Controller Gain Tuning of a Nonholonomic Mobile Robot via Neural Network Predictor

Yıldırım, Şahin; Savaş, Sertaç; Andruškienė, Jurgita

doi:10.1109/ieeeconf52705.2021.9467455

2021 25th International Conference Electronics 2021

DOI: 10.1109/ieeeconf52705.2021.9467455

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Controller Gain Tuning of a Nonholonomic Mobile Robot via Neural Network Predictor

Şahin Yıldırım

Sertaç Savaş

Jurgita Andruškienė

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2023

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“…There are studies in which the neural network training phase is performed offline. In Yildirim et al [19], the design of the controller is done by means of a neural network predictor. This is not so feasible because it is necessary to use training data until the appropriate values of the gains are obtained so that the error is minimized.…”

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confidence: 99%

Trajectory Tracking Control of a Mobile Robot using Neural Networks

Trujillo,

Morales,

Chávez

et al. 2023

Emerg Sci J

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This paper presents a novel soft computing-based machine learning technique designed to enhance the trajectory tracking capabilities of mobile robots through the application of neural networks. The goal of this approach is to enhance the accuracy and overall performance of trajectory tracking without the need for manual gain recalibration, which is a tedious and time-consuming task for the designer when setting up the robot. This improvement is achieved by creating a kinematic controller based on neural networks, which are constructed using the kinematic model of the robot. In the initial phase, the controller requires gains defined by the designer. Subsequently, during the application phase, the backpropagation algorithm is used to dynamically adjust the gains of the neural network, aiming to minimize the closed-loop error. One of the key innovations introduced by this controller is the potential for automatic online gain tuning, thereby eliminating the need for a pre-learning phase, typically required by traditional neural controllers. To validate the effectiveness of this approach, the results are systematically analyzed and compared against those obtained using a conventional kinematic controller. Performance metrics reveal the improved precision in trajectory tracking achieved by the controller, with reduced effort, highlighting the performance enhancements in different trajectories. Doi: 10.28991/ESJ-2023-07-06-01 Full Text: PDF

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mentioning

confidence: 99%

Trajectory Tracking Control of a Mobile Robot using Neural Networks

Trujillo,

Morales,

Chávez

et al. 2023

Emerg Sci J

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Controller Gain Tuning of a Nonholonomic Mobile Robot via Neural Network Predictor

Cited by 1 publication

References 9 publications

Trajectory Tracking Control of a Mobile Robot using Neural Networks

Trajectory Tracking Control of a Mobile Robot using Neural Networks

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