Modified Extended State Observer Control of Linear Systems

Heise, Christian D.; Schatz, Simon P.; Holzapfel, Florian

doi:10.2514/6.2016-0364

Cited by 6 publications

(1 citation statement)

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“…Thus, it is not feasible to use them for online estimation, which hinders their application for time-varying system with large uncertainties and disturbances. On the basis of model reference adaptive control (MRAC), a MESO is developed for a linear time-invariant (LTI) system according to its nominal plant model, and the deviation between the real plant model and the desired nominal one is regarded as the general disturbance signal, which can be estimated online by using observer errors only [26][27][28]. Despite its success of estimating disturbance online for LTI system, the performance would deteriorate while implementing on the nonlinear timevarying systems.…”

Section: Introductionmentioning

confidence: 99%

On validation of neural modified extended state observer through analysis and experimentation

2020

Asian Journal of Control

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In this paper, a neural modified extended state observer (ESO) (NMESO) based on the compound neural orthogonal network (CONN) is proposed to improve the online estimation of the system uncertainties and the exogenous disturbances in the uncertain system. In the developed scheme, CONN is designed as the main estimator to identify the total disturbances and facilitate the estimation of the conventional ESO. The convergence of NMESO is proved in time domain and the estimation errors are shown to be bounded. Due to the orthogonality of hidden nodes and the iterative updating mechanism of CONN, the estimation accuracy, rapidity, and real-time performance of NMESO have been greatly enhanced. Furthermore, the cooperative mechanism of the CONN estimator and the conventional ESO in the developed NMESO is also studied. Finally, the effectiveness of the proposed method and the results of analysis are verified through comparative simulation experimentation. This paper depicts a promising prospect of the NMESO in application of practical engineering.

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Section: Introductionmentioning

confidence: 99%