A novel adaptive control framework equipped with an accelerated iterative learning update mechanism is developed to handle time-varying uncertainties, based on the combination of standard adaptive control architecture and Heavy-ball optimization algorithm. The stability analysis shows that the tracking error and the estimated weight error are both bounded, and the closed-loop system is exponentially stable. The momentum term, introduced in the accelerated iterative adaptive law, makes the proposed learning-based adaptive control possess a faster convergence rate. The proposed learning-based adaptive control is applied to aircraft control to show that the proposed framework can handle time-varying uncertain parameters.

基于标准自适应控制架构和重球优化算法的组合，开发了一种新颖的自适应控制框架，该框架配备了加速的迭代学习更新机制，可以应对时变的不确定性。稳定性分析表明跟踪误差和估计的重量误差都有界，并且闭环系统是指数稳定的。在加速迭代自适应律中引入的动量项使所提出的基于学习的自适应控制具有更快的收敛速度。提出的基于学习的自适应控制应用于飞机控制，表明所提出的框架可以处理时变的不确定参数。