Near Time-Optimal Real-Time Path Following Under Error Tolerance and System Constraints
Near Time-Optimal Real-Time Path Following Under Error Tolerance and System Constraints
复制标题
容错和系统约束下的近时最优实时路径跟踪
DOI：
10.1115/1.4038651
复制
发表时间：
2018
影响因子：
1.7
通讯作者：
T. Tsao
中科院分区：
文献类型：
--
作者：
Yen;Cheng;T. Tsao
提出了一种受轮廓误差容限和其他原型约束影响的在线快速路径跟踪控制算法，类似于赛道边界内的赛车。提出了一种用于机电系统实时实现的后退二次规划（QP）。该算法的一个关键特征是，通过在沿着轮廓移动时最小化无法到达的目标和实际位置之间的距离来近似具有挑战性的约束最小时间优化，模仿灰狗比赛中追逐兔子诱饵。实现中的建模误差和其他不确定性通过观察者状态反馈进行补偿，该反馈为每个后退地平线优化提供初始状态的实时更新。应用所提出的在线方法，放松了传统离线轨迹规划方法对精确模型的要求。该方法通过在多轴纳米光刻定位系统上实现 1kHz 采样率的实验结果得到证明。
An online fast path following control algorithm subject to contouring error tolerance and other prototypical constraints, analogous to a racing car within track boundaries, is presented. A receding horizon quadratic programming (QP) for real-time implementation on electromechanical systems is proposed. A key feature of the algorithm is that the challenging constrained minimal-time optimization is approximated by minimizing the distance between an unattainable target and actual location when moving along the contour, mimicking pursuing rabbit lures in greyhound racing. Modeling errors and other uncertainties in implementation are compensated for by observer state feedback, which provides real-time updates of initial states for every receding horizon optimization. Applying the proposed online method, the requirement of an accurate model from conventional offline trajectory planning methods is relaxed. The proposed method is demonstrated by experimental results from a 1 kHz sampling rate implementation on a multi-axis nanolithographic position system.