卡尔曼状态观测器在机器人力控制中的应用

为解决机器人与环境间接触力控制性能受机器人本身动力学参数时变、接触环境变化以及力测量信号干扰噪声影响的问题，设计了基于卡尔曼状态观测器的机器人力控制方案，采用递归最小二乘法来实时估计环境刚度矩阵，引入系统状态误差反馈和卡尔曼滤波算法实现机器人力控制系统对外界干扰及系统模型误差的补偿．实验结果表明：在环境刚度未知的条件下，采用该控制方法可使机器人末端与环境间接触力的误差控制在10％左右．

Application of Kalman active observers in robot force control

Performance of robot force control would degrade under the condition of time-varying robot dynamic parameters,contacted environment uncertainties and the serious noise interference of force measurement signals.Thus,the robot force control framework was introduced based on Kalman active observers.The recursive least squares algorithm was used to estimate the environment stiffness matrix on-line.The proposed robot force control method compensated the disturbances from external noises and system model uncertainties through the system state error feedback principle and the Kalman filtering algorithm.The experiment results show that the error in actual contact force between the robot′s end-effector and environments with unknown stiffness coefficients remains approx 10% by the proposed robot force control method.