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Design and implementation of a kind of neural networks robustness controller for variable structure bicycle robot's track-stand motion

机译:一种用于可变结构自行车机器人轨道站运动的神经网络鲁棒控制器的设计与实现

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This paper focus on dynamic modeling and a kind of controller for the variable structure bicycle robot's (which called VSBR) track-stand motion when its front wheel is fixed at 45 degrees about its front fork. Firstly, making dynamic analysis for the VSBR whose front wheel is fixed at arbitrary degrees about its front fork. The roll angle, yaw angle, the angle front fork rotated and the angle front wheel rolling are chosen as generalized coordinates. A kind of dynamic model of VSBR is built based on Routh equation. The angle of front fork rotated is fixed at 45 degrees in this dynamic model. And the angle of back wheel rolling is 0 in the track-stand motion. At last two dynamic differential equations are presented and the dynamic model is built. The robust controller is designed based on RBF neutral network arithmetic. Feedforward term in the controller is used for nonlinear compensation in the controller. The drive torque of the motor which is driving front wheel is output of the controller. The roll angle of body of VSBR and the angle that front wheel rotated are inputs of the controller. A multiple-input single-output (MISO) controller is built. The L-gain of the system would be adjusted to make the controller robust. The simulation results of the RBF robust controller and the dynamic model of VSBR show feasibility and effectiveness of the dynamic model and the designed controller.
机译:本文侧重于动态建模和一种控制器的可变结构自行车机器人(其中称为VSBR)轨道站运动,当其前轮固定在其前叉45度时。首先,对前轮固定在其前叉的任意度的VSBR的动态分析。辊角,偏航角,旋转角度前叉,角度前轮滚动被选择为广义坐标。基于Routh方程构建了一种VSBR动态模型。前叉旋转的角度在该动态模型中以45度固定。在轨道支架运动中,背轮滚动的角度是0。在最后两个动态微分方程,构建了动态模型。鲁棒控制器基于RBF中性网络算法设计。控制器中的前馈期用于控制器中的非线性补偿。驱动前轮的电动机的驱动扭矩是控制器的输出。 VSBR主体的滚动角和前轮旋转的角度是控制器的输入。构建了多输入单输出(MISO)控制器。将调整系统的L增益以使控制器稳健。 RBF鲁棒控制器的仿真结果和VSBR动态模型显示动态模型和设计控制器的可行性和有效性。

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