Dynamic model estimating and designing controller for the 2-DoF planar robot in interaction with cable-driven robot based on adaptive neural network

2021 ◽  
pp. 1-20
Author(s):  
Vahid Bahrami ◽  
Ahmad Kalhor ◽  
Mehdi Tale Masouleh
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Reference Model ◽  
Sliding Mode ◽  
Dynamic Properties ◽  
Tracking Error ◽  
Main Idea ◽  
Robot Dynamics ◽  
Adaptive Neural Network ◽  
Planar Robot

This study intends to investigate the dynamic model estimation and the design of an adaptive neural network based controller for a passive planar robot, performing 2-DoF motion pattern which is in interaction with an actuated cable-driven robot. In fact, the main goal of applying this structure is to use a number of light cables to drive serial robot links and track the desired reference model by the robot’s end-effector. The under study system can be used as a rehabilitation setup which is helpful for those with arm disability. In this way, upon applying sliding mode error dynamics, it is necessary to determine a vector that contains the matrices related to the robot dynamics. However, finding these matrices requires the use of computational approaches such as Newton-Euler or Lagrange. In addition, since the purpose of this paper is to express comprehensive methods, so with increasing the number of links and degrees of freedom of the robot, finding the dynamics of the robot becomes more difficult. Therefore, the Adaptive Neural Network (ANN) with specific inputs has been used for estimation unknown matrices of the system and the controller design has been performed based on it. So, the main idea in using an adaptive controller is the fact there is no pre-knowledge for the dynamic modeling of the system since the human arm could have different dynamic properties. Hence, the controller is formed by an ANN and robust term. In this way, the adaptation laws of the parameters are extracted by Lyapunov approach, and as a result, as aforementioned, the asymptotic stability of the whole of the system is guaranteed. Simulation results certify the efficiency of the proposed method. Finally, using the Roots Mean Square Error (RMSE) criteria, it has been revealed that, in the presence of bounded disturbance with different amplitude, adding the robust term to the controller leads to improve the tracking error about 34% and 62%, respectively.

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.

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.

The Simulation Analysis of Semi-Active Suspension System in Vehicle Based on Sliding Mode Control with Varying Structure

2011 ◽  
Vol 216 ◽  
pp. 96-100
Author(s):  
Jing Jun Zhang ◽  
Wei Sha Han ◽  
Li Ya Cao ◽  
Rui Zhen Gao
Keyword(s):  
Control Method ◽  
Simulation Analysis ◽  
Reference Model ◽  
Sliding Mode ◽  
Tracking Error ◽  
Active Suspension ◽  
Suspension System ◽  
Sliding Mode Controller ◽  
Error Dynamics ◽  
Dynamic Quality

A sliding mode controller for semi-active suspension system of a quarter car is designed with sliding model varying structure control method. This controller chooses Skyhook as a reference model, and to force the tracking error dynamics between the reference model and the plant in an asymptotically stable sliding mode. An equal near rate is used to improve the dynamic quality of sliding mode motion. Simulation result shows that the stability of performance of the sliding-mode controller can effectively improve the driving smoothness and safety.

scholarly journals Neural Network Identification and Sliding Mode Control for Hysteresis Nonlinear System with Backlash-Like Model

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Ruiguo Liu ◽  
Xuehui Gao
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Sliding Mode Control ◽  
Controller Design ◽  
Sliding Mode ◽  
Control Strategies ◽  
Tracking Error ◽  
Mode Control ◽  
Hysteresis Nonlinearity ◽  
System States

A new neural network sliding mode control (NNSMC) is proposed for backlash-like hysteresis nonlinear system in this paper. Firstly, only one neural network is designed to estimate the unknown system states and hysteresis section instead of multiscale neural network at former researches since that can save computation and simplify the controller design. Secondly, a new NNSMC is proposed for the hysteresis nonlinearity where it does not need tracking error transformation. Finally, the Lyapunov functions are adopted to guarantee the stabilities of the identification and control strategies semiglobally uniformly ultimately bounded (UUB). Two cases simulations are proved the effectiveness of the presented identification approach and the performance of the NNSMC.

A sky-hook sliding mode semiactive control for commercial truck seat suspension

2020 ◽  
pp. 107754632094097
Author(s):  
Qiang Chen ◽  
Yong Zhang ◽  
Chengwei Zhu ◽  
Jinbo Wu ◽  
Ye Zhuang
Keyword(s):  
Nonlinear System ◽  
Phase Portrait ◽  
Control Method ◽  
Reference Model ◽  
Sliding Mode ◽  
Tracking Error ◽  
Magnetorheological Damper ◽  
Nonlinear Controller ◽  
Parameter Uncertainties ◽  
Seat Suspension

A semiactive seat suspension control method is proposed in this study and applied to attenuate the vibration of the commercial truck seat for enhancing its ride comfort. The semiactive seat suspension system with a magnetorheological damper behaves with undesirable nonlinear properties. The proposed controller is a typical nonlinear controller, which takes the ideal sky-hook controller as the reference model and forces the tracking error vector. The controller has achieved great performance of attenuating vibration and is robust to parameter uncertainties and external disturbances. The relaxation oscillation phenomenon and convergence were also analyzed by the contribution of the phase portrait. As the phase portrait depicted, the sky-hook controller, a weakly nonlinear system, could be approximated by the equivalent linear approximate model. However, the proposed controller, the sky-hook sliding mode controller, is a strongly nonlinear system, which could not be linearized by the regular perturbation theory, and the criterion is given by the phase portrait. The experiment results showed good agreement with the simulation results, and some other matters encountered were also analyzed in the process of application.

scholarly journals Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yanchao Yin ◽  
Hongwei Niu ◽  
Xiaobao Liu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Approximation Error ◽  
Model Parameters ◽  
Control Law ◽  
The Novel ◽  
Adaptive Neural Network ◽  
Mode Control ◽  
Attitude Tracking

A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS) is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA). Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF) neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.

Sign in / Sign up

Export Citation Format

Share Document