基于步态数据的下肢康复机器人控制设计

为了使下肢康复机器人的辅助康复训练更加符合人体运动特性,为下肢运动功能障碍的患者提供更加安全、有效的康复训练,提出一种基于人体步态数据和径向基函数(RBF)神经网络的下肢康复机器人控制策略。首先,以三维动作捕捉系统获取的健康人体步态数据为系统的期望输入;其次,通过RBF神经网络自适应控制器产生驱动机器人关节运动的力矩,使机器人的运动轨迹跟踪期望轨迹;然后,采用基于前馈控制的校正模型对机器人的输入力矩进行补偿校正,达到实时校正的目的,从而实现更好的跟踪效果;最后,通过基于实验数据的仿真结果,验证了该方法的可行性和有效性。

Design of highly efficient elliptic curve crypto-processor with two multiplications over GF(2^(163))

In this article, a parallel hardware processor is presented to compute elliptic curve scalar multiplication in polynomial basis representation. The processor is applicable to the operations of scalar multiplication by using a modular arithmetic logic unit （MALU）. The MALU consists of two multiplications, one addition, and one squaring. The two multiplications and the addition or squaring can be computed in parallel. The whole computations of scalar multiplication over GF（2^163） can be performed in 3 064 cycles. The simulation results based on Xilinx Virtex2 XC2V6000 FPGAs show that the proposed design can compute random GF（2^163） elliptic curve scalar multiplication operations in 31.17 μs, and the resource occupies 3 994 registers and 15 527 LUTs, which indicates that the crypto-processor is suitable for high-performance application.