Nguyen Tuan Nghia, Nguyen Hung, Su Steven, Celler Branko
Faculty of Engineering, University of Technology, Sydney, Broadway, NSW 2007, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1005-8. doi: 10.1109/IEMBS.2011.6090233.
The paper proposes a robust online adaptive neural network control scheme for an automated treadmill system. The proposed control scheme is based on Feedback-Error Learning Approach (FELA), by using which the plant Jacobian calculation problem is avoided. Modification of the learning algorithm is proposed to solve the overtraining issue, guaranteeing to system stability and system convergence. As an adaptive neural network controller can adapt itself to deal with system uncertainties and external disturbances, this scheme is very suitable for treadmill exercise regulation when the model of the exerciser is unknown or inaccurate. In this study, exercise intensity (measured by heart rate) is regulated by simultaneously manipulating both treadmill speed and gradient in order to achieve fast tracking for which a single input multi output (SIMO) adaptive neural network controller has been designed. Real-time experiment result confirms that robust performance for nonlinear multivariable system under model uncertainties and unknown external disturbances can indeed be achieved.
本文提出了一种用于自动跑步机系统的鲁棒在线自适应神经网络控制方案。所提出的控制方案基于反馈误差学习方法（FELA），通过该方法避免了被控对象雅可比矩阵的计算问题。提出了学习算法的改进方法来解决过训练问题，确保系统的稳定性和收敛性。由于自适应神经网络控制器能够自适应地处理系统不确定性和外部干扰，该方案非常适合在锻炼者模型未知或不准确的情况下进行跑步机运动调节。在本研究中，通过同时调节跑步机速度和坡度来调节运动强度（以心率测量），以实现快速跟踪，为此设计了一个单输入多输出（SIMO）自适应神经网络控制器。实时实验结果证实，在模型不确定性和未知外部干扰下，非线性多变量系统确实能够实现鲁棒性能。
