低成本六自由度平台实现精准实时控制与状态估计
End-to-End Design and Validation of a Low-Cost Stewart Platform with Nonlinear Estimation and Control
- 融合反馈线性化与LQR设计轨迹跟踪控制器
- 扩展卡尔曼滤波融合惯导与编码器数据，实现实时状态估计
- 软硬件一体设计，适合科研与教学场景
本文完整设计并实验验证了一款低成本斯图尔特平台原型机，作为面向研究与教育的经济型机器人测试平台。该平台采用现成部件与3D打印、定制零件结合，通过六个线性执行器实现运动平台与固定基座间的六自由度运动。系统软件集成动态建模、数据采集与实时控制。基于反馈线性化并引入LQR补偿方案的鲁棒轨迹跟踪控制器，有效抑制非线性动力学影响，实现精确运动控制。同时，扩展卡尔曼滤波（EKF）融合惯性测量单元（IMU）与执行器编码器信号，在传感器噪声与外部扰动下提供高精度可靠的状态估计。相比以往仅关注建模或控制的孤立工作，本研究实现了从硬件到软件的全流程验证，涵盖静态与动态轨迹的仿真与实验。结果表明，平台具备优异的轨迹跟踪能力与实时状态估计性能，展现出在先进研究与教育应用中的低成本、多功能潜力。
原文摘要 · Abstract (English)
This paper presents the complete design, control, and experimental validation of a low-cost Stewart platform prototype developed as an affordable yet capable robotic testbed for research and education. The platform combines off the shelf components with 3D printed and custom fabricated parts to deliver full six degrees of freedom motions using six linear actuators connecting a moving platform to a fixed base. The system software integrates dynamic modeling, data acquisition, and real time control within a unified framework. A robust trajectory tracking controller based on feedback linearization, augmented with an LQR scheme, compensates for the platform's nonlinear dynamics to achieve precise motion control. In parallel, an Extended Kalman Filter fuses IMU and actuator encoder feedback to provide accurate and reliable state estimation under sensor noise and external disturbances. Unlike prior efforts that emphasize only isolated aspects such as modeling or control, this work delivers a complete hardware-software platform validated through both simulation and experiments on static and dynamic trajectories. Results demonstrate effective trajectory tracking and real-time state estimation, highlighting the platform's potential as a cost effective and versatile tool for advanced research and educational applications.
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