Adaptive Critic Trajectory Tracking Control of Autonomous Wheeled Robot
Date Issued
2006
Date
2006
Author(s)
Yang, Ping-Chieh
DOI
en-US
Abstract
The trajectory tracking controller of a wheeled mobile robot (WMR) is designed to compose an adaptive critic velocity controller and a neural network based posture controller. The learning algorithm of the adaptive critic velocity controller is derived by using dual heuristic programming (DHP) method. This adaptive critic design enables the WMR system to comply with variant road conditions. The posture controller design uses MLP neural network to learn the inverse drive model of WMR. The training patterns are obtained by continuously driving WMR with a variety of velocities. Vision information about a desired trajectory in the near future is utilized in both path planner and posture controller. This information can prepare the WMR system for changes in the trajectory in advance. Extensive simulation studies have verified the feasibility of the proposed design.
Subjects
自評自調法
追跡控制
機器人
類神經網路
Adaptive critic design
tracking control
mobile robot
neural network
Type
thesis
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