论文标题
循环模拟中的硬件和TWPTR的分层预测控制
Hardware in the loop simulation and hierarchical predictive control of a TWPTR
论文作者
论文摘要
在本文中，我们使用非线性分层模型预测控制（MPC）稳定Segway机器人。我们还使用循环（HIL）模拟中的硬件来对车轮电动机的延迟响应进行建模并验证控制算法。在两轮的个人运输机器人（TWPTR）中，更改质量位置和价值的中心，方程式的非线性以及系统的动态是使控制问题复杂化的主题。非线性MPC可以预测系统的动力学并有效地解决控制问题，但需要系统模型的确切信息。由于模型不确定性是不可避免的，因此使用时间延迟控制（TDC）方法来取消未知的动态和意外的干扰。应用TDC方法时，结果表明发动机所需的最大扭矩降低了7％。在平衡轴周围，机器人的最大位移下降了44％。换句话说，机器人稳定性增加了78％。由于实践中实施控制的成本，这项研究首次进行了HIL模拟。此模拟的使用有助于无需近似就可以实现控制算法，并且可以更现实地讨论系统响应。
In this paper, we use a nonlinear hierarchical model predictive control (MPC) to stabilize the Segway robot. We also use hardware in the loop (HIL) simulation in order to model the delay response of the wheels' motor and verify the control algorithm. In Two-Wheeled Personal Transportation Robots (TWPTR), changing the center of mass location and value, the nonlinearity of the equations, and the dynamics of the system are topics complicating the control problem. A nonlinear MPC predicts the dynamics of the system and solves the control problem efficiently, but requires the exact information of the system models. Since model uncertainties are unavoidable, the time-delay control (TDC) method is used to cancel the unknown dynamics and unexpected disturbances. When the TDC method is applied, the results show that the maximum required torque for engines is reduced by 7%. And the maximum displacement of the robot has dropped by 44% around the balance axis. In other words, robot stability has increased by 78%. Due to the cost of implementing control in practice, this research runs the HIL simulation for the first time. The use of this simulation helps in implementing the control algorithms without approximation, and also the system response can be discussed in a more realistic way.
