Feasibility Guaranteed Traffic Merging Control Using Control Barrier Functions
Feasibility Guaranteed Traffic Merging Control Using Control Barrier Functions
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DOI：
10.23919/acc53348.2022.9867620
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Kaiyuan Xu;Wei Xiao;C. Cassandras
中科院分区：
文献类型：
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作者：
Kaiyuan Xu;Wei Xiao;C. Cassandras
我们考虑互联和自动驾驶车辆（CAV）的合并控制问题，旨在共同最大限度地减少旅行时间和能源消耗，同时提供速度相关的安全保证，并满足速度和加速度约束。应用联合最优控制和控制障碍函数（OCBF）方法，通过将最优跟踪问题转化为一系列二次规划（QP），有效地得到了一个能最优跟踪无约束最优控制解同时保证满足所有约束的控制器.然而，这些QP可能变得不可行，特别是在严格的控制界限下，从而无法保证安全约束。我们解决这个问题，推导出一个控制相关的可行性约束对应于每个CBF约束，将其添加到每个QP，并表明这种修改的QP是保证是可行的。合并控制问题的大量仿真说明了这种可行性保证控制器的有效性。
We consider the merging control problem for Connected and Automated Vehicles (CAVs) aiming to jointly minimize travel time and energy consumption while providing speed-dependent safety guarantees and satisfying velocity and acceleration constraints. Applying the joint optimal control and control barrier function (OCBF) method, a controller that optimally tracks the unconstrained optimal control solution while guaranteeing the satisfaction of all constraints is efficiently obtained by transforming the optimal tracking problem into a sequence of quadratic programs (QPs). However, these QPs can become infeasible, especially under tight control bounds, thus failing to guarantee safety constraints. We solve this problem by deriving a control-dependent feasibility constraint corresponding to each CBF constraint, add it to each QP and show that such modified QPs are guaranteed to be feasible. Extensive simulations of the merging control problem illustrate the effectiveness of this feasibility guaranteed controller.