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Neural network adaptive sliding mode control for omnidirectional vehicle with uncertainties
Xingyang Lu1, Xiangyin Zhang2, Guoliang Zhang2
1Faulty of Information Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China; Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China.
ISA Transactions
|November 14, 2018
Summary
A novel neural network adaptive sliding mode control (NNASMC) method enhances omnidirectional vehicle control. This approach ensures stable and robust performance against uncertainties and disturbances, outperforming traditional controllers.
Area of Science:
- Robotics
- Control Systems Engineering
- Artificial Intelligence
Background:
- Omnidirectional vehicles with Mecanum wheels offer superior maneuverability in confined spaces.
- Traditional control methods like PID and SMC face challenges with uncertainties and external disturbances.
- Accurate modeling of vehicle dynamics is crucial for effective control design.
Purpose of the Study:
- To propose a novel Neural Network Adaptive Sliding Mode Control (NNASMC) method for omnidirectional vehicles.
- To enhance control system robustness and stability in the presence of uncertainties and external disturbances.
- To validate the effectiveness of the NNASMC method through simulations and experiments.
Main Methods:
- Development of kinematic and dynamic models for the omnidirectional vehicle.
- Design of an inner-loop controller using Sliding Mode Control (SMC).
- Integration of an Artificial Neural Network (ANN) based adaptive law for uncertainty estimation.
- Stability and robustness analysis using Lyapunov theory.
Main Results:
- The NNASMC method demonstrated superior performance compared to classical PID and SMC controllers.
- Simulations and platform experiments confirmed the effectiveness and robustness of the NNASMC method.
- The ANN-based adaptive law successfully modeled and estimated uncertainties and disturbances.
Conclusions:
- The proposed NNASMC method provides a stable and robust control solution for omnidirectional vehicles.
- NNASMC effectively handles system uncertainties and external disturbances, improving dynamic performance.
- This research contributes a significant advancement in intelligent control strategies for mobile robotic platforms.
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