Multiple Model Robust Dynamic Programming

Modeling error is a common problem for modelbased control techniques. We present multiple model dynamic programming (MMDP) as a method to generate controllers that are robust to modeling error. Our method generates controllers that are approximately optimal for a collection of models, thereby forcing the controller to be less model-dependent. We compare MMDP to stochastic dynamic programming, minimax dynamic programming, and a baseline implementation of dynamic programming on the test problem of pendulum swing-up. We simulate modeling error by varying model parameters.