scholarly journals Finite-Time Terminal Sliding Mode Tracking Control of a VTOL UAV via the Generalized NDOB

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Lu Wang ◽  
Jianhua Cheng
Keyword(s):  
Trajectory Tracking ◽  
Finite Time ◽  
Tracking Control ◽  
Sliding Mode ◽  
Convergence Result ◽  
Trajectory Tracking Control ◽  
Terminal Sliding Mode ◽  
Loop Control ◽  
Finite Time Convergence ◽  
Vtol Uav

In this paper, we propose a finite-time sliding mode trajectory tracking control methodology for the vertical takeoff and landing unmanned aerial vehicle (VTOL UAV). Firstly, a system error model of trajectory tracking task is established based on Rodrigues parameters by considering both external and internal uncertainties. According to the cascade property, the system model is divided into translational and rotational subsystems, and a hierarchical control structure is hence proposed. Then, a finite-time generalized nonlinear disturbance observer (NDOB) is proposed, based on which the finite-time convergence result of equivalent disturbance estimation can be acquired. Finally, by introducing a tan-type compensator into the traditional terminal sliding mode control (SMC), the finite-time convergence result of the closed-loop control system is acquired based on Lyapunov stability analysis. Simulation results show the effectiveness of the proposed methodology.

scholarly journals A New Model-Free Trajectory Tracking Control for Robot Manipulators

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yaoyao Wang ◽  
Kangwu Zhu ◽  
Bai Chen ◽  
Hongtao Wu
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulators ◽  
Tracking Accuracy ◽  
Trajectory Tracking Control ◽  
Terminal Sliding Mode ◽  
Loop Control ◽  
Model Free ◽  
Loop Control System

In this paper, we propose a novel model-free trajectory tracking control for robot manipulators under complex disturbances. The proposed method utilizes time delay control (TDC) as its control framework to ensure a model-free scheme and uses adaptive nonsingular terminal sliding mode (ANTSM) to obtain high control accuracy and fast dynamic response under lumped disturbance. Thanks to the application of adaptive law, the proposed method can ensure high tracking accuracy and effective suppression of noise effect simultaneously. Stability of the closed-loop control system is proved using Lyapunov method. Finally, the effectiveness and advantages of the newly proposed TDC scheme with ANTSM dynamics are verified through several comparative simulations.

Adaptive neural finite-time trajectory tracking control of hydraulic excavators

2018 ◽  
Vol 232 (7) ◽  
pp. 909-925 ◽  
Author(s):  
Yong Li ◽  
Qingfeng Wang
Keyword(s):  
Trajectory Tracking ◽  
Finite Time ◽  
Tracking Control ◽  
Sliding Mode ◽  
Tracking Error ◽  
Approximation Error ◽  
Superior Performance ◽  
Observation Error ◽  
Trajectory Tracking Control ◽  
Terminal Sliding Mode

This article is focused on the high-performance trajectory tracking control of single actuator of a hydraulic excavator. A novel adaptive neural finite-time controller without tedious offline parameter identification and the complex backstepping scheme is put forward. By employing a coordinate transform, the original system can be represented in a canonical form. Consequently, the control objective is retained by controlling the transformed system, which allows a simple controller design without using backstepping. To estimate the immeasurable states of the transformed system, a high-order sliding mode observer is employed, of which observation error is guaranteed to be bounded in finite time. To guarantee finite-time trajectory tracking performance, an adaptive neural finite-time controller based on neural network approximation and terminal sliding mode theory is synthesized. During its synthesis, an echo state network is used to approximate the lumped uncertain system functions, and it guarantees an improved approximation with online-updated output weights. Besides, to handle the lumped uncertain nonlinearities resulting from observation error and neural approximation error, a robust term is employed. The influences of the uncertain nonlinearities are restrained with a novel parameter adaption law, which estimates and updates the upper bound of the lumped uncertain nonlinearities online. With this novel controller, the finite-time trajectory tracking error convergence is proved theoretically. The superior performance and the practical applicability of the proposed method are verified by comparative simulations and experiments.

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