Information-seeking polynomial