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Neural network-based motion control of an underactuated wheeled inverted pendulum model.
IEEE Transactions on Neural Networks and Learning Systems
|October 21, 2014
Summary
This study develops an adaptive neural network control for wheeled inverted pendulum (WIP) models, ensuring stable motion control for two-wheeled vehicles. The method guarantees precise pendulum tilt angle tracking through subsystem coupling.
Area of Science:
- Robotics and Control Systems
- Artificial Intelligence in Engineering
- Vehicle Dynamics
Background:
- Wheeled Inverted Pendulum (WIP) models are crucial for simulating two-wheeled vehicles.
- Underactuated systems present significant control challenges.
- Adaptive control strategies are needed for systems with unknown dynamics.
Purpose of the Study:
- To investigate automatic motion control for a specific WIP model.
- To develop an adaptive neural network (NN) control scheme for a fully actuated subsystem.
- To indirectly control the non-actuated pendulum subsystem via dynamic coupling.
Main Methods:
- Decomposition of the WIP model into fully actuated (Σa) and non-actuated (Σb) subsystems.
- Application of an adaptive NN for motion control of subsystem Σa, leveraging its universal approximation capabilities.
- Utilizing a model reference approach optimized with finite-time linear quadratic regulation.
- Indirect control of subsystem Σb through dynamic coupling with subsystem Σa's planar motion.
Main Results:
- Successful implementation of an adaptive NN control scheme for the actuated subsystem.
- Demonstrated indirect control of the pendulum motion, achieving satisfactory tracking of a set tilt angle.
- Validation of the control strategy through rigorous theoretical analysis and simulation studies.
Conclusions:
- The developed adaptive NN control method effectively manages motion control for WIP models.
- The approach ensures precise pendulum tilt angle tracking by exploiting subsystem dynamic coupling.
- This research provides a robust framework for controlling complex underactuated robotic systems.
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