This paper proposes a new fractional-order model reference adaptive control (FOMRAC) framework for a fractional-order multivariable system with parameter uncertainty. The designed FOMRAC scheme depends on a fractional-order nonlinear scalar update law. Specifically, the scalar update law does not change as the input–output dimension changes. The main advantage of the proposed adaptive controller is that only one parameter online update is needed such that the computational burden in the existing FOMRAC can be relieved. Furthermore, we show that all signals in this adaptive scheme are bounded and the mean value of the squared norm of the error converges to zero. Two illustrative numerical examples are presented to demonstrate the efficiency of the proposed control scheme.
This paper puts forward a model reference adaptive control (MRAC) framework with a compensator based on a novel scalar update law for the dynamical systems with matched uncertainty. The scalar update law is given by an algebraic expression of basis functions and system errors. The main advantage of the proposed MRAC framework is that only a scalar function requires updating online such that the architecture is simple and the computational burden in the existing results can be relieved. Furthermore, the asymptotic stability of the system error dynamics is guaranteed by the proposed controller. Two numerical examples are given to demonstrate the effectiveness of the proposed MRAC framework.
This paper puts forward a novel output feedback model reference adaptive control (MRAC) scheme for solving an adaptive output tracking problem. The proposed control scheme only needs a scalar function to be updated online, which decreases the system adaptation complexity, compared to the existing MRAC schemes. Furthermore, the closed-loop signal boundedness and asymptotic output tracking are guaranteed with the proposed MRAC scheme. A simulation study is carried out to verify the effectiveness of the established approach.
In this article, a new fractional-order model reference adaptive control (FOMRAC) framework based on a nonlinear scalar update law is proposed for a fractional-order multivariable system with parameter uncertainty. The scalar update law does not change as the input-output dimension changes. Compared with the existing results, the main advantage of the proposed adaptive controller is that only one parameter online updating is needed such that the control scheme is structurally simple and computationally inexpensive. We further show that all signals in this adaptive scheme are bounded and that the mean value of the squared norm of the error converges to zero. Two illustrative numerical examples are presented to test the efficiency of the proposed architecture
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