Control of smart exercise machines. II. Self-optimizing control
Control of smart exercise machines. II. Self-optimizing control
复制标题
智能健身器材的控制。
DOI：
10.1109/3516.653049
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发表时间：
1997
影响因子：
6.4
通讯作者：
R. Horowitz
中科院分区：
文献类型：
--
作者：
Perry Y. Li;R. Horowitz
对于点。我看到了同上。第237-47页(1997)。介绍了一种运动机智能控制器的设计。控制目标是使用户以使与用户的机械功率相关的标准最优化的方式进行锻炼。最优的运动策略是由个体使用者未知的生物力学行为决定的，这种生物力学行为被称为希尔表面。因此，控制方案必须同时：1)识别用户的生物力学行为；2)优化控制器；3)将系统稳定到估计的最优状态。当使用者的生物力学行为未知时，我们解决了最优运动策略的确定和最终执行的自我优化问题。这是通过自适应控制器和参考发电机的组合来实现的。后者在训练策略和估计的最优策略之间切换期望的练习策略。根据所选择的切换方案，结果表明，渐近地，用户将以概率1执行最优操作或接近该最优操作。整个系统的实验结果验证了设计的有效性。
For pt. I see ibid. p. 237-47 (1997). Concerns the design of an intelligent controller for a class of exercise machines. The control objective is to cause the user to exercise in a manner that optimizes a criterion related to the user's mechanical power. The optimal exercise strategy is determined by an a priori unknown biomechanical behavior, called the Hill surface, of the individual user. Consequently, the control scheme must simultaneously: 1) identify the user's biomechanical behavior; 2) optimize the controller; and 3) stabilize the system to the estimated optimal states. We address the self-optimization problem in which both the determination and the eventual execution of the optimal exercise strategy are accomplished, when the user's biomechanical behavior is unknown. This is achieved by a combination of an adaptive controller and a reference generator. The latter switches the desired exercise strategy between a training strategy and the estimated optimal strategy. Depending on the switching scheme chosen, it is shown that, asymptotically, the user will either execute the optimal exercise with probability one or operate close to it. Experimental results of the overall system verify the efficacy of the design.