Neural network gain-scheduling sliding mode control for three-dimensional trajectory tracking of robotic airships

Abstract

This article proposed a novel control approach for trajectory tracking of robotic airships. First, the nonlinear dynamics model of a robotic airship is derived and formulated. Second, a sliding mode controller is designed to track the commanded trajectory for its robustness against parametric variations and external disturbances. To address the chattering problem results from the sliding mode controller, a radial basis function neural network is adopted to construct the neural network self-gain-scheduling sliding mode controller in which the control gains are scheduled synchronously with the sliding surface via radial basis function neural network according to the learning algorithm, with switching sliding surface and its differential as radial basis function neural network inputs and control gains as radial basis function neural network outputs. In addition, the stability and convergence of the closed-loop controller are proven using the Lyapunov stability theorem. Finally, the effectiveness and robustness of the proposed controller are demonstrated via numerical simulations. Contrasting simulation results indicate that the neural network self-gain-scheduling sliding mode controller attenuates the chattering effectively and has better performance against the sliding mode controller.

A gain-scheduling control strategy and short-term path optimization with genetic algorithm for autonomous navigation of a sailboat robot

“…As explained above, this changing of parameters is attractive mainly because our system works in a highly nonlinear environment that needs a different strategy at each situation of wind. So, following the success of gainscheduling control strategies in other types of mobile robots [11][12][13][14][25][26][27][28][29] , we propose to use such approach in this work for the development of the sailboat control system. The proposed method uses experimental data obtained in simulation to decide which controller parameters satisfy specific constraints.…”

Modeling and path-following control of a vector-driven stratospheric satellite

“…Sliding mode control is one of the most important approaches to handling systems with large uncertainties, nonlinearities, and bounded external disturbances (Zhihong et al, 1994). It has been used in the development of stratospheric aircraft control system in recent years (Yang et al, 2014;Yang and Yan, 2015;Yang et al, 2013Yang et al, , 2012. Chen et al (2013) presented a composite control structure for the stratospheric airship to realize accurate position control and to decrease energy consumption, which took the control allocation problem into consideration.…”

Neuroadaptive output-feedback trajectory tracking control for a stratospheric airship with prescribed performance

“…As a novel aircraft, the stratospheric airship exhibits enormous potential in real-time surveillance, telecommunication, scientific experiments, and other fields due to its unique capability (1,11,14) , implying that high-precision trajectory tracking is necessary to complete diverse tasks. However, natural nonlinearity, the high-coupled property of the airship and external disturbances hinder reaching and accurately tracking a time-varying trajectory and make the control scheme design process quite complicated (6,10,18,20,27,39,42) . Additionally, for an underactuated airship, the trajectory tracking control design is more daunting due to the actuators of the airship not providing independent forces or moments in all degrees of freedom.…”