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摘要翻译：
针对一类具有不确定动态的二阶非线性时不变系统，提出了一种基于并行学习的自适应观测器。所开发的技术导致一致最终有界的状态和参数估计误差。与传统的自适应控制方法中参数收敛需要持续激励不同，该方法只需要在有限的时间间隔内激励即可实现参数收敛。给出了无噪声和噪声环境下的仿真结果，验证了设计的正确性。
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英文标题：
《Online Simultaneous State and Parameter Estimation》
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作者：
Ryan Self and Moad Abudia and S. M. Nahid Mahmud, and Rushikesh
Kamalapurkar
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最新提交年份：
2020
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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：Systems and Control 系统与控制
分类描述：This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Computer Science 计算机科学
二级分类：Systems and Control 系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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英文摘要：
In this paper, a concurrent learning based adaptive observer is developed for a class of second-order nonlinear time-invariant systems with uncertain dynamics. The developed technique results in uniformly ultimately bounded state and parameter estimation errors. As opposed to persistent excitation which is required for parameter convergence in traditional adaptive control methods, the developed technique only requires excitation over a finite time interval to achieve parameter convergence. Simulation results in both noise-free and noisy environments are presented to validate the design.
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PDF链接：
https://arxiv.org/pdf/1703.07068
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