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On model reference adaptive control for uncertain dynamical systems with unmodeled dynamics

机译:具有未建模动力学的不确定动力学系统的模型参考自适应控制

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On model reference adaptive control for uncertain dynamical systems, it is well know that there exists a fundamental stability limit, where the closed-loop dynamical system subject to this class of control laws remains stable either if there does not exist significant unmodeled dynamics or the effect of system uncertainties is negligible. Specifically, this implies that model reference adaptive controllers cannot tolerate large system uncertainties even when unmodeled dynamics satisfy a set of conditions. Motivated from this standpoint, this paper proposes a model reference adaptive control approach to relax this fundamental stability limit, where an adaptive control signal is augmented with an adaptive robustifying term. The key feature of our framework allows the closed-loop dynamical system to remain stable in the presence of large system uncertainties when the unmodeled dynamics satisfy a set of conditions. An illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.
机译:在不确定动力系统的模型参考自适应控制中,众所周知存在一个基本的稳定性极限,如果不存在显着的未建模动力或影响,则遵循此类控制律的闭环动力系统将保持稳定。系统不确定性的影响可以忽略不计。具体而言,这意味着即使未建模的动力学满足一组条件,模型参考自适应控制器也无法承受较大的系统不确定性。从这一观点出发,本文提出了一种模型参考自适应控制方法,以放松该基本稳定性极限,其中以自适应鲁棒性项来增强自适应控制信号。当未建模的动力学满足一组条件时,我们框架的关键特征允许闭环动力学系统在存在较大系统不确定性的情况下保持稳定。提供了一个说明性的数值示例,以证明所提出方法的有效性。

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