Brian Douglas, a control systems engineer based in Seattle, has created various resources over the years and invites visitors to explore them.
- Linear (PID, lead-lag, full-state feedback) Nonlinear (gain scheduling, backstepping, sliding mode) Optimal & Predictive Control LQR, MPC, Hamilton-Jacobi-Bellman
- Intelligent & Multi-Agent Control Reinforcement learning, genetic algorithms, swarm control
- Robust & Adaptive Control
- H-infinity, mu-synthesis, active disturbance rejection, model reference adaptive control
- Planning & Estimation
- Path planning (RRT, A*), Kalman filters, sensor fusion
- Modeling, Simulation & System Analysis
- State-space, transfer functions, stability analysis, Nyquist, Lyapunov
This article explores the application of reinforcement learning (RL) to Partial Differential Equations (PDEs), highlighting the complexity and challenges involved in controlling systems described by PDEs compared to Ordinary Differential Equations (ODEs). It discusses various approaches, including genetic programming and neural network-based methods, and presents experimental results on controlling PDE systems like the diffusion equation and Kuramoto–Sivashinsky equation. The author emphasizes the potential of machine learning to improve understanding and control of PDE systems, which have wide-ranging applications in fields like fluid dynamics, thermodynamics, and engineering.
