Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots
Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots
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DOI：
10.1109/tro.2023.3308773
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发表时间：
2022-07
影响因子：
7.8
通讯作者：
Nathan J. Kong;Chuanzheng Li;George Council;Aaron M. Johnson
中科院分区：
文献类型：
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作者：
Nathan J. Kong;Chuanzheng Li;George Council;Aaron M. Johnson
模型预测控制（MPC）是一种流行的控制机器人的策略，但由于混合动力学的复杂性，很难与接触系统。为了实现MPC的系统接触，动态模型往往被简化或接触序列固定的时间，以便有效地规划轨迹。在这项工作中，我们提出了混合迭代线性二次调节器（iLQR）（HiLQR），它扩展了iLQR的一类分段光滑的混合动态系统的状态跳。这是通过以下方式实现的：首先，允许在前向通道中改变混合模式，其次，使用突变矩阵来更新后向通道中的梯度信息，以及第三，使用参考扩展来考虑模式失配。我们证明了这些变化的各种混合动力系统，并比较不同的策略来计算梯度。我们进一步展示了如何HiLQR可以工作在MPC的方式（HiLQR MPC），第一，修改如何计算成本函数时，接触模式不对齐，第二，利用并行化时，模拟刚体动力学，第三，使用高效的解析导数计算的刚体动力学。结果是一个系统，可以修改参考行为的接触序列，并规划整个身体的运动凝聚力-这是至关重要的，当处理大扰动。HiLQR MPC在两个系统上进行了测试：首先，在一个简单的驱动弹跳球混合动力系统上验证了混合动力成本修改。然后，HiLQR MPC的比较方法，利用质心动力学假设的四足机器人（Unitree A1）。HiLQR MPC在仿真和硬件测试中均优于质心方法。
Model predictive control (MPC) is a popular strategy for controlling robots but is difficult for systems with contact due to the complex nature of hybrid dynamics. To implement MPC for systems with contact, dynamic models are often simplified or contact sequences fixed in time in order to plan trajectories efficiently. In this work, we propose the hybrid iterative linear quadratic regulator (iLQR) (HiLQR), which extends iLQR to a class of piecewisesmooth hybrid dynamical systems with state jumps. This is accomplished by, first, allowing for changing hybrid modes in the forward pass, second, using the saltation matrix to update the gradient information in the backwards pass, and third, using a reference extension to account for mode mismatch. We demonstrate these changes on a variety of hybrid systems and compare the different strategies for computing the gradients. We further show how HiLQR can work in an MPC fashion (HiLQR MPC) by, first, modifying how the cost function is computed when contact modes do not align, second, utilizing parallelizations when simulating rigid body dynamics, and third, using efficient analytical derivative computations of the rigid body dynamics. The result is a system that can modify the contact sequence of the reference behavior and plan whole body motions cohesively—which is crucial when dealing with large perturbations. HiLQR MPC is tested on two systems: first, the hybrid cost modification is validated on a simple actuated bouncing ball hybrid system. Then, HiLQR MPC is compared against methods that utilize centroidal dynamic assumptions on a quadruped robot (Unitree A1). HiLQR MPC outperforms the centroidal methods in both simulation and hardware tests.