Adaptive integral LOS path following for an unmanned airship with uncertainties based on robust RBFNN backstepping. (November 2016)

• Record Type: Journal Article
• Authors: Zheng, Zewei; Zou, Yao
• Is Part Of: ISA transactions. Volume 65(2016:Nov.)
• Page Start: 210
• Page End: 219
• Publication Date: 2016-11
• Subjects: Unmanned airship -- Path following control -- Integral LOS -- Backstepping -- Robust RBFNN; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2016.09.008
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper investigates the path following control problem for an unmanned airship in the presence of unknown wind and uncertainties. The backstepping technique augmented by a robust adaptive radial basis function neural network (RBFNN) is employed as the main control framework. Based on the horizontal dynamic model of the airship, an improved adaptive integral line-of-sight (LOS) guidance law is first proposed, which suits any parametric paths. The guidance law calculates the desired yaw angle and estimates the wind. Then the controller is extended to cope with the airship yaw tracking and velocity control by resorting to the augmented backstepping technique. The uncertainties of the dynamics are compensated by using the robust RBFNNs. Each robust RBFNN utilizes an n th-order smooth switching function to combine a conventional RBFNN with a robust control. The conventional RBFNN dominates in the neural active region, while the robust control retrieves the transient outside the active region, so that the stability range can be widened. Stability analysis shows that the controlled closed-loop system is globally uniformly ultimately bounded. Simulations are provided to validate the effectiveness of the proposed control approach. Abstract : Highlights: We present a nonlinear path following control method for an unmanned airship with uncertainties. Adaptive integral LOS guidance method is improved in a uniform form for all kinds of paths. Robust RBFNNs compensate the unmodeled dynamics and the tracking performance is improved. Controlled closed-loop system is proved to be globally uniformly ultimately bounded. Simulations show that the method has an excellent performance.