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Integrated design of a lineareuro-adaptive controller in the presence of norm-bounded uncertainties

机译:存在范数有界不确定性的线性/神经自适应控制器的集成设计

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摘要

Adaptive neural controllers are often criticised for the lack of clear and easy design methodologies that relate adaptive neural network (NN) design parameters to performance requirements. This study proposes a methodology for the design of an integrated linear-adaptive model reference controller that guarantees component-wise boundedness of the tracking error within an a priori specified compact domain. The approach is based on the design of a robust invariant ellipsoidal set where both the NN reconstruction error and the neuro-adaptive control are considered as bounded persistent uncertainties. We show that all the performance and control requirements for the closed-loop system can be expressed as linear matrix inequality constraints. This brings the advantage that feasibility and optimal design parameters can be effectively computed while solving a linear optimisation problem. An advantage of the method is that it allows a systematic and quantitative evaluation of the interplay between the design parameters and their impact on the requirements. This produces an integrated linear/ neuro-adaptive performance-oriented design methodology. A numerical example is used to illustrate the approach.
机译:经常有人批评自适应神经控制器缺乏将自适应神经网络(NN)设计参数与性能要求相关联的清晰易用的设计方法。这项研究提出了一种用于设计集成线性自适应模型参考控制器的方法,该方法可确保在先验指定的紧凑域内跟踪误差的分量化有界。该方法基于稳健的不变椭圆集的设计,其中NN重建误差和神经自适应控制都被视为有界的持久不确定性。我们表明,闭环系统的所有性能和控制要求都可以表示为线性矩阵不等式约束。这带来的优点是,在解决线性优化问题的同时,可以有效地计算可行性和最佳设计参数。该方法的优点在于,它可以对设计参数之间的相互作用及其对需求的影响进行系统且定量的评估。这产生了集成的线性/神经自适应性能导向设计方法。数值示例说明了该方法。

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