Tracking control of mobile robots: a case study in backstepping
Automatica (Journal of IFAC)
Neural Control Applied to the Problem of Trajectory Tracking of Mobile Robots with Uncertainties
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
Neural Dynamic Control of a Nonholonomic Mobile Robot Incorporating the Actuator Dynamics
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
Velocity-scheduling control for a unicycle mobile robot: theory and experiments
IEEE Transactions on Robotics
Dual adaptive dynamic control of mobile robots using neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Control of nonholonomic mobile robots based on the transverse function approach
IEEE Transactions on Robotics
Path-following control of mobile robots in presence of uncertainties
IEEE Transactions on Robotics
Robust adaptive control of nonholonomic mobile robot with parameter and nonparameter uncertainties
IEEE Transactions on Robotics
Global exponential tracking control of a mobile robot system via aPE condition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive tracking control of a wheeled mobile robot via anuncalibrated camera system
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Control of a nonholonomic mobile robot using neural networks
IEEE Transactions on Neural Networks
A target-reaching controller for mobile robots using spiking neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Tracking control of a nonholonomic mobile robot using compound cosine function neural networks
Intelligent Service Robotics
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This paper presents the design and implementation of a novel adaptive trajectory tracking controller for a nonholonomic wheeled mobile robot (WMR) with unknown parameters and uncertain dynamics. The learning ability of neural networks is used to design a robust adaptive backstepping controller that does not require the knowledge of the robot dynamics. The kinematic controller gains are tuned on-line to minimize the velocity error and improve the trajectory tracking characteristics. The performance of the proposed control algorithm is verified and compared with the classical backstepping controller through simulation and experiments on a commercial mobile robot platform.