Iterative-Learning Control (ILC) for linear and nonlinear systems.
ILC can be thought of as either
- a simple reinforcement-learning (RL) strategy, or
- a method to solve open-loop optimal control problems.
ILC is suitable in situations where a repetitive task is to be performed multiple times, and disturbances acting on the system are also repetitive and predictable but may be unknown. Multiple versions of ILC exists, of which we support a few that are listed below. When ILC iterations are performed by running experiments on a physical system, ILC resembles episode-based reinforcement learning (or adaptive control), while if a model is used to simulate the experiments, we can instead think of ILC as a way to solve optimal control problems (trajectory optimization).
See the documentation for more details.