基于收缩约束模型预测控制的无人车辆避障路径跟踪
作者:
作者单位:

1.兰州理工大学;2.曲阜师范大学

作者简介:

通讯作者:

中图分类号:

TP273

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Unmanned vehicle obstacle avoidance path tracking based on contraction constraint model predictive control
Author:
Affiliation:

1.Lanzhou University of Technology;2.Qufu Normal University

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    针对存在有界扰动的非线性无人驾驶车辆避障过程中最优路径规划跟踪问题,本文提出了一种基于预测时域内系统输入输出收缩约束(PIOCC)的模型预测控制(MPC)方法.首先在构建目标函数时,为扩大可行性解的范围引入软约束思想,将最优规划路径的跟随问题转化为对模型预测控制优化问题的求解.其次为避免短预测时域造成闭环系统发散而导致在约束条件限定下出现无可行性解的情况,采用预测时域内系统输入输出收缩约束的方法,设计无人驾驶车辆在避障过程中的路径规划跟踪模型预测控制器.然后基于Lyapunov稳定性理论证明本文所设计的闭环模型预测控制系统的稳定性.最后通过仿真实例,验证了所提出基于PIOCC的控制策略在解决扩大可行解范围和避免闭环系统发散问题时的有效性,实现了无人驾驶车辆在避障过程中跟随最优规划路径时具有良好跟随性和稳定性的控制要求.

    Abstract:

    This paper investigates optimal path planning and tracking in the obstacle avoidance process of nonlinear unmanned vehicles with bounded disturbances, a model predictive control (MPC) method based on the predictive input and output contraction constraints (PIOCC) of the system is proposed. First, when constructing the objective function, the idea of soft constraints is introduced to expand the range of feasible solutions, and the problem of following the optimal planning path is transformed into the solution of the model predictive control optimization problem. Secondly, in order to avoid the divergence of the closed-loop system caused by the short prediction time domain, resulting in infeasible solutions under the constraint conditions, the method of the predictive input and output contraction constraints of the system in the time domain is further adopted. Then based on the Lyapunov stability theory, the stability of the closed-loop model predictive control system designed is proved in this paper. Finally, through a simulation example, it verifies the effectiveness of the proposed control strategy based on PIOCC in solving the problem of expanding the range of feasible solutions and avoiding the divergence of the closed-loop system, the control requirements of good following and stability are realized when the driverless vehicle follows the optimal planning path during obstacle avoidance.

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历史
  • 收稿日期:2020-09-07
  • 最后修改日期:2021-01-15
  • 录用日期:2021-01-19
  • 在线发布日期: 2021-02-04
  • 出版日期: