High-gain-observer based adaptive output-feedback formation control for underactuated unmanned surface vessels with input saturation and uncertainties
by Meijiao Zhao; Huayan Pu; Yueying Wang; Jun Luo; Shaorong Xie; Yan Peng
International Journal of Vehicle Design (IJVD), Vol. 84, No. 1/2/3/4, 2020

Abstract: An adaptive output feedback formation control strategy based on high gain observer has been developed to solve the problem of underactuated surface vessels formation control with uncertain dynamics, ocean environment disturbance and input saturation. In this strategy, a high gain observer that only depends on position information is used to estimate the unmeasurable velocity, and in order to solve the ‘complex explosion problem’ in the conventional backstepping control algorithm, a first-order low-pass filter is adopted to obtain the derivative of the virtual control signal. In addition, adaptive neural networks (NNs) and minimum learning parameters (MLPs) algorithm are used to approximate environmental disturbances and uncertain dynamics, while reducing online update parameters. Stability analysis proved that all signals in closed-loop are uniformly ultimately bounded and the formation tracking errors are arbitrarily small. Simulation results demonstrate the effectiveness of the controller.

Online publication date: Fri, 25-Jun-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Design (IJVD):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com