Parameter identification in structured discrete-time uncertainties without persistency of excitation
Parameter identification in structured discrete-time uncertainties without persistency of excitation
复制标题
无持续激励的结构化离散时间不确定性中的参数识别
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
R. Ordóñez
中科院分区：
文献类型：
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作者：
Ouboti Djaneye;R. Ordóñez
并发学习以前已用于连续时间不确定性估计问题和自适应控制，以解决参数识别问题，而无需持续激励输入。特别选择的过去数据与当前数据联合组合以进行适应。在这里，我们将连续时间域中结构化不确定性的并行学习的参数识别问题结果扩展到离散时间域。与连续时间情况类似，我们表明，在离散时间中，与激励持续性相比，充分、可在线测试且限制较少的条件保证了使用并发学习时参数误差的全局指数稳定性。
Concurrent Learning has been previously used in continuous-time uncertainty estimation problems and adaptive control to solve the parameter identification problem without requiring persistently exciting inputs. Specifically selected past data are jointly combined with current data for adaptation. Here, we extend the parameter identification problem results of Concurrent Learning for structured uncertainties in the continuous-time domain to the discrete-time domain. Alike the continuous-time case, we show that, in discrete-time, a sufficient, testable on-line and less restrictive condition compared to persistency of excitation guarantees global exponential stability of the parameter error when using Concurrent Learning.