Fast and stable adaptation is necessary to achieve stringent tracking performance specifications in the face of large system uncertainties. This work develops a novel fast adaption architecture based on a high‐order optimization idea, where an approximated filter of weight is applied to smoothen and stabilize the estimation process. Larger learning rate can be selected to achieve fast adaption in that high‐frequency uncertainties are attenuated. Moreover, composite learning combined with filtering regression and experience replay technique is utilized to further smoothen and accelerate the parameter estimation process. Given a nonlinear plant with multi‐input multi‐output strict‐feedback structure, the proposed adaptive control is integrated into the backstepping framework. The uniformly bounded property of the tracking errors and the approximation errors is proven by Lyapunov theory. The superiority of the proposed method is demonstrated by comparative simulations.

中文翻译：

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通过高阶优化实现快速稳定的复合学习

面对较大的系统不确定性，快速而稳定的自适应对于实现严格的跟踪性能指标是必要的。这项工作基于高阶优化思想开发了一种新颖的快速自适应体系结构，其中采用了近似的权重滤波器来平滑和稳定估计过程。可以选择更大的学习率来实现快速适应，因为高频不确定性会降低。此外，结合滤波回归和经验重播技术的复合学习被用来进一步平滑和加速参数估计过程。给定具有多输入多输出严格反馈结构的非线性设备，将所提出的自适应控制集成到反推框架中。Lyapunov理论证明了跟踪误差和逼近误差的一致有界性质。通过比较仿真证明了该方法的优越性。