66991

Автор(ы): 

Автор(ов): 

3

Параметры публикации

Тип публикации: 

Статья в журнале/сборнике

Название: 

Adaptive Neural-Network-Based Control of Nonlinear Underactuated Plants: An Example of a Two-Wheeled Balancing Robot

ISBN/ISSN: 

2782-2427

DOI: 

10.25728/cs.2021.5.3

Наименование источника: 

  • Control Sciences

Обозначение и номер тома: 

№ 5

Город: 

  • Москва

Издательство: 

  • ИПУ РАН

Год издания: 

2021

Страницы: 

29-42
Аннотация
This paper proposes a new method to control nonlinear underactuated plants for eliminating unmatched parametric uncertainties. The method is based on a model reference adaptive control. The controller consists of a basic LQ one and an adaptive compensator reducing the uncertainty norm under certain assumptions. The compensator involves a multilayer neural network due to its universal approximation properties. The network is trained online. The equations to tune the compensator’s neural network parameters are derived using Lyapunov’s second method and the backpropagation algorithm. The asymptotic convergence of the tracking error (the difference between the plant’s and reference model’s outputs) to a given domain is proved. The theoretical results are validated by numerical experiments with the developed control system for the mathematical model of a balancing LEGO EV3 robot in MATLAB.

Библиографическая ссылка: 

Глущенко А.И., Петров В.А., Ласточкин К.А. Adaptive Neural-Network-Based Control of Nonlinear Underactuated Plants: An Example of a Two-Wheeled Balancing Robot // Control Sciences. 2021. № 5. С. 29-42.