Learning control of mobile robots using a multiprocessor system

被引:23
作者
Antonini, P. [1 ]
Ippoliti, G. [1 ]
Longhi, S. [1 ]
机构
[1] Univ Politecn Marche, Dipartimento Ingn Informat Gestionale & Automaz, Via Brecce Bianche, I-60131 Ancona, Italy
关键词
robotics; autonomous vehicles; multiple models; multiprocessor systems; neural networks;
D O I
10.1016/j.conengprac.2005.06.012
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A real-time multiprocessor system is proposed for the solution of the tracking problem of mobile robots operating in a real context with environmental disturbances and parameter uncertainties. The proposed control scheme utilizes multiple models of the robot for its identification in an adaptive and learning control framework. Radial Basis Function Networks (RBFNs) are considered for the multiple models in order to exploit the net non-linear approximation capabilities for modeling the kinematic behavior of the vehicle and for reducing unmodeled contributions to tracking errors. The training of the nets and the tests of the achieved control performance have been done in a real experimental setup. The proposed control architecture improves the robot tracking performance achieving fast and accurate control actions in presence of large and time-varying uncertainties in dynamical environments. The experimental results are satisfactory in terms of tracking errors and computational efforts. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1279 / 1295
页数:17
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