In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number of control computations while minimizing network resources without sacrificing the stability of the sampled data closed-loop control system. A finite sampling frequency is guaranteed for the designed triggering condition by establishing a positive lower bound on the inter-event execution time which is equivalent to the Zeno-free behavior of the system. Finally, the proposed event-based neuro-adaptive robust controller is implemented on a practical system (Q-bot 2e) to show the effectiveness of the proposed design.


Electrical and Computer Engineering

Keywords and Phrases

Adaptive sliding mode control; Artificial intelligence; Artificial neural networks; concurrent learning; differential drive mobile robot; event-triggered control; Kinematics; Mobile robots; Perturbation methods; radial basis function neural network; Trajectory; Uncertainty

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version

Final Version

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Publication Date

01 Jan 2022