Ferreira João P, Crisóstomo Manuel M, Coimbra A Paulo
Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal.
IEEE Trans Neural Netw. 2009 Dec;20(12):1885-97. doi: 10.1109/TNN.2009.2032183. Epub 2009 Oct 2.
The real-time balance control of an eight-link biped robot using a zero moment point (ZMP) dynamic model is difficult due to the processing time of the corresponding equations. To overcome this limitation, two alternative intelligent computing control techniques were compared: one based on support vector regression (SVR) and another based on a first-order Takagi-Sugeno-Kang (TSK)-type neural-fuzzy (NF) network. Both methods use the ZMP error and its variation as inputs and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR and the NF were trained based on simulation data and their performance was verified with a real biped robot. Two performance indexes are proposed to evaluate and compare the online performance of the two control methods. The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is similar to a human gait that was acquired and adapted to the robot's size. Some experiments are presented and the results show that the implemented gait combined either with the SVR controller or with the TSK NF network controller can be used to control this biped robot. The SVR and the NF controllers exhibit similar stability, but the SVR controller runs about 50 times faster.
由于相应方程的处理时间，使用零力矩点（ZMP）动态模型对八连杆双足机器人进行实时平衡控制具有难度。为克服这一限制，对两种替代智能计算控制技术进行了比较：一种基于支持向量回归（SVR），另一种基于一阶高木-菅野-康（TSK）型神经模糊（NF）网络。两种方法均将ZMP误差及其变化作为输入，输出为机器人矢状面平衡所需的躯干校正量。SVR和NF基于仿真数据进行训练，并使用实际双足机器人对其性能进行验证。提出了两个性能指标来评估和比较两种控制方法的在线性能。通过读取放置在机器人每只脚下的四个力传感器来计算ZMP。该双足机器人实现的步态类似于获取并适应机器人尺寸的人类步态。给出了一些实验，结果表明，结合SVR控制器或TSK NF网络控制器实现的步态可用于控制该双足机器人。SVR和NF控制器表现出相似的稳定性，但SVR控制器的运行速度快约50倍。
