基于神经网络的遥控车辆多模型控制

M. Cavalletti, G. Ippoliti, S. Longhi
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引用次数: 2

摘要

本文研究了水下航行器在不同载荷配置下的跟踪控制问题,这些载荷配置会不时引入其质量和惯性参数的较大变化。由于自适应时间过长,传统的自适应控制技术无法充分面对这种模式切换过程的控制。为了解决这一问题,提出了一种切换控制方案,并定义了多个基于神经网络的对象模型来描述车辆的不同可能运行状态。通过数值仿真对切换控制器的性能进行了评价
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Multiple Model Control Using Neural Networks for a Remotely Operated Vehicle
This paper considers the tracking control problem of an underwater vehicle subjected to different load configurations, which from time to time introduce considerable variations of its mass and inertial parameters. The control of this kind of mode-switch process can not be adequately faced with traditional adaptive control techniques because of the too long time needed for adaptation. To cope with this problem, a switching control scheme is proposed and multiple neural network-based plant models are defined to describe the different possible operative conditions of the vehicle. The performance of the switched controller is evaluated by numerical simulations
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