
Hao Li
Keyword(s):  
Objective Function ◽  
Real Time ◽  
Predictive Control ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Velocity Signal ◽  
The Stability

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

Mission Energy Prediction for Unmanned Ground Vehicles Using Real-time Measurements and Prior Knowledge

2013 ◽  
Vol 30 (3) ◽  
pp. 399-414 ◽  
Author(s):  
Amir Sadrpour ◽  
Jionghua Judy Jin ◽  
A. Galip Ulsoy
Keyword(s):  
Real Time ◽  
Prior Knowledge ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Energy Prediction

scholarly journals Formation Control of Unmanned Ground Vehicles with Particle Swarm Optimization

2021 ◽  
Author(s):  
Siyao Gu ◽  
Marcos Bird ◽  
Scott Timme ◽  
Eric Ortega ◽  
Yufang Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Formation Control ◽  
Particle Swarm ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Swarm Optimization

Study on real-time obstacle avoidance for unmanned ground vehicles

ICCAS 2010 ◽  
2010 ◽  
Author(s):  
Sangwoo Moon ◽  
Unghui Lee ◽  
David Hyunchul Shim
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles
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