摘要翻译：
在本论文的第一部分中，我们在一个物理单足跳跃机器人平台上对基于力学的数学模型的预测性能进行了实验验证。我们扩展了最近提出的一种近似解析解，用于实际机器人系统的有损弹簧-质量模型，并进行了参数系统辨识，以仔细识别所提出的模型中的系统参数。我们也给出了对我们的单腿跳跃机器人数据的近似解析解的预测性能的评估。第二部分考虑利用输入输出数据估计腿部运动的状态空间模型。为了实现这一目标，我们首先提出了一种状态空间辨识方法，在全状态测量假设下估计混合LTP系统的时间周期状态和输入矩阵。然后，我们解除这个假设，继续子空间辨识方法估计LTP状态空间实现未知稳定LTP系统。我们利用双线性（Tustin）变换和频域提升方法来推广我们对不同LTP系统模型的求解。我们的结果为腿运动状态空间模型的辨识提供了依据。
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英文标题：
《Identification of Legged Locomotion via Model-Based and Data-Driven
  Approaches》
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作者：
Ismail Uyanik
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In the first part of this thesis, we present our efforts on experimental validation of the predictive performance of mechanics-based mathematical models on a physical one-legged hopping robot platform. We extend upon a recently proposed approximate analytical solution developed for the lossy spring--mass models for a real robotic system and perform a parametric system identification to carefully identify the system parameters in the proposed model. We also present our assessments on the predictive performance of the proposed approximate analytical solution on our one-legged hopping robot data.   The second part considers estimating state space models of legged locomotion using input--output data. To accomplish this, we first propose a state space identification method to estimate time periodic state and input matrices of a hybrid LTP system under full state measurement assumption. We then release this assumption and proceed with subspace identification methods to estimate LTP state space realizations for unknown stable LTP systems. We utilize bilinear (Tustin) transformation and frequency domain lifting methods to generalize our solutions to different LTP system models. Our results provide a basis towards identification of state space models for legged locomotion.
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PDF链接：
https://arxiv.org/pdf/1710.04275