Online-learning control with weakened saturation response to attitude tracking: A variable learning intensity approach
Online-learning control with weakened saturation response to attitude tracking: A variable learning intensity approach
复制标题
对态度跟踪的饱和响应减弱的在线学习控制：一种可变学习强度方法
DOI：
10.1016/j.ast.2021.106981
复制
发表时间：
2021
影响因子：
5.6
通讯作者：
He Wei
中科院分区：
文献类型：
--
作者：
Zhang Chengxi;Ahn Choon Ki;Wu Jin;He Wei
本文研究了一种变学习强度在线学习控制（OLC）方案的姿态跟踪控制问题。所提出的VLI-OLC方案的独特之处在于，它通过在线学习先前的控制信息来实现控制性能的增强。实现是由一个简单的代数方程，实现体面的控制鲁棒性，同时避免了复杂的控制设计和节省计算资源。通过引入VLI方法，OLC在执行期间由广泛的系统错误引起的饱和响应被显著削弱。可以保证与以前算法的兼容性。应用实例表明，该控制器在保证控制性能的同时，也降低了饱和度。
This brief investigates the problem of attitude tracking control using a variable learning intensity (VLI) online-learning control (OLC) scheme. The unique specialty of the proposed VLI-OLC scheme is that it achieves control performance enhancement via learning the previous control information online. The implementation is performed by a simple algebraic equation, which achieves decent control robustness while avoiding a complex control design and saving computational resources. The OLC's saturation response caused by the extensive system error during execution is noticeably weakened by introducing a VLI approach. Compatibility with previous algorithms can be guaranteed. The application example shows that control performance and saturation reduction are guaranteed concurrently.