Flight Control Projects
Explore guided student projects in Flight Control. Build hands-on skills with real aerospace tools and data.
4 projects
Combine with other filters →Land on the Moon with Reinforcement Learning
Train an AI agent to fire rocket thrusters and touch down safely—in simulation.
Use OpenAI Gymnasium's LunarLander-v3 environment and the Stable-Baselines3 library to train a reinforcement learning agent that learns to land a spacecraft through trial and error—no hand-coded control laws required.
Start Project →AI/MLJSBSim Reinforcement Learning: Build an Autopilot
Train an agent to fly a real aircraft model — six degrees of freedom included.
Build a JSBSim reinforcement learning autopilot with a custom Gymnasium environment and Stable-Baselines3. Compare PPO and SAC, plus the gym-jsbsim approach.
Start Project →AI/MLTrain a Drone to Hover with PyTorch RL
Implement PPO from scratch and watch a quadrotor learn to balance itself.
Implement the Proximal Policy Optimisation (PPO) algorithm from scratch in PyTorch and train a quadrotor to hover and track waypoints in a lightweight physics simulation. By building PPO yourself — actor-critic networks, GAE advantage estimation, clipped surrogate loss — you gain deep understanding of why modern RL algorithms work.
Start Project →AI/MLNeural Network Flight Controller
Replace a PID controller with a neural network trained on flight data
Train a neural network in TensorFlow to replace a classical PID flight controller for a fixed-wing aircraft. The network learns the control mapping from flight dynamics data and is evaluated on stability, disturbance rejection, and robustness compared to a tuned PID baseline.
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