The goal is to build a flywheel inverted pendulum (FIP) model stabilized by a neural network trained by deep reinforcement learning method. Literally, the network will not know anything about the physics of the phenomena and about it's own physical "body", it will use a method of trial and error in order to figure out how to get to upright position.
- Get enough understanding of a physics model
- Simulate free-fall system with spontaneous rotation of a wheel
- Implement a balancing algorithm in a simulation
- Implement a swing-up algorithm in a simulation
- Build a physical model with stepper motor for rotating a wheel
- Try other controller to stabilize the system (TBD)
- Learn DRL enough to apply to the system
- Pre-train a NN in a simulation and use it inside a real device
- Allow NN to tune itself in a real device
- DESIGN AND CONTROL OF A FLYWHEEL INVERTED PENDULUM SYSTEM'16
- Design of a Flywheel-Controlled Inverted Pendulum'19
- Flywheel Inverted Pendulum Design for Evaluation of Swing-Up Energy-Based Strategies'20
- Global Stabilization of a Reaction Wheel Pendulum'20
- Inertia Wheel Inverted Pendulum'19
- Nonlinear control of the Reaction Wheel Pendulum'01
- Robust Control of the FIP System Considering Parameter Uncertainty'21
- Two-Axis Reaction Wheel Inverted Pendulum'17