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High-Gain Observer-Based Neural Adaptive Feedback Linearizing Control of a Team of Wheeled Mobile Robots

Published online by Cambridge University Press:  11 April 2019

Neda Sarrafan  and
Khoshnam Shojaei Open the ORCID record for Khoshnam Shojaei [Opens in a new window]
Neda Sarrafan
Affiliation:
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. E-mail: [email protected]
Khoshnam Shojaei*
Affiliation:
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. E-mail: [email protected] Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper addresses the neural network (NN) output feedback formation tracking control of nonholonomic wheeled mobile robots (WMRs) with limited voltage input. A desired formation is achieved based on the leader–follower strategy utilizing hyperbolic tangent saturation functions to reduce the risk of actuator saturation. The controller is developed by incorporating the high-gain observer and radial basis function (RBF) NNs using the inverse dynamics control technique. The high-gain observer is introduced to estimate velocities of the followers. The RBF NN preserves the robustness of the proposed controller against uncertain nonlinearities. The adaptive laws are also combined by a robust control term to estimate the weights of RBF NN, approximation errors, and bounds of unknown time-variant environmental disturbances. A Lyapunov-based stability analysis proves that all signals of the closed-loop system are bounded, and tracking errors are uniformly ultimately bounded. Finally, some simulations are carried out to show the effectiveness of the proposed controller for a number of WMRs.

Keywords

Actuator saturationAdaptive robust controlHigh-gain observerLeader– follower formationRadial basis function neural networkWheeled mobile robots
Type
Articles
Information
Robotica , Volume 38 , Issue 1 , January 2020 , pp. 69 - 87
Copyright
© Cambridge University Press 2019 

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