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Free Model Task Space Controller Based on Adaptive Gain for Robot Manipulator Using Jacobian Estimation

机译:基于雅可比估计的机器人自适应自由模型任务空间控制器

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A Free Model Task Space Controller (FMTSC) is presented in this paper for an omnidirectional mobile manipulator. However, it is well known the difficulty to know the precsise details of the robotic system and commonly limited for the accuracy of the kinematic and dynamic model, the model based methods are not sufficient, so far. Therefore the use of available information like joints velocities and robot tip velocity allow to estimate the robot Jacobian matrix information, without any requirement of mathematical model. An adaptive Kalman filter is computed to estimate Jacobian to deal with the adaptive robot control in the task space. The control law is developed with the Jacobian estimate for Strong Tracking Kalman Filter (STKF) algortihm. The control algorithm is intended for nonlinear discrete-time system (robot) which provides adpative control gain for taks space controller, designed by Fuzzy Rules Emulated Network Adaptive Gain (FRENAG). The performance of the controller is validated with Kuka youBot mobile manipulator plataform experiments.
机译:本文提出了一种用于全向移动机械手的自由模型任务空间控制器(FMTSC)。然而,众所周知,很难知道机器人系统的精确细节,并且通常由于运动学和动态模型的准确性而受到限制,到目前为止,基于模型的方法还不够。因此,使用诸如关节速度和机器人尖端速度之类的可用信息可以估计机器人雅可比矩阵信息,而无需任何数学模型的要求。计算自适应卡尔曼滤波器以估计雅可比矩阵,以处理任务空间中的自适应机器人控制。控制定律是根据强跟踪卡尔曼滤波器(STKF)算法的雅可比估计来开发的。该控制算法适用于由模糊规则仿真网络自适应增益(FRENAG)设计的非线性离散时间系统(机器人),该系统为Taks空间控制器提供自适应控制增益。控制器的性能已通过Kuka youBot移动操纵器平台实验进行了验证。

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