scholarly journals Real-Time Inverse Optimal Neural Control for Image Based Visual Servoing with Nonholonomic Mobile Robots

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Carlos López-Franco ◽  
Michel López-Franco ◽  
Alma Y. Alanis ◽  
Javier Gómez-Avila ◽  
Nancy Arana-Daniel
Keyword(s):  
Real Time ◽  
Visual Servoing ◽  
Neural Control ◽  
Parameter Uncertainties ◽  
Visual Sensor ◽  
External Disturbances ◽  
Neural Controller ◽  
Nonholonomic Mobile Robots ◽  
Nonholonomic Mobile Robot ◽  
High Order Neural Network

We present an inverse optimal neural controller for a nonholonomic mobile robot with parameter uncertainties and unknown external disturbances. The neural controller is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter. The reference velocities for the neural controller are obtained with a visual sensor. The effectiveness of the proposed approach is tested by simulations and real-time experiments.

scholarly journals Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties

2021 ◽  
Vol 11 (6) ◽  
pp. 2797
Author(s):  
Filiberto Muñoz ◽  
Jorge S. Cervantes-Rojas ◽  
Jose M. Valdovinos ◽  
Omar Sandre-Hernández ◽  
Sergio Salazar ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Neural Control ◽  
Autonomous Underwater Vehicle ◽  
Parametric Uncertainty ◽  
Underwater Vehicle ◽  
Added Mass ◽  
Parametric Estimation ◽  
Parameter Uncertainties ◽  
External Disturbances ◽  
Adaptive Tracking Control

This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.

scholarly journals A Switched Approach to Image-Based Stabilization for Nonholonomic Mobile Robots with Field-of-View Constraints

2021 ◽  
Vol 11 (22) ◽  
pp. 10895
Author(s):  
Yao Huang
Keyword(s):  
Mobile Robot ◽  
Visual Servoing ◽  
Nonholonomic Constraint ◽  
Focal Length ◽  
Field Of View ◽  
System Model ◽  
Nonholonomic Mobile Robots ◽  
Nonholonomic Mobile Robot ◽  
Polar Coordinate ◽  
Simulation Results

This paper presents a switched visual servoing strategy for maneuvering the nonholonomic mobile robot to the desired configuration while keeping the tracked image points in the vision of the camera. Firstly, a pure backward motion and a pure rotational motion are applied to the mobile robot in succession. Thus, the principle point and the scaled focal length in x direction of the camera are identified through the visual feedback from a fixed onboard camera. Secondly, the identified parameters are used to build the system model in polar-coordinate representation. Then an adaptive non-smooth controller is designed to maneuver the mobile robot to the desired configuration under the nonholonomic constraint. And a switched strategy which consists of two image-based controllers is utilized for keeping the features in the field-of-view. Simulation results are presented to validate the effectiveness of the proposed approach.

scholarly journals Real-time neural control of all-terrain tracked robots with unknown dynamics and network communication delays

2020 ◽  
Vol 21 (3) ◽  
pp. 1-12
Author(s):  
Alma Y. Alanis ◽  
Jorge D. Rios ◽  
Nancy Arana-Daniel ◽  
Carlos Lopez-Franco
Keyword(s):  
Real Time ◽  
Neural Control ◽  
Actuator Saturation ◽  
Cyber Physical Systems ◽  
Communication Delays ◽  
Packet Losses ◽  
Physical Systems ◽  
Intelligent Controller ◽  
High Order Neural Network ◽  
Robot Platform

This work focuses on the design of an intelligent controller that is a considerably large challenge for cyber-physical systems. The proposed controller can deal with unknown dynamics, actuator saturation, unknown external and internal disturbances, unknown communication delays and packet losses. Such a controller is designed using a discrete-time approach based on inverse optimal control and a recurrent high-order neural network identifier. The applicability of the proposed scheme is shown through real-time results using a tracked robot platform controlled through a wireless network under different network scenarios.

scholarly journals Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7084
Author(s):  
Song Kang ◽  
Yongfeng Rong ◽  
Wusheng Chou
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Entire System ◽  
Actuator Faults ◽  
Extended State ◽  
Parameter Uncertainties ◽  
External Disturbances ◽  
Measurement Noises ◽  
Fuzzy Adaptive ◽  
Logic System

In this paper, an output-feedback fuzzy adaptive dynamic surface controller (FADSC) based on fuzzy adaptive extended state observer (FAESO) is proposed for autonomous underwater vehicle (AUV) systems in the presence of external disturbances, parameter uncertainties, measurement noises and actuator faults. The fuzzy logic system is incorporated into both the observers and controllers to improve the adaptability of the entire system. The dynamics of the AUV system is established first, considering the external disturbances and parameter uncertainties. Based on the dynamic models, the ESO, combined with a fuzzy logic system tuning the observer bandwidth, is developed to not only adaptively estimate both system states and the lumped disturbances for the controller, but also reduce the impact of measurement noises. Then, the DSC, together with fuzzy logic system tuning the time constant of the low-pass filter, is designed using estimations from the FAESO for the AUV system. The asymptotic stability of the entire system is analyzed through Lyapunov’s direct method in the time domain. Comparative simulations are implemented to verify the effectiveness and advantages of the proposed method compared with other observers and controllers considering external disturbances, parameter uncertainties and measurement noises and even the actuator faults that are not considered in the design process. The results show that the proposed method outperforms others in terms of tracking accuracy, robustness and energy consumption.

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

2021 ◽  
pp. 095965182110131
Author(s):  
Guoqing Zhang ◽  
Shen Gao ◽  
Jiqiang Li ◽  
Weidong Zhang
Keyword(s):  
Neural Control ◽  
Fault Tolerant ◽  
Lyapunov Stability Theory ◽  
Actuator Faults ◽  
Closed Loop System ◽  
External Disturbances ◽  
Unmanned Surface Vehicle ◽  
Adaptive Parameters ◽  
Control Gain ◽  
Event Triggered

This study investigates the course-tracking problem for the unmanned surface vehicle in the presence of constraints of the actuator faults, control gain uncertainties, and environmental disturbance. A novel event-triggered robust neural control algorithm is proposed by fusing the robust neural damping technique and the event-triggered input mechanism. In the algorithm, no prior information of the system model about the unknown yawing dynamic parameters and unknown external disturbances is required. The transmission burden between the controller and the actuator could be relieved. Moreover, the control gain-related uncertainties and the unknown actuator faults are compensated through two updated online adaptive parameters. Sufficient effort has been made to verify the semi-global uniform ultimate bounded stability for the closed-loop system based on Lyapunov stability theory. Finally, simulation results are presented to illustrate the effectiveness and superiority of the proposed algorithm.

Real-Time Decentralized Neural Control for a Five Dof Redundant Robot

2013 ◽  
Vol 19 (1) ◽  
pp. 23-37 ◽  
Author(s):  
Ramon Garcia-Hernandez ◽  
Jose A. Ruz-Hernandez ◽  
Edgar N. Sanchez ◽  
Maarouf Saad
Keyword(s):  
Real Time ◽  
Neural Control ◽  
Redundant Robot

Real-Time Visual Servoing Using Controlled Illumination

1994 ◽  
Vol 13 (1) ◽  
pp. 93-100 ◽  
Author(s):  
J.P. Urban ◽  
G. Motyl ◽  
J. Gallice
Keyword(s):  
Real Time ◽  
Visual Servoing ◽  
Controlled Illumination
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