Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model
Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model
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DOI：
10.1109/tnnls.2014.2302475
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发表时间：
2014-03
影响因子：
10.4
通讯作者：
Chenguang Yang;Zhijun Li;Rongxin Cui;Bugong Xu
中科院分区：
文献类型：
--
作者：
Chenguang Yang;Zhijun Li;Rongxin Cui;Bugong Xu
本文对轮式倒立摆（WIP）模型的自动运动控制进行了研究，该模型已广泛应用于大量两轮现代车辆的建模。首先，欠驱动轮式倒立摆模型被分解为一个完全驱动的二阶子系统Σa，它由车辆前进运动的平面运动和偏航角运动组成，以及一个被动（无驱动）的一阶摆倾斜运动子系统Σb。由于子系统Σa的动力学未知以及神经网络（NN）的通用逼近能力，一种自适应神经网络方案被用于子系统Σa的运动控制。采用了模型参考方法，同时参考模型通过有限时间线性二次调节技术进行了优化。受人类控制倒立摆策略的启发，被动子系统Σb中的倾斜角运动通过与子系统Σa的平面向前运动的动态耦合被间接控制，从而可以保证对设定倾斜角的满意跟踪。建立了严格的理论分析，并进行了仿真研究以证明所提出的方法。
In this paper, automatic motion control is investigated for wheeled inverted pendulum (WIP) models, which have been widely applied for modeling of a large range of two wheeled modern vehicles. First, the underactuated WIP model is decomposed into a fully actuated second-order subsystem Σa consisting of planar movement of vehicle forward motion and yaw angular motions, and a passive (nonactuated) first-order subsystem Σb of pendulum tilt motion. Due to the unknown dynamics of subsystem Σa and universal approximation ability of neural network (NN), an adaptive NN scheme has been employed for motion control of subsystem Σa. Model reference approach has been used, whereas the reference model is optimized by finite time linear quadratic regulation technique. Inspired by human control strategy of inverted pendulum, the tilt angular motion in the passive subsystem Σb has been indirectly controlled using the dynamic coupling with planar forward motion of subsystem Σa, such that the satisfactory tracking of set tilt angle can be guaranteed. Rigorous theoretic analysis has been established, and simulation studies have been performed to demonstrate the developed method.