Apply Model-Free Adaptive Control Approach for Mobile Robot Path Following

In this work, a novel higher-order model-free adaptive control scheme is presented based on a dynamic linearization approach for a class of discrete-time single input and single output (SISO) nonlinear systems. The control scheme consists of an adaptive control law, a parameter estimation law, and a reset mechanism. The design and analysis of the proposed control approach depends merely on the measured input and output data of the controlled plant. The control performance is improved by using more information of control input and output error measured from previous sampling time instants. Rigorous mathematical analysis is developed to show the bounded input and bounded output (BIBO) stability of the closed-loop system. Two simulation comparisons show the effectiveness of the proposed control scheme.

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ADAPTIVE CONTROL OF MOBILE ROBOTS USING A NEURAL NETWORK

International Journal of Neural Systems ◽

10.1142/s0129065701000643 ◽

2001 ◽

Vol 11 (03) ◽

pp. 211-218 ◽

Cited By ~ 2

Author(s):

Celso de Sousa ◽

Elder Moreira Hermerly

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Mobile Robot ◽

Mobile Robots ◽

Robot Navigation ◽

Control Approach ◽

On Line ◽

Simulation Results ◽

Previous Learning

A Neural Network - based control approach for mobile robot is proposed. The weight adaptation is made on-line, without previous learning. Several possible situations in robot navigation are considered, including uncertainties in the model and presence of disturbance. Weight adaptation laws are presented as well as simulation results.

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Modified Model-Free Adaptive Control Method Applied to Vibration Control of an Elastic Crane

Volume 6: 15th International Conference on Multibody Systems, Nonlinear Dynamics, and Control ◽

10.1115/detc2019-97654 ◽

2019 ◽

Author(s):

Hoang Anh Pham ◽

Dirk Söffker

Keyword(s):

Adaptive Control ◽

Control Method ◽

Structural Information ◽

Control Strategies ◽

Control Input ◽

Tracking Errors ◽

Adaptive Controllers ◽

Control Approach ◽

Model Free ◽

Controlled Systems

Abstract Model-free adaptive control (MFAC) is a data-driven control approach receiving increased attention in the last years. Different model-free-based control strategies are proposed to design adaptive controllers when mathematical models of the controlled systems should not be used or are not available. Using only measurements (I/O data) from the system, a feedback controller is generated without the need of any structural information about the controlled plant. In this contribution an improved MFAC is discussed for control of unknown multivariable flexible systems. The main improvement in control input calculation is based on the consideration of output tracking errors and its variations. A new updated control input algorithm is developed. The novel idea is firstly applied for controlling vibrations of a MIMO ship-mounted crane. The control efficiency is verified via numerical simulations. The simulation results demonstrate that vibrations of the elastic boom and the payload of the crane can be reduced significantly and better control performance is obtained when using the proposed controller compared to standard model-free adaptive and PI controllers.

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Model-Free Adaptive Control Approach Using Integral Reinforcement Learning

2019 IEEE International Symposium on Robotic and Sensors Environments (ROSE) ◽

10.1109/rose.2019.8790432 ◽

2019 ◽

Author(s):

Mohammed Abouheaf ◽

Wail Gueaieb

Keyword(s):

Adaptive Control ◽

Reinforcement Learning ◽

Control Approach ◽

Model Free

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Simultaneous tracking and stabilization of an omnidirectional mobile robot in polar coordinates: a unified control approach

Robotica ◽

10.1017/s0263574708004852 ◽

2009 ◽

Vol 27 (3) ◽

pp. 447-458 ◽

Cited By ~ 14

Author(s):

Hsu-Chih Huang ◽

Ching-Chih Tsai

Keyword(s):

Mobile Robot ◽

Feedback Linearization ◽

Control Method ◽

Kinematic Model ◽

Path Following ◽

Polar Space ◽

Polar Coordinates ◽

Control Approach ◽

Omnidirectional Mobile Robot ◽

Omnidirectional Wheels

SUMMARYThis paper presents a polar-space kinematics control method to achieve simultaneous tracking and stabilization for an omnidirectional wheeled mobile robot with three independent driving omnidirectional wheels equally spaced at 120° from one another. The kinematic model of the robot in polar coordinates is presented. With the kinematic model, a kinematic control method based on feedback linearization is proposed in order to achieve simultaneous tracking and stabilization. The proposed method is easily extended to address the path following problem. Computer simulations and experimental results are presented to show the effectiveness and usefulness of the proposed control method at slow speeds.

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