scholarly journals Neural Network-Based Adaptive Control of Robotic Manipulator: Application to a Three Links Cylindrical Robot

2017 ◽  
Vol 13 (1) ◽  
pp. 114-122
Author(s):  
Abdul-Basset AL-Hussein
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Lyapunov Theorem ◽  
External Disturbances ◽  
The Stability ◽  
Learning Law

A composite PD and sliding mode neural network (NN)-based adaptive controller, for robotic manipulator trajectory tracking, is presented in this paper. The designed neural networks are exploited to approximate the robotics dynamics nonlinearities, and compensate its effect and this will enhance the performance of the filtered error based PD and sliding mode controller. Lyapunov theorem has been used to prove the stability of the system and the tracking error boundedness. The augmented Lyapunov function is used to derive the NN weights learning law. To reduce the effect of breaching the NN learning law excitation condition due to external disturbances and measurement noise; a modified learning law is suggested based on e-modification algorithm. The controller effectiveness is demonstrated through computer simulation of cylindrical robot manipulator.

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

2021 ◽  
Vol 11 (2) ◽  
pp. 12-16
Author(s):  
Monisha Pathak ◽  
◽  
Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Trajectory Tracking Control ◽  
Chattering Effect

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

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.

Force Control of Robot Manipulator by Neural Network Model - Experiment and Evaluation of One-Degree-of-Freedom Manipulator -

1990 ◽  
Vol 2 (4) ◽  
pp. 273-281 ◽  
Author(s):  
Masatoshi Tokita ◽  
◽  
Toyokazu Mitsuoka ◽  
Toshio Fukuda ◽  
Takashi Kurihara ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Force Control ◽  
Pid Controller ◽  
Robot Manipulator ◽  
Force Sensor ◽  
Robotic Manipulator ◽  
The Neural Network ◽  
The Stability

In this paper, a force control of a robotic manipulator based on a neural network model is proposed with consideration of the dynamics of both the force sensor and objects. This proposed system consists of the standard PID controller, the gains of which are augmented and adjusted depending on objects through a process of learning. The authors proposed a similar method previously for the force control of the robotic manipulator with consideration of dynamics of objects, but without consideration of dynamics of the force sensor, showing only simulation results. This paper shows the similar structure of the controller via the neural network model applicable to the cases with consideration of both effects and demonstrates that the proposed method shows the better performance than the conventional PID type of controller, yielding to the wider range of applications, consequently. Therefore, this method can be applied to the force/compliance control problems. The effects of the number of neurons and hidden layers of the neural network model are also discussed through the simulation and experimental results as well as the stability of the control system.

Neural Network Sliding Mode Control of 5DOFs Robotic Manipulator

2014 ◽  
Vol 898 ◽  
pp. 514-520 ◽  
Author(s):  
Chang Kai Xu ◽  
Ming Li ◽  
Jian Yin
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Dynamic Performance ◽  
Robotic Manipulator ◽  
Terminal Sliding Mode ◽  
Mode Control ◽  
The Stability

In this paper, a neural network sliding mode controller for a kind of 5DOFs robotic manipulator is proposed. A radial basis function (RBF) neural network is used as an estimator to approximate uncertainties of the system. The learning algorithm of the neural network improves the performance of the system. A globle terminal sliding mode control (GTSMC) is designed to guarantee the stability and improve the dynamic performance of the robotic manipulator. Simulation results show that the proposed NNSMC strategy is effective to ensure the robustness and dynamic performance of the 5DOFs robotic manipulator.

scholarly journals Nonlinear PD plus sliding mode control with application to a parallel delta robot

2018 ◽  
Vol 69 (5) ◽  
pp. 329-336 ◽  
Author(s):  
Chems Eddine Boudjedir ◽  
Djamel Boukhetala ◽  
Mohamed Bouri
Keyword(s):  
Convergence Rate ◽  
Error Rate ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Tracking Error ◽  
Sliding Mode Controller ◽  
External Disturbances ◽  
On Line ◽  
Delta Robot ◽  
The Stability

Abstract In this paper, a hybrid nonlinear proportional-derivative-sliding mode controller (NPD-SMC) is developed for the trajectory tracking of robot manipulators. The proposed controller combines the advantage of the easy implementation of NPD control and the robustness of SMC. The gains of PD control are tuned on-line in order to increase the convergence rate, whereas the SMC term is introduced to reject the external disturbances without requiring to know the system dynamics. The stability of the NPD-SMC is proved using Lyaponuv theorem, and it is demonstrated that the tracking error and the tracking error rate converge asymptotically to zero. Experiments are carried out on the parallel Delta robot to illustrate the effectiveness and robustness of the proposed approach. It is also shown the superiority of the NPD-SMC control over the NPD control and PD-SMC control.

scholarly journals Sliding Mode with Adaptive Control of Robot Manipulator Trajectory Tracking using Neural Network Approximation

2021 ◽  
Vol 10 (6) ◽  
pp. 120
Author(s):  
Monisha Pathak* ◽  
◽  
Dr. Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Robotic Manipulator ◽  
Adaptive Sliding Mode Control ◽  
Control Approach ◽  
Adaptive Sliding Mode

