Scholarlink

<p>To ensure the safety of vehicle driving and the accuracy of trajectory tracking, intelligent vehicles must make prompt and precise control decisions when performing trajectory tracking control in complex traffic environments. Nonlinear model predictive control demonstrates a clear advantage in terms of trajectory tracking accuracy. However, solving complex optimization problems leads to significant computational burdens, posing a challenge for improving the real-time performance of control systems. The nonlinear model predictive control, combining dynamic adjustment in the horizon with an adaptive event-triggering mechanism, is proposed. This algorithm employs an improved adaptive event-triggering mechanism to reduce the frequency of solving the optimization control problem, thus improving the control system's real-time performance. Additionally, it utilizes the particle swarm optimization algorithm to dynamically optimize the prediction horizon under varying vehicle velocities and road curvatures, establishing a predictive horizon dynamic adjustment strategy that enhances the trajectory tracking accuracy. The algorithm also dynamically adjusts the control time horizon based on the trajectory tracking accuracy, balancing real-time performance with tracking accuracy. Numerical simulation results demonstrate that the algorithm proposed in this paper significantly reduces the computational burden and effectively improves the real-time performance of the control system, while also enhancing the trajectory tracking accuracy of intelligent vehicles.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Real-time Improvement Measures of Nonlinear Model Predictive Control for Intelligent Vehicle Trajectory Tracking

  • 期刊论文

  • 发表日期：

    2025年5月23日

查看全文
  • Zhiyong Zhang,
  • Hao Lu,
  • Wenming Feng,
  • Wenguang Wu

摘要

To ensure the safety of vehicle driving and the accuracy of trajectory tracking, intelligent vehicles must make prompt and precise control decisions when performing trajectory tracking control in complex traffic environments. Nonlinear model predictive control demonstrates a clear advantage in terms of trajectory tracking accuracy. However, solving complex optimization problems leads to significant computational burdens, posing a challenge for improving the real-time performance of control systems. The nonlinear model predictive control, combining dynamic adjustment in the horizon with an adaptive event-triggering mechanism, is proposed. This algorithm employs an improved adaptive event-triggering mechanism to reduce the frequency of solving the optimization control problem, thus improving the control system's real-time performance. Additionally, it utilizes the particle swarm optimization algorithm to dynamically optimize the prediction horizon under varying vehicle velocities and road curvatures, establishing a predictive horizon dynamic adjustment strategy that enhances the trajectory tracking accuracy. The algorithm also dynamically adjusts the control time horizon based on the trajectory tracking accuracy, balancing real-time performance with tracking accuracy. Numerical simulation results demonstrate that the algorithm proposed in this paper significantly reduces the computational burden and effectively improves the real-time performance of the control system, while also enhancing the trajectory tracking accuracy of intelligent vehicles.