论文标题
连续的Lyapunov控制器和混乱的非线性系统优化使用深度机器学习
Continuous Lyapunov Controller and Chaotic Non-linear System Optimization using Deep Machine Learning
论文作者
论文摘要
引入意外的系统干扰和新的系统动态不允许保证连续的系统稳定性。在这项研究中，我们提出了一种新的方法，用于检测非线性高度混沌系统的早期失败指标，并因此预测了使用深机学习回归模型来抵消这种不稳定的最佳参数校准。该方法不断监视系统和控制器信号。根据一组旨在维持系统稳定性的条件，触发系统和控制器参数的重新校准，而无需损害系统速度，预期的结果或所需的处理能力。深神经模型预测了最好抵消预期系统稳定性的参数值。为了证明所提出的方法的有效性，它应用于行李范围振荡器的非线性复合物组合。该方法还在不同方案下测试了系统和控制器参数最初是错误选择的，或者在运行或在运行时引入新的系统动态时，系统参数会在衡量效率和反应时间时引入。
The introduction of unexpected system disturbances and new system dynamics does not allow guaranteed continuous system stability. In this research we present a novel approach for detecting early failure indicators of non-linear highly chaotic system and accordingly predict the best parameter calibrations to offset such instability using deep machine learning regression model. The approach proposed continuously monitors the system and controller signals. The Re-calibration of the system and controller parameters is triggered according to a set of conditions designed to maintain system stability without compromise to the system speed, intended outcome or required processing power. The deep neural model predicts the parameter values that would best counteract the expected system in-stability. To demonstrate the effectiveness of the proposed approach, it is applied to the non-linear complex combination of Duffing Van der pol oscillators. The approach is also tested under different scenarios the system and controller parameters are initially chosen incorrectly or the system parameters are changed while running or new system dynamics are introduced while running to measure effectiveness and reaction time.
