具有参数突变的MRAC指数收敛的复位回归滤波
Regression Filtration with Resetting to Provide Exponential Convergence of MRAC for Plants with Jump Change of Unknown Parameters
摘要：
本文提出了一种新的方法，使复合自适应控制系统的参数和跟踪误差都指数收敛，而不需要持续激励（PE）。相反，派生的复合自适应法律确保上述性质下严格较弱的有限激励（FE）条件。与已知的解决方案不同，除了PE要求松弛之外，它还在设备不确定性参数的跳变下提供了更好的瞬态响应。为了得到这样的自适应律，提出了一种新的方案，不确定性过滤与复位，它提供了所需的性能的控制系统。所提出的自适应律的所有性能的严格证明。这种法律相比，已知的复合材料，这也放宽了PE的要求，使用机翼摇滚问题进行数值实验。所得结果充分支持了理论分析，并证明了所提出的方法的优点。
英文摘要：
This paper proposes a new method to provide the exponential convergence of both the parameter and tracking errors of the composite adaptive control system without the persistent excitation (PE) requirement. Instead, the derived composite adaptive law ensures the above-mentioned properties under the strictly weaker finite excitation (FE) condition. Unlike known solutions, in addition to the PE requirement relaxation, it provides better transient response under jump change of the plant uncertainty parameters. To derive such an adaptive law, a novel scheme of uncertainty filtration with resetting is proposed, which provides the required properties of the control system. A rigorous proof of all mentioned properties of the developed adaptive law is presented. Such law is compared with the known composite ones, which also relax the PE requirement, using the wing-rock problem to conduct numerical experiments. The obtained results fully support the theoretical analysis and demonstrate the advantages of the proposed method.
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