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.

scholarly journals Adaptive Backstepping Sliding Mode Control of Air-Breathing Hypersonic Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shuo Wang ◽  
Ju Jiang ◽  
Chaojun Yu
Keyword(s):  
Sliding Mode Control ◽  
Tracking Control ◽  
Control Method ◽  
Sliding Mode ◽  
Hypersonic Vehicles ◽  
Control Input ◽  
Backstepping Method ◽  
Trajectory Tracking Control ◽  
Air Breathing ◽  
Mode Control

In this paper, a controller combining backstepping and adaptive supertwisting sliding mode control method is proposed for altitude and velocity tracking control of air-breathing hypersonic vehicles (AHVs). Firstly, the nonlinear longitudinal model of AHV is introduced and transformed into a strict feedback form, to which the backstepping method can be applied. Considering the longitudinal trajectory tracking control problem (altitude control and velocity control), the altitude tracking control system is decomposed to several one-order subsystems based on the backstepping method, and an adaptive supertwisting sliding mode controller is designed for each subsystem, in order to obtain the virtual control variables and actual control input. Secondly, the overall stability of the closed-loop system is proved by the Lyapunov stability theory. At last, the simulation is carried out on an AHV model. The results show that the proposed controller has good control performances and good robustness in the parameter perturbation case.

scholarly journals A Novel Active Fault-Tolerant Tracking Control for Robot Manipulators with Finite-Time Stability

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8101
Author(s):  
Thanh Nguyen Truong ◽  
Anh Tuan Vo ◽  
Hee-Jun Kang ◽  
Mien Van
Keyword(s):  
Dynamic Model ◽  
Finite Time ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robotic Manipulators ◽  
Convergence Speed ◽  
Lyapunov Theory ◽  
Control Input ◽  
Observation Error ◽  
Simulation Performance

Many terminal sliding mode controllers (TSMCs) have been suggested to obtain exact tracking control of robotic manipulators in finite time. The ordinary method is based on TSMCs that secure trajectory tracking under the assumptions such as the known robot dynamic model and the determined upper boundary of uncertain components. Despite tracking errors that tend to zero in finite time, the weakness of TSMCs is chattering, slow convergence speed, and the need for the exact robot dynamic model. Few studies are handling the weakness of TSMCs by using the combination between TSMCs and finite-time observers. In this paper, we present a novel finite-time fault tolerance control (FTC) method for robotic manipulators. A finite-time fault detection observer (FTFDO) is proposed to estimate all uncertainties, external disturbances, and faults accurately and on time. From the estimated information of FTFDO, a novel finite-time FTC method is developed based on a new finite-time terminal sliding surface and a new finite-time reaching control law. Thanks to this approach, the proposed FTC method provides a fast convergence speed for both observation error and control error in finite time. The operation of the robot system is guaranteed with expected performance even in case of faults, including high tracking accuracy, small chattering behavior in control input signals, and fast transient response with the variation of disturbances, uncertainties, or faults. The stability and finite-time convergence of the proposed control system are verified that they are strictly guaranteed by Lyapunov theory and finite-time control theory. The simulation performance for a FARA robotic manipulator proves the proposed control theory’s correctness and effectiveness.

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.

Tracking control using hierarchical fuzzy-regulated optimal approach for robotic manipulators

2018 ◽  
Vol 233 (2) ◽  
pp. 635-648 ◽  
Author(s):  
Haiyun Zhang ◽  
Wei Li
Keyword(s):  
Tracking Control ◽  
Fuzzy Controller ◽  
Dynamical Behavior ◽  
Robotic Manipulators ◽  
Optimal Controller ◽  
Hierarchical Architecture ◽  
Trajectory Tracking Control ◽  
Optimal Tracking ◽  
Barbalat’S Lemma ◽  
Self Organizing

Robotic manipulators have captured the attention of many specialists owing to its importance in academic research and industrial automation. This paper proposes a novel hierarchical self-organizing fuzzy optimal controller for the trajectory tracking control of robotic manipulator systems. The hierarchical self-organizing fuzzy optimal controller employs a self-organizing fuzzy logic system as a superior control strategy regulator for a subordinate optimal tracking controller. Using the self-organizing learning and fuzzy inference operation, the weighting matrix in the optimal controller is configured adaptively according to the robotic dynamical behavior. The optimal tracking control law under this hierarchical architecture is derived using the maximum principle. Stability and robustness of the hierarchical self-organizing fuzzy optimal controller are then analyzed and proved through the Lyapunov stability approach and Barbalat's Lemma. A simulation study demonstrates the effectiveness and feasibility of this hierarchical system, and compares it with a self-organizing fuzzy controller.

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