Locally Optimal Estimation and Control of Cable Driven Parallel Robots using Time Varying Linear Quadratic Gaussian Control
Locally Optimal Estimation and Control of Cable Driven Parallel Robots using Time Varying Linear Quadratic Gaussian Control
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DOI：
10.1109/iros47612.2022.9981144
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Gerry Chen;S. Hutchinson;F. Dellaert
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文献类型：
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作者：
Gerry Chen;S. Hutchinson;F. Dellaert
提出了一种基于时变线性二次高斯（TV-LQG）控制器的索驱动并联机器人（CDPR）局部最优跟踪控制器。与许多使用固定反馈增益的方法相比，我们的时变控制器根据工作空间中的位置和未来轨迹计算最佳增益。同时，我们在很大程度上依赖于离线计算，以减少在线实现和可行性检查的负担。随着概率图模型在最优控制中的日益普及，我们使用因子图作为工具来制定我们的控制器，以提高其效率，直观性和模块性。因子图的拓扑结构编码方程的相关结构属性，以便于使用稀疏线性代数求解器进行洞察和有效计算。我们首先使用因子图优化来计算标称轨迹，然后将图线性化并应用变量消除来计算局部最优的时变线性反馈增益。其次，我们利用因子图公式来计算局部最优的时变卡尔曼滤波器增益，最后联合收割机结合局部最优的线性控制和估计律，形成TV-LQG控制器。我们比较了我们的TV-LQG控制器的跟踪精度，以一个国家的最先进的双空间前馈控制器的2.9米× 2.3米，4电缆平面机器人，并证明了改进的跟踪精度为0.8°和11.6毫米的均方根误差分别在旋转和平移。
We present a locally optimal tracking controller for Cable Driven Parallel Robot (CDPR) control based on a time-varying Linear Quadratic Gaussian (TV-LQG) controller. In contrast to many methods which use fixed feedback gains, our time-varying controller computes the optimal gains depending on the location in the workspace and the future trajectory. Meanwhile, we rely heavily on offline computation to reduce the burden of online implementation and feasibility checking. Following the growing popularity of probabilistic graphical models for optimal control, we use factor graphs as a tool to formulate our controller for their efficiency, intuitiveness, and modularity. The topology of a factor graph encodes the relevant structural properties of equations in a way that facilitates insight and efficient computation using sparse linear algebra solvers. We first use factor graph optimization to compute a nominal trajectory, then linearize the graph and apply variable elimination to compute the locally optimal, time varying linear feedback gains. Next, we leverage the factor graph formulation to compute the locally optimal, time-varying Kalman Filter gains, and finally combine the locally optimal linear control and estimation laws to form a TV-LQG controller. We compare the tracking accuracy of our TV-LQG controller to a state-of-the-art dual-space feed-forward controller on a 2.9m x 2.3m, 4-cable planar robot and demonstrate improved tracking accuracies of 0.8° and 11.6 mm root mean square error in rotation and translation respectively.