Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks

This paper proposes a new adaptive controller for three-wheeled mobile robots (3WMRs) called the ABHSMC controller. This ABHSMC controller is developed through a cooperative approach, combining a backstepping controller and a Radial Basis Function (RBF) neural network-based Hierarchical Sliding Mode...

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Bibliographic Details
Published in:Electronics (Basel) 2023-05, Vol.12 (11), p.2345
Main Authors: Dang, Son Tung, Dinh, Xuan Minh, Kim, Thai Dinh, Xuan, Hai Le, Ha, Manh-Hung
Format: Article
Language:English
Subjects:
Adaptive control
Autonomous cars
Closed loop systems
Control algorithms
Controllers
Deep learning
Fuzzy logic
Kalman filters
Mathematical models
Methods
Neural networks
Obstacle avoidance
Parameters
Radial basis function
Restrictions
Robot control
Robot dynamics
Robots
Sliding mode control
Trajectories
Vehicles
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Summary:This paper proposes a new adaptive controller for three-wheeled mobile robots (3WMRs) called the ABHSMC controller. This ABHSMC controller is developed through a cooperative approach, combining a backstepping controller and a Radial Basis Function (RBF) neural network-based Hierarchical Sliding Mode Controller (HSMC). Notably, the RBF neural network exhibits the remarkable capability to estimate both the uncertainty components of the model and systematically adapt its parameters, leading to enhanced output trajectory responses. A novel navigational model, constructed by the connection to the adaptive BHSMC controller, Timed Elastic Band (TEB) Local Planner, and A-star (A*) Global Planner, is called ABHSMC navigation stack, and it is applied to effectively solve the tracking issue and obstacle avoidance for the 3-Wheeled Mobile Robot (3WMR). The simulation results implemented in the Matlab/Simulink platform demonstrate that the 3WMRs can precisely follow the desired trajectory, even in the presence of disturbances and changes in model parameters. Furthermore, the controller’s reliability is endorsed on our constructed self-driving car model. The achieved experimental results indicate that the proposed navigational structure can effectively control the actual vehicle model to track the desired trajectory with a small enough error and avoid a sudden obstacle simultaneously.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12112345