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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Updated: Sep 10, 2025

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Marco de estimación de estado en cascada en tiempo real sobre grupos de mentira para robots con piernas que usan

Botao Liu1, Fei Meng1, Zhihao Zhang1

  • 1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100811, China.

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|August 27, 2025
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Resumen
Este resumen es generado por máquina.

Este estudio introduce un nuevo marco de estimación del estado del robot utilizando la propriocepción. El método mejora la precisión y el rendimiento en tiempo real de los robots con piernas, especialmente durante el contacto con el suelo.

Palabras clave:
El filtro de KalmanEstimación del horizonte móvilpropiocepciónestimación del estado

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Área de la Ciencia:

  • La robótica
  • Estimación del Estado
  • Sistemas de control

Sus antecedentes:

  • La estimación precisa del estado es crucial para el control de robots con piernas.
  • Los sensores propioceptivos ofrecen una rica fuente de información para la estimación.
  • Los métodos existentes luchan con la dinámica de impacto durante la locomoción.

Objetivo del estudio:

  • Desarrollar un marco de estimación de estado en cascada para robots que utilizan la propiocepción.
  • Mejorar la precisión y el rendimiento en tiempo real de la estimación del estado del robot.
  • Para manejar eficazmente el ruido de impacto en la locomoción de robots con piernas.

Principales métodos:

  • Un filtro de Kalman basado en el momento generalizado (GMKF) estima las fuerzas de reacción de tierra.
  • Un filtro de Kalman de estado de error (ESKF) proporciona estimaciones previas del estado.
  • Se formula un problema de estimación de horizonte móvil (MHE) en grupos de Lie y se resuelve con iteración paralela en tiempo real (Para-RTI).

Principales resultados:

  • El marco propuesto permite una estimación estrechamente acoplada de los colectores.
  • Se ha demostrado una mayor precisión y rendimiento en tiempo real en comparación con los métodos existentes.
  • Mitigará eficazmente el ruido de impacto durante el contacto del pie con el suelo en robots con patas.

Conclusiones:

  • El marco basado en la propriocepción en cascada ofrece un enfoque superior para la estimación del estado del robot.
  • El método es particularmente efectivo para robots con patas que navegan en entornos dinámicos.
  • La validación experimental en el robot BQR3 confirma la eficacia del marco.