This paper briefly discusses about the Robust Controller based on Adaptive Sliding Mode Technique with RBF Neural Network (ASMCNN) for Robotic Manipulator tracking control in presence of uncertainities and disturbances. The aim is to design an effective trajectory tracking controller without any modelling information. The ASMCNN is designed to have robust trajectory tracking of Robot Manipulator, which combines Neural Network Estimation with Adaptive Sliding Mode Control. The RBF model is utilised to construct a Lyapunov function-based adaptive control approach. Simulation of the tracking control of a 2dof Robotic Manipulator in the presence of unpredictability and external disruption demonstrates the usefulness of the planned ASMCNN.

scholarly journals Fault-Tolerant Control for N-Link Robot Manipulator via Adaptive Nonsingular Terminal Sliding Mode Control Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Nannan Shi ◽  
Fanghui Luo ◽  
Zhikuan Kang ◽  
Lihui Wang ◽  
Zhuo Zhao ◽  
...  
Keyword(s):  
Sliding Mode Control ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Tracking Error ◽  
Terminal Sliding Mode Control ◽  
Terminal Sliding Mode ◽  
Mode Control ◽  
Nonsingular Terminal Sliding Mode ◽  
The Stability

An adaptive nonsingular terminal sliding mode control (ANTSMC) scheme for the n-link robot manipulator is presented in this study, which can achieve faster convergence and higher precision tracking compared with the linear hyperplane-based sliding mode control. Novel adaptive updating laws based on the actual tracking error are employed to online adjust the upper bound of uncertainty, which comprehensively consider both the tracking performance and chattering eliminating problem. The stability analysis of the proposed ANTSMC is verified using the Lyapunov method with the existence of the parameter uncertainty and the actuator faults. Numerical simulation studies the comparison of performance between ANTSMC and the conventional nonsingular terminal sliding mode control (NTSMC) scheme to validate the advantages of the proposed control algorithm.

Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

Robotica ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 497-512 ◽  
Author(s):  
Juntao Fei ◽  
Yuzheng Yang
Keyword(s):  
Neural Network ◽  
Upper Bound ◽  
Tracking Control ◽  
Sliding Mode ◽  
Tracking Error ◽  
Modeling Error ◽  
External Disturbances ◽  
Mems Gyroscope ◽  
Control Scheme ◽  
The Impact

SUMMARYA new robust neural sliding mode (RNSM) tracking control scheme using radial basis function (RBF) neural network (NN) is presented for MEMS z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed to eliminate the impact of NN modeling error and external disturbances for guaranteeing the asymptotic stability property. Moreover, another RBF NN is employed to learn the upper bound of NN modeling error and external disturbances, so the prior knowledge of the upper bound of system uncertainties is not required. All the adaptive laws in the RNSM control system are derived in the same Lyapunov framework, which can guarantee the stability of the closed loop system. Comparative numerical simulations for an MEMS gyroscope are investigated to verify the effectiveness of the proposed RNSM tracking control scheme.

Adaptive Sliding Mode Recurrent Fuzzy Neural Network Estimator in Magnetic Bearing System

2014 ◽  
Vol 543-547 ◽  
pp. 1487-1491 ◽  
Author(s):  
Huann Keng Chiang ◽  
Chao Ting Chu ◽  
Tzu Chieh Lin
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Magnetic Bearing ◽  
Lyapunov Theorem ◽  
Adaptive Sliding Mode ◽  
Fuzzy Neural ◽  
Network Estimation ◽  
The Stability ◽  
Bearing System

This paper proposesd am adaptive sliding mode fuzzy neural network estimation (ASFNE) in the magnetic bearing system (MBS). The fuzzy neural network estimator has fuzzy rules base and neural network weights which the stability is proved by Lyapunov theorem in ASFNE. Therefore, ASFNE estimates system lump uncertainty to improve steady-state error and reduced chattering phenomenon. Finally, we compared ASFNE and sliding mode controller in MBS which ASFNE has better output responses.

Neural-network-based robust hybrid force/position controller for a constrained robot manipulator with uncertainties

2017 ◽  
Vol 40 (5) ◽  
pp. 1625-1636 ◽  
Author(s):  
Mohammad-Hossein Ghajar ◽  
Mehdi Keshmiri ◽  
Javad Bahrami
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Robot Manipulator ◽  
Tracking Error ◽  
Vertical Surface ◽  
Contact Friction ◽  
Neural Network Controller ◽  
Position Controller ◽  
The Stability ◽  
Constrained Robot

Here, an intelligent hybrid position/force controller is designed for a constrained robot manipulator with contact friction between its end-effector and environment in presence of both large parameter and dynamic uncertainties. The controller includes two major parts. The first part, denoted as the main controller, consists of two closed-loops fulfilling motion tracking and force tracking objectives. The second part, called the tuning controller, is an adaptive neural network controller to compensate for the deficiencies of the model-based controller. The stability of the overall system is guaranteed through the Lyapunov and passivity theorems. The performance of the controller is evaluated using numerical simulations as well as experimental implementation. In the experimental analyses, the proposed controller is implemented on a two-link robot manipulator that interacts with a vertical surface. Results show a significant decrease in tracking error in the presence of uncertainties, owing to use of neural network sub-block.

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