昆虫尺寸扑翼机器人的建模与LQR控制
Modeling and LQR Control of Insect Sized Flapping Wing Robot
AI总结：
本文针对昆虫尺寸扑翼机器人FIRs，提出基于更精确扑翼周期平均模型的LQR最优控制方法，在150 mg的UW Robofly上实现机载计算的高效稳定悬停与轨迹跟踪，避免了传统PID手工调试和任务特定调参的局限。
摘要：
飞行昆虫能够执行快速而复杂的机动动作，例如后空翻、急转弯和飞行中碰撞恢复。为了在重量不足一克的空中机器人（称为飞行昆虫机器人FIRs）中模拟这些能力，快速且响应灵敏的控制系统至关重要。迄今为止，这些控制系统的核心在很大程度上是比例-积分-微分（PID）型反馈控制的改进形式。无一例外，它们的增益都是通过手工精心调试的。攻击性机动还需要针对特定任务进行调试。最优控制有潜力缓解这些问题，但迄今为止仅使用近似模型和后退地平线控制器（RHC）进行了演示，这些控制器计算量过大，无法在机器人机载执行。在此，我们使用更精确的扑翼周期平均力与力矩模型，首次在FIR上实现了最优控制演示，其计算效率足够高，可以由机载微处理器执行。我们使用定制的灵敏力-力矩传感器对150 mg的FIR——UW Robofly进行了力和力矩测量，并利用自由飞行中的运动捕捉数据对其进行了验证。我们使用最优线性二次调节器（LQR）演示了稳定悬停（RMS误差约4 cm）以及平移速度高达25 cm/s的轨迹跟踪机动。这些结果得益于更精确的模型，并为未来工作奠定了基础，即利用我们改进的模型和最优控制器，结合低功耗后退地平线控制的最新进展，在不进行迭代式、任务特定调试的情况下执行精确的攻击性机动。
英文摘要：
Flying insects can perform rapid, sophisticated maneuvers like backflips, sharp banked turns, and in-flight collision recovery. To emulate these in aerial robots weighing less than a gram, known as flying insect robots (FIRs), a fast and responsive control system is essential. To date, these have largely been, at their core, elaborations of proportional-integral-derivative (PID)-type feedback control. Without exception, their gains have been painstakingly tuned by hand. Aggressive maneuvers have further required task-specific tuning. Optimal control has the potential to mitigate these issues, but has to date only been demonstrated using approxiate models and receding horizon controllers (RHC) that are too computationally demanding to be carried out onboard the robot. Here we used a more accurate stroke-averaged model of forces and torques to implement the first demonstration of optimal control on an FIR that is computationally efficient enough to be performed by a microprocessor carried onboard. We took force and torque measurements from a 150 mg FIR, the UW Robofly, using a custom-built sensitive force-torque sensor, and validated them using motion capture data in free flight. We demonstrated stable hovering (RMS error of about 4 cm) and trajectory tracking maneuvers at translational velocities up to 25 cm/s using an optimal linear quadratic regulator (LQR). These results were enabled by a more accurate model and lay the foundation for future work that uses our improved model and optimal controller in conjunction with recent advances in low-power receding horizon control to perform accurate aggressive maneuvers without iterative, task-specific tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
