scholarly journals Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Jinzhu Peng ◽  
Zeqi Yang ◽  
Tianlei Ma
Keyword(s):  
Neural Network ◽  
Impedance Control ◽  
Tracking Error ◽  
Tracking Performance ◽  
Robotic Systems ◽  
Position Tracking ◽  
External Disturbances ◽  
Outer Loop ◽  
Control Scheme ◽  
Force Tracking

In this paper, an adaptive Jacobian and neural network based position/force tracking impedance control scheme is proposed for controlling robotic systems with uncertainties and external disturbances. To achieve precise force control performance indirectly by using the position tracking, the control scheme is divided into two parts: the outer-loop force impedance control and the inner-loop position tracking control. In the outer-loop, an improved impedance controller, which combines the traditional impedance relationship with the PID-like scheme, is designed to eliminate the force tracking error quickly and to reduce the force overshoot effectively. In this way, the satisfied force tracking performance can be achieved when the manipulator contacts with environment. In the inner-loop, an adaptive Jacobian method is proposed to estimate the velocities and interaction torques of the end-effector due to the system kinematical uncertainties, and the system dynamical uncertainties and the uncertain term of adaptive Jacobian are compensated by an adaptive radial basis function neural network (RBFNN). Then, a robust term is designed to compensate the external disturbances and the approximation errors of RBFNN. In this way, the command position trajectories generated from the outer-loop force impedance controller can be then tracked so that the contact force tracking performance can be achieved indirectly in the forced direction. Based on the Lyapunov stability theorem, it is proved that all the signals in closed-loop system are bounded and the position and velocity errors are asymptotic convergence to zero. Finally, the validity of the control scheme is shown by computer simulation on a two-link robotic manipulator.

scholarly journals Impedance with Finite-Time Control Scheme for Robot-Environment Interaction

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Heyu Hu ◽  
Xiaoqi Wang ◽  
Lerui Chen
Keyword(s):  
Finite Time ◽  
Impedance Control ◽  
Tracking Performance ◽  
Position Tracking ◽  
Environment Interaction ◽  
Robot System ◽  
Outer Loop ◽  
Time Control ◽  
Control Scheme ◽  
Force Tracking

For the robot system with the uncertain model and unknown environment parameters, a control scheme combining impedance and finite time is proposed. In order to obtain accurate force control performance indirectly by using position tracking, the control scheme is divided into two parts: an outer loop for force impedance control and an inner loop for position tracking control. In the outer loop, in order to eliminate the force tracking error quickly, the impedance control based on force is adopted; when the robot contacts with the environment, the satisfactory force tracking performance can be obtained. In the inner loop, the finite-time control method based on the homogeneous system is used. Through this method, the desired virtual trajectory generated by the outer loop can be tracked, and the contact force tracking performance can be obtained indirectly in the direction of force. This method does not need the dynamics model knowledge of the robot system, thus avoiding the online real-time calculation of the inverse dynamics of the robot. The unknown uncertainty and external interference of the system are obtained online by using the time-delay estimation, and the control process is effectively compensated, so the algorithm is simple, the convergence speed is fast, and the practical application is easy. The theory of finite-time stability is used to prove that the closed-loop system is finite-time stable, and the effectiveness of the algorithm is proved by simulations.

Development of an autonomous robotic system for beard shaving assistance of disabled people based on an adaptive force tracking impedance control

2021 ◽  
pp. 095440622098482
Author(s):  
Oladayo S Ajani ◽  
Samy FM Assal
Keyword(s):  
Impedance Control ◽  
Environmental Parameters ◽  
Robotic System ◽  
Robotic Systems ◽  
Full Potential ◽  
Robotic Assistance ◽  
Head Pose ◽  
Detection And Tracking ◽  
Control Scheme ◽  
Force Tracking

Recently, people with upper arm disabilities due to neurological disorders, stroke or old age are receiving robotic assistance to perform several activities such as shaving, eating, brushing and drinking. Although the full potential of robotic assistance lies in the use of fully autonomous robotic systems, these systems are limited in design due to the complexities and the associated risks. Hence, rather than the shared controlled or active robotic systems used for such tasks around the head, an adaptive compliance control scheme-based autonomous robotic system for beard shaving assistance is proposed. The system includes an autonomous online face detection and tracking as well as selected geometrical features-based beard region estimation using the Kinect RGB-D camera. Online trajectory planning for achieving the shaving task is enabled; with the capability of online re-planning trajectories in case of unintended head pose movement and occlusion. Based on the dynamics of the UR-10 6-DOF manipulator using ADAMS and MATLAB, an adaptive force tracking impedance controller whose parameters are tuned using Genetic Algorithm (GA) with force/torque constraints is developed. This controller can regulate the contact force under head pose changing and varying shaving region stiffness by adjusting the target stiffness of the controller. Simulation results demonstrate the system capability to achieve beard shaving autonomously with varying environmental parameters that can be extended for achieving other tasks around the head such as feeding, drinking and brushing.

