论文标题
具有高效率的自动化车辆控制器设计的连续时间有限摩托ADP
Continuous-time finite-horizon ADP for automated vehicle controller design with high efficiency
论文作者
论文摘要
自动车辆控制器的设计通常可以被配置为最佳控制问题。本文提出了一种连续的时间有限 - 摩尼子近似动态程序（ADP）方法，该方法可以通过分析车辆动力学综合近乎最佳的控制策略。它位于一般政策迭代框架上，它采用价值和庞大的神经网络，分别近似从该系统状态映射到价值功能和控制输入。所提出的方法可以收敛到有限的汉密尔顿 - 雅各比 - 贝尔曼（HJB）方程的近距离解决方案。我们进一步将算法应用于对路径跟踪操作的自动车辆控制模拟。结果表明，提出的ADP方法可以获得近距离策略，其中误差为1％，计算时间较少。此外，提出的ADP算法也适用于非线性控制系统，在非线性控制系统中，ADP比非线性MPC IPOPT求解器快几乎500倍。
The design of an automated vehicle controller can be generally formulated into an optimal control problem. This paper proposes a continuous-time finite-horizon approximate dynamicprogramming (ADP) method, which can synthesis off-line near-optimal control policy with analytical vehicle dynamics. Lying on the general Policy Iteration framework, it employs value andpolicy neural networks to approximate the mappings from thesystem states to value function and control inputs, respectively. The proposed method can converge to the near-optimal solutionof the finite-horizon Hamilton-Jacobi-Bellman (HJB) equation. We further applied our algorithm to the simulation of automated vehicle control for the path tracking maneuver. The results suggest that the proposed ADP method can obtain the near-optimal policy with 1% error and less calculation time. What is more, the proposed ADP algorithm is also suitable for nonlinear control systems, where ADP is almost 500 times faster than the nonlinear MPC ipopt solver.
