基于扩展卡尔曼滤波器的液压驱动器状态估计

本文的液压驱动器属于不可观测的非线性系统，普通的状态观测器难以胜任系统的状态估计，故采用扩展卡尔曼滤波器对液压缸活塞腔和杆腔压力进行估计.建立了液压系统的4阶非线性状态空间模型，用高斯-牛顿法拟合模型参数，用关节角度信号通过扩展卡尔曼滤波器估计液压驱动器状态.仿真结果表明该方法可以精确估计液压缸压力，实验结果显示该方法估计的关节扭矩接近测量值；从而验证了扩展卡尔曼滤波器估计液压系统状态的有效性，为基于状态的故障诊断和液压驱动器控制提供了一定的借鉴.

State Estimation of Robot Hydraulic Actuator Based on Extended Kalman Filter

The hydraulic actuator in this work is an un observable nonlinear system， for which a normal observer is infeasible. The extended Kalman filter (EKF) was thus adopted to estimate pressures in piston and rod chambers of the cylinder. The 4th order nonlinear state space model was developed and its parameters were obtained by the Gauss Newton method. The state of the hydraulic actuator was estimated using joint angle based on the EKF. The simulation results indicate that the proposed method can estimate the cylinder pressures accurately. The experimental results show that the estimated joint torque is close to the measured one， which verifies the validity of estimating hydraulic states by the EKF. A reference for state based fault detection and control of a hydraulic actuator is thus provided.