Composite Adaptive Backstepping Control considering Computational Complexity and Relaxation of Persistent Excitation

Jeon, Byoung-Ju; Shin, Hyo‐Sang; Tsourdos, Antonios

doi:10.1016/j.ifacol.2020.12.2065

Cited by 1 publication

(3 citation statements)

References 17 publications

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“…This section describes the design process of the composite learning adaptive law. The prediction error is obtained according to the regressor filtering scheme described in [10, 13, 14], which can avoid the usage of the derivative of state variable

{x}_n

. In the following, we will comprehensively consider prediction error and tracking error to design the adaptive law.…”

Section: Resultsmentioning

confidence: 99%

“…Composite adaptive control has achieved rapid development and wide application in recent decades; see earlier studies [10][11][12][13][14][15][16][17] and so on. In previous research [11,12], a technique combining direct and indirect adaptation is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In another work [16,17], the composite adaptive technique is created and applied to the control of robot manipulators. In earlier studies [10,13,14], the regressor filtering scheme is applied to the formulation of composite adaptive laws, therefore expanding the composite adaptive approaches in other work [11,12,16,17]. Compared with the direct adaptive approach, the composite adaptive control introduces a prediction error term in the adaptive law based on the tracking error term.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive neural control with fast approximation for uncertain nonlinear systems: A novel composite learning approach

Xie

Wang

Dai

et al. 2023

Asian Journal of Control

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In existing adaptive neural control approaches, only when the regressor satisfies the persistent excitation (PE) or interval excitation (IE) conditions, the constant optimal weights of neural network (NN) can be identified, which can be used to establish uncertainties in nonlinear systems. This paper proposes a novel composite learning approach based on adaptive neural control. The focus of this approach is to make the NN approximate uncertainties in nonlinear systems quickly and accurately without identifying the constant optimal weights of the NN. Hence, the regressor does not need to satisfy the PE or IE conditions. In this paper, regressor filtering scheme is adopted to generate prediction error, and then the prediction error and tracking error simultaneously drive the update of NN weights. Under the framework of Lyapulov theory, the proposed composite learning approach can ensure that approximation error of the uncertainty and tracking error of the system states converge to an arbitrarily small neighborhood of zero exponentially. The simulation results verify the effectiveness and advantages of the proposed approach in terms of fast approximation.

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