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Model-Predictive Control
Table of Contents
General Concept
Recipe for trajectory optimization.
- Observe the current state at time $t$.
- Plan a cost-minimizing trajectory of actions for time $t$ to $t+T$, where $T$ is a finite-time horizon.
- Execute only the first action of the trajectory. Repeat.
How to design for global stability with local optimizations?
Implementation
The simplest implementation of model-predictive control (MPC) is probably random shooting MPC. Random shooting MPC generates candidate trajectories by randomly sampling the action space, then selecting the cost-minimizing trajectory from the candidates. There are smarter things that you could do instead, like the cross-entropy method.
Limitations
Planning trajectories requires dynamics model, like a physics engine. This additional complexity can be overkill for simple systems. Furthermore, dependence on a good dynamics model means MPC can be difficult to implement.
Planning over a receding time horizon is also a feature of action chunking policies.1