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.

scholarly journals A Novel Modified Super-Twisting Control Augmented Feedback Linearization for Wearable Robotic Systems Using Time Delay Estimation

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 597
Author(s):  
Brahim Brahmi ◽  
Ibrahim El Bojairami ◽  
Tanvir Ahmed ◽  
Asif Al Zubayer Swapnil ◽  
Mohammad AssadUzZaman ◽  
...  
Keyword(s):  
Time Delay ◽  
Feedback Linearization ◽  
High Performance ◽  
Time Delay Estimation ◽  
Robotic Systems ◽  
External Disturbances ◽  
Uncertain Dynamics ◽  
Control Scheme ◽  
Twisting Control ◽  
Dynamics Stability

The research presents a novel controller designed for robotic systems subject to nonlinear uncertain dynamics and external disturbances. The control scheme is based on the modified super-twisting method, input/output feedback linearization, and time delay approach. In addition, to minimize the chattering phenomenon and ensure fast convergence to the selected sliding surface, a new reaching law has been integrated with the control law. The control scheme aims to provide high performance and enhanced accuracy via limiting the effects brought by the presence of uncertain dynamics. Stability analysis of the closed-loop system was conducted using a powerful Lyapunov function, showing finite time convergence of the system’s errors. Lastly, experiments shaping rehabilitation tasks, as performed by healthy subjects, demonstrated the controller’s efficiency given its uncertain nonlinear dynamics and the external disturbances involved.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

scholarly journals A model reference adaptive variable impedance control method for robot

2021 ◽  
Vol 336 ◽  
pp. 03005
Author(s):  
Xinchao Sun ◽  
Lianyu Zhao ◽  
Zhenzhong Liu
Keyword(s):  
Real Time ◽  
Tracking Control ◽  
Control Method ◽  
Impedance Control ◽  
Tracking Error ◽  
Reference Trajectory ◽  
Control Parameters ◽  
Model Reference ◽  
Force Tracking ◽  
Model Reference Adaptive

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.

Barrier Lyapunov function-based robot control with an augmented neural network approximator

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zuguo Zhang ◽  
Qingcong Wu ◽  
Xiong Li ◽  
Conghui Liang
Keyword(s):  
Neural Network ◽  
Lyapunov Function ◽  
Control Algorithm ◽  
Tracking Performance ◽  
Content Type ◽  
Barrier Lyapunov Function ◽  
Output Error ◽  
Control Scheme ◽  
Stringent Constraint ◽  
Continuous Dynamic

Purpose Considering the complexity of dynamic and friction modeling, this paper aims to develop an adaptive trajectory tracking control scheme for robot manipulators in a universal unmodeled method, avoiding complicated modeling processes. Design/methodology/approach An augmented neural network (NN) constituted of radial basis function neural networks (RBFNNs) and additional sigmoid-jump activation function (SJF) neurons is introduced to approximate complicated dynamics of the system: the RBFNNs estimate the continuous dynamic term and SJF neurons handle the discontinuous friction torques. Moreover, the control algorithm is designed based on Barrier Lyapunov Function (BLF) to constrain output error. Findings Lyapunov stability analysis demonstrates the exponential stability of the closed-loop system and guarantees the tracking errors within predefined boundaries. The introduction of SJFs alleviates the limitation of RBFNNs on discontinuous function approximation. Owing to the fast learning speed of RBFNNs and jump response of SJFs, this modified NN approximator can reconstruct the system model accurately at a low compute cost, and thereby better tracking performance can be obtained. Experiments conducted on a manipulator verify the improvement and superiority of the proposed scheme in tracking performance and uncertainty compensation compared to a standard NN control scheme. Originality/value An enhanced NN approximator constituted of RBFNN and additional SJF neurons is presented which can compensate the continuous dynamic and discontinuous friction simultaneously. This control algorithm has potential usages in high-performance robots with unknown dynamic and variable friction. Furthermore, it is the first time to combine the augmented NN approximator with BLF. After more exact model compensation, a smaller tracking error is realized and a more stringent constraint of output error can be implemented. The proposed control scheme is applicable to some constraint occasion like an exoskeleton and surgical robot.

Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs

2010 ◽  
pp. 286-311 ◽  
Author(s):  
Luis J. Ricalde ◽  
Edgar N. Sanchez ◽  
Alma Y. Alanis
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Network Control ◽  
Design Parameters ◽  
The Neural Network ◽  
Control Scheme ◽  
Output Trajectory ◽  
Error Dynamics ◽  
Adaptation Law ◽  
Constrained Inputs

This Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies . Tracking error boundedness is established as a function of design parameters.

Indirect Neural Network Adaptive Robust Control of Linear Motor Drive System

2002 ◽  
Author(s):  
J. Q. Gong ◽  
Bin Yao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Tracking Error ◽  
Linear Motor ◽  
Adaptive Robust Control ◽  
Operating Conditions ◽  
Tracking Performance ◽  
Motor Drive ◽  
Output Tracking ◽  
On Line

In this paper, an indirect neural network adaptive robust control (INNARC) scheme is developed for the precision motion control of linear motor drive systems. The proposed INNARC achieves not only good output tracking performance but also excellent identifications of unknown nonlinear forces in system for secondary purposes such as prognostics and machine health monitoring. Such dual objectives are accomplished through the complete separation of unknown nonlinearity estimation via neural networks and the design of baseline adaptive robust control (ARC) law for output tracking performance. Specifically, recurrent neural network (NN) structure with NN weights tuned on-line is employed to approximate various unknown nonlinear forces of the system having unknown forms to adapt to various operating conditions. The design is actual system dynamics based, which makes the resulting on-line weight tuning law much more robust and accurate than those in the tracking error dynamics based direct NNARC designs in implementation. With a controlled learning process achieved through projection type weights adaptation laws, certain robust control terms are constructed to attenuate the effect of possibly large transient modelling error for a theoretically guaranteed robust output tracking performance in general. Experimental results are obtained to verify the effectiveness of the proposed INNARC strategy. For example, for a typical point-to-point movement, with a measurement resolution level of ±1μm, the output tracking error during the entire execution period is within ±5μm and mainly stays within ±2μm showing excellent output tracking performance. At the same time, the outputs of NNs approximate the unknown forces very well allowing the estimates to be used for secondary purposes such as prognostics.

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