Event-triggered-based adaptive command-filtered asymptotic tracking control for flexible robotic manipulators

2021 ◽  
Author(s):  
Tingting Cheng ◽  
Ben Niu ◽  
Guangju Zhang ◽  
Zhenhua Wang ◽  
Peiyong Duan
Keyword(s):  
Tracking Control ◽  
Robotic Manipulators ◽  
Asymptotic Tracking ◽  
Event Triggered

scholarly journals Event-Triggered Adaptive Asymptotic Tracking Control of Flexible Robotic Manipulators: A Command Filtered Backstepping Method

2021 ◽  
Author(s):  
Tingting Cheng ◽  
Ben Niu ◽  
Guangju Zhang ◽  
Zhenhua Wang ◽  
Peiyong Duan
Keyword(s):  
Tracking Control ◽  
Adaptive Design ◽  
Robotic Manipulators ◽  
Control Input ◽  
Backstepping Method ◽  
Design Algorithm ◽  
Design Scheme ◽  
Barbalat’S Lemma ◽  
Asymptotic Tracking ◽  
Event Triggered

Abstract This paper formulates an event-triggered adaptive asymptotic tracking control scheme for flexible robotic manipulators via command filtered backstepping method. Firstly, in the proposed design algorithm, the unknown nonlinear functions are firstly approximated by using intelligent estimation technique. Then, the “explosion of complexity” problem existing in the traditional backstepping procedure is solved by cleverly applying the command filtered backstepping method. In addition, an event-triggered mechanism is adopted so that the control input is updated irregularly following the occurrence of an event. The advantages of the proposed adaptive design scheme are as follows: (i) the Barbalat’s Lemma is used to asymptotically drive the tracking error to zero; (ii) all the variables in the closed-loop system are bounded; (iii) the utilized event-triggered mechanism reduces the transmission frequency of computer and saves computer resources. Finally, the simulation results of the robotic system are given to illustrate the effectiveness of our design scheme.

Event-triggered adaptive asymptotic tracking control of uncertain nonlinear systems with unknown dead-zone constraints

2020 ◽  
Vol 386 ◽  
pp. 125528
Author(s):  
Li-Bing Wu ◽  
Ju H. Park ◽  
Xiang-Peng Xie ◽  
Ya-Juan Liu ◽  
Zhi-Chun Yang
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Dead Zone ◽  
Uncertain Nonlinear Systems ◽  
Asymptotic Tracking ◽  
Event Triggered

scholarly journals Neural Network-Based Event-triggered Adaptive Asymptotic Tracking Control for Switched Nonlinear Systems

2021 ◽  
Author(s):  
Baomin Li ◽  
Jianwei Xia ◽  
Wei Sun ◽  
Hao Shen ◽  
Huasheng Zhang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
Tracking Error ◽  
Network Control ◽  
Switched Nonlinear Systems ◽  
Unknown Control Directions ◽  
Asymptotic Tracking ◽  
Unknown Control ◽  
Event Triggered

Abstract This paper addresses the event-triggered based adaptive asymptotic tracking control problem for switched nonlinear systems with unknown control directions based on neural network technique. A novel asymptotic tracking controller, in which Nussbaum functions are introduced to address the issue of unknown control directions, is designed by combining neural network control technology and event-triggered strategy. Different from the existing tracking control schemes, the proposed controller in this paper can guarantee that the tracking error ς 1 asymptotically converges to the origin and reduce the communication burden from the controller to the actuator. Finally, the effectiveness of the presented control design is proved by numerical examples.

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