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