Adaptive prescribed performance control of nonlinear asymmetric input saturated systems with application to AUVs. Issue 16 (October 2021)

• Record Type: Journal Article
• Authors: Wang, Chenggang; Zhu, Shanying; Yu, Wenbin; Song, Lei; Guan, Xinping
• Is Part Of: Journal of the Franklin Institute. Volume 358:Issue 16(2021)
• Page Start: 8330
• Page End: 8355
• Publication Date: 2021-10
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2021.08.026
• Languages: English
• ISSNs: 0016-0032

Abstract

Highlights: This paper focuses on the prescribed performance control of nonlinear asymmetric input saturated systems in strict-feedback form. The prescribed transient and steady-state performances of the tracking errors can be guaranteed. The Gaussian error functions are used to model the asymmetric input saturation and the system uncertainties are solved by the radial basis function neural network. The overall function approximation errors and the external disturbances are tackled by the adaptive control laws. All the signals in the closed-loop systems are uniformly ultimately bounded. Abstract: In this paper, the adaptive prescribed performance tracking control of nonlinear asymmetric input saturated systems in strict-feedback form is addressed under the consideration of model uncertainties and external disturbances. A radial basis function neural network (RBF-NN) is utilized to handle the model uncertainties. By prescribed performance functions, the transient performance of the system can be guaranteed. The continuous Gaussian error function is represented as an approximation of asymmetric saturation nonlinearity such that the backstepping technique can be leveraged in the control design. Based on the Lyapunov synthesis, residual function approximation inaccuracies and external disturbances are compensated by constructed adaptive control laws. As a consequence, all the signals in the closed-loop system are uniformly ultimately bounded and the tracking errors bounded by prescribed functions converge to a small neighbourhood of zero. The proposed method is applied to the autonomous underwater vehicles (AUVs) with extensive simulation results demonstrating the effectiveness of the proposed method.