Improved adaptive dynamic surface control for uncertain nonlinear systems with input saturation

Hui Gao; Jing Ma

doi:10.1117/12.3015033

4 March 2024 Improved adaptive dynamic surface control for uncertain nonlinear systems with input saturation

Hui Gao, Jing Ma

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129815U (2024) https://doi.org/10.1117/12.3015033
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

This paper discusses an adaptive dynamic surface control method for uncertain strict feedback nonlinear systems with input saturation and external disturbance. The radial basis neural network (RBFNN) is used to approximate the unknown function and the hyperbolic tangent function is used to solve the input saturation problem. Meanwhile, the improved dynamic surface control technology reduces the influence of first-order filter on the state error, and the control scheme has excellent tracking performance and stability. Finally, the final bounded convergence of all closed-loop signals is proved, and the superiority of the proposed control scheme is verified by simulation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hui Gao and Jing Ma "Improved adaptive dynamic surface control for uncertain nonlinear systems with input saturation", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129815U (4 March 2024); https://doi.org/10.1117/12.3015033

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