An Adaptive Neural Network Sliding Mode Controller for Robotic Manipulators

2006 ◽  
Author(s):  
N. Sadati ◽  
R. Ghadami ◽  
M. Bagherpour
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Robotic Manipulators ◽  
Sliding Mode Controller ◽  
Adaptive Neural Network

Trajectory-Tracking-Based Adaptive Neural Network Sliding Mode Controller for Robot Manipulators

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Bin Ren ◽  
Yao Wang ◽  
Jiayu Chen
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Lyapunov Theory ◽  
Robot Dynamics ◽  
Sliding Mode Controller ◽  
Adaptive Neural Network ◽  
Proposed Model ◽  
The Impact

Abstract Unpredictable disturbances and chattering are the major challenges of the robot manipulator control. In recent years, trajectory-tracking-based controllers have been recognized by many researchers as the most promising method to understand robot dynamics with uncertainties and improve robot control. However, reliable trajectory-tracking-based controllers require high model precision and complexity. To develop an agile and straightforward method to mitigate the impact caused by uncertain disturbance and chattering, this study proposed an adaptive neural network sliding mode controller based on the super-twisting algorithm. The proposed model not only can minimize the tracking error but also improve the system robustness with a simpler structure. Moreover, the proposed controller has the following two distinctive features: (1) the weights of the radial basis function (RBF network) are designed to be adjusted in real-time and (2) the prior knowledge of the actual robot system is not required. The analytical model of the proposed controller was proved to be stable and ensured by the Lyapunov theory. To validate the proposed model, this study also conducted a comparative simulation on a two-link robot manipulator system with the conventional sliding mode controller and the model-based controller. The results suggest the proposed model improved the control accuracy and had fewer chattering.

scholarly journals Adaptive neural network sliding mode control for active suspension systems with electrohydraulic actuator dynamics

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094198
Author(s):  
Jinwei Sun ◽  
Kai Zhao
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Active Suspension ◽  
Sliding Mode Controller ◽  
Adaptive Neural Network ◽  
Actuator Dynamics ◽  
Suspension Systems ◽  
Active Suspension Systems ◽  
Highly Nonlinear ◽  
Electrohydraulic Actuator

The object of this article is to design an observer-based adaptive neural network sliding mode controller for active suspension systems. A general nonlinear suspension model is established, and the electrohydraulic actuator dynamics are considered. The proposed controller is decomposed into two loops. Since the dynamics of the actuator is assumed highly nonlinear with uncertainties, the adaptive neural network is presented in the inner loop to ensure the control system robustness against uncertainties, and the self-tuning weighting vector is adjusted online according to the updated law obtained by Lyapunov stability theory. In the outer loop, a model reference sliding mode controller is developed to track the desired states of the hybrid reference model that combines skyhook and groundhook control methods. Besides, to obtain the unmeasured states of the system, an unscented Kalman filter is utilized to provide necessary information for the controller. Simulation results show that the exerted force can be tracked precisely even in the existence of uncertainties. Moreover, the proposed controller can improve the suspension’s performance effectively.

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

A novel robust finite-time tracking control of uncertain robotic manipulators with disturbances

2020 ◽  
pp. 107754632098244
Author(s):  
Hamid Razmjooei ◽  
Mohammad Hossein Shafiei ◽  
Elahe Abdi ◽  
Chenguang Yang
Keyword(s):  
Finite Time ◽  
Control Method ◽  
Sliding Mode ◽  
Robotic Manipulators ◽  
Sliding Mode Controller ◽  
Time Varying ◽  
State Variables ◽  
Terminal Sliding Mode ◽  
Control Laws ◽  
Finite Time Boundedness

In this article, an innovative technique to design a robust finite-time state feedback controller for a class of uncertain robotic manipulators is proposed. This controller aims to converge the state variables of the system to a small bound around the origin in a finite time. The main innovation of this article is transforming the model of an uncertain robotic manipulator into a new time-varying form to achieve the finite-time boundedness criteria using asymptotic stability methods. First, based on prior knowledge about the upper bound of uncertainties and disturbances, an innovative finite-time sliding mode controller is designed. Then, the innovative finite-time sliding mode controller is developed for finite-time tracking of time-varying reference signals by the outputs of the system. Finally, the efficiency of the proposed control laws is illustrated for serial robotic manipulators with any number of links through numerical simulations, and it is compared with the nonsingular terminal sliding mode control method as one of the most powerful finite-time techniques.

Sliding Mode Controller with Neural Network Identification

2015 ◽  
Vol 13 (12) ◽  
pp. 3754-3757 ◽  
Author(s):  
Jose Egidio Azzaro ◽  
Ricardo Alfredo Veiga
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Network Identification

scholarly journals Robust Control for Robotic Manipulators Base on Adaptive Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 497-502 ◽  
Author(s):  
Wenhui Zhang ◽  
Xiaoping Ye ◽  
Lihong Jiang ◽  
Fang Yamin
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Robotic Manipulators ◽  
Adaptive Neural Network
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