基于车轮反作用力矩的地面机器人空中定向反馈线性化与自适应反推控制的比较
Comparing Feedback Linearization and Adaptive Backstepping Control for Airborne Orientation of Agile Ground Robots using Wheel Reaction Torque
摘要：
在本文中，两种非线性方法稳定的四轮独立驱动和转向（4WIDS）机器人的方向，而在空中进行了分析，实现仿真，并进行了比较。AGRO（敏捷地面机器人）是一种4WIDS检查机器人，可以通过投掷部署到不安全的环境中，并可以使用其四个轮子的反作用扭矩来控制其在空中的方向。之前的工作已在硬件原型上证明，具有手动调整增益的简单PD控制足以在500 ms内稳定方向，但并非最佳。本文的目标是利用非线性控制方法来减少系统的镇定时间和抑制干扰。加入了基于模型的反馈线性化（FL）来补偿非线性科里奥利项。然而，外部干扰，模型不确定性和传感器噪声，FL控制器不能保证稳定性。作为一种替代方案，第二个控制器的开发使用反推方法与自适应补偿器的外部干扰，模型的不确定性，和传感器的偏移。控制器的设计采用李雅普诺夫分析。使用AGRO的全非线性动力学在各向同性转向配置中编写了仿真，其中在其俯仰和横滚上的控制权限被均衡。在相同的初始条件下，将PD+FL控制方法与反推控制方法进行了仿真比较。反推控制器和PD+FL控制器都在250毫秒内稳定了系统。自适应反推控制器也能够实现这种性能与自适应法律启用和补偿偏移噪声正弦干扰。
英文摘要：
In this paper, two nonlinear methods for stabilizing the orientation of a Four-Wheel Independent Drive and Steering (4WIDS) robot while in the air are analyzed, implemented in simulation, and compared. AGRO (the Agile Ground Robot) is a 4WIDS inspection robot that can be deployed into unsafe environments by being thrown, and can use the reaction torque from its four wheels to command its orientation while in the air. Prior work has demonstrated on a hardware prototype that simple PD control with hand-tuned gains is sufficient, but hardly optimal, to stabilize the orientation in under 500ms. The goal of this work is to decrease the stabilization time and reject disturbances using nonlinear control methods. A model-based Feedback Linearization (FL) was added to compensate for the nonlinear Coriolis terms. However, with external disturbances, model uncertainty and sensor noise, the FL controller does not guarantee stability. As an alternative, a second controller was developed using backstepping methods with an adaptive compensator for external disturbances, model uncertainty, and sensor offset. The controller was designed using Lyapunov analysis. A simulation was written using the full nonlinear dynamics of AGRO in an isotropic steering configuration in which control authority over its pitch and roll are equalized. The PD+FL control method was compared to the backstepping control method using the same initial conditions in simulation. Both the backstepping controller and the PD+FL controller stabilized the system within 250 milliseconds. The adaptive backstepping controller was also able to achieve this performance with the adaptation law enabled and compensating for offset noisy sinusoidal disturbances.
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