Scholarlink

<p>With the continuous increase in the operating speed of high-speed railways, the fluctuation of the contact force in pantograph–catenary systems becomes increasingly significant, posing higher requirements for current collection quality and operational reliability. Conventional passive control methods mainly rely on structural parameter optimization and lack online adaptability, which limits their effectiveness under complex operating conditions. Although the Linear Quadratic Regulator (LQR) provides good response performance and robustness, its weighting matrices are typically selected through empirical tuning, and the fixed-gain structure cannot adequately address the nonlinear and time-varying characteristics of pantograph–catenary systems. To overcome these limitations, this paper proposes an Adaptive Linear Quadratic Regulator based on the Multi-strategy Dandelion Optimization Algorithm (ALQR-MDO). First, the original Dandelion Optimization (DO) algorithm is enhanced by integrating several improvement strategies, including boundary reflection, sub-elite guidance, Gaussian–Cauchy hybrid mutation, and dynamic population adjustment, thereby improving global search capability and convergence efficiency. Second, an adaptive LQR controller based on pantograph head displacement-segmented gain scheduling is developed, in which smooth transitions of feedback gains under different operating conditions are achieved using linear interpolation. Subsequently, the weighting matrices of the ALQR controller are treated as optimization variables, and a fitness function considering both pantograph head displacement and contact force fluctuations is constructed. The optimal control parameters are obtained using the MDO algorithm. Finally, a pantograph–catenary coupled dynamic model is established and validated through simulations under different operating speeds. Additionally, control energy consumption analysis and closed-loop stability verification are conducted. Simulation results show that under 200 km/h operating conditions, the proposed ALQR-MDO method reduces the standard deviation and range of contact force as well as the standard deviation and range of pantograph head displacement by 49.72%, 41.92%, 59.69%, and 50.96%, respectively, compared with passive control. Under 300 km/h, the corresponding improvements reach 50.48%, 48.14%, 54.95%, and 48.60%. These results demonstrate that the proposed method significantly improves current collection performance while exhibiting strong adaptability to high-speed operating conditions and robust disturbance rejection capability.</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

Adaptive LQR active control of pantograph based on MDO algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

查看全文
  • Hui Lichuan,
  • Zhang Hanlin

摘要

With the continuous increase in the operating speed of high-speed railways, the fluctuation of the contact force in pantograph–catenary systems becomes increasingly significant, posing higher requirements for current collection quality and operational reliability. Conventional passive control methods mainly rely on structural parameter optimization and lack online adaptability, which limits their effectiveness under complex operating conditions. Although the Linear Quadratic Regulator (LQR) provides good response performance and robustness, its weighting matrices are typically selected through empirical tuning, and the fixed-gain structure cannot adequately address the nonlinear and time-varying characteristics of pantograph–catenary systems. To overcome these limitations, this paper proposes an Adaptive Linear Quadratic Regulator based on the Multi-strategy Dandelion Optimization Algorithm (ALQR-MDO). First, the original Dandelion Optimization (DO) algorithm is enhanced by integrating several improvement strategies, including boundary reflection, sub-elite guidance, Gaussian–Cauchy hybrid mutation, and dynamic population adjustment, thereby improving global search capability and convergence efficiency. Second, an adaptive LQR controller based on pantograph head displacement-segmented gain scheduling is developed, in which smooth transitions of feedback gains under different operating conditions are achieved using linear interpolation. Subsequently, the weighting matrices of the ALQR controller are treated as optimization variables, and a fitness function considering both pantograph head displacement and contact force fluctuations is constructed. The optimal control parameters are obtained using the MDO algorithm. Finally, a pantograph–catenary coupled dynamic model is established and validated through simulations under different operating speeds. Additionally, control energy consumption analysis and closed-loop stability verification are conducted. Simulation results show that under 200 km/h operating conditions, the proposed ALQR-MDO method reduces the standard deviation and range of contact force as well as the standard deviation and range of pantograph head displacement by 49.72%, 41.92%, 59.69%, and 50.96%, respectively, compared with passive control. Under 300 km/h, the corresponding improvements reach 50.48%, 48.14%, 54.95%, and 48.60%. These results demonstrate that the proposed method significantly improves current collection performance while exhibiting strong adaptability to high-speed operating conditions and robust disturbance rejection capability.