Adaptive Neural Control of Wheeled Inverted Pendulum Models
This paper investigate motion control of wheeled inverted pendulum (WIP) models, which have been widely applied for a large class of modern vehicles that can transport human with high safety and work capability. Neural network (NN) has been employed to design adaptive control for the fully actuated tilt and yaw angular motion subsystem using a reference model derived by finite time linear quadratic regulation (LQR) optimization technique. The forward velocity is indirectly “controlled by the implicit control trajectory, which is then planned by an NN based adaptive generator of implicit control trajectory (AGICT).
WIP under actuated system neural network model reference control
Chenguang Yang Zhijun Li
School of Computing and Mathematics, the University of Plymouth, UK, PL4 8AA Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
4400-4405
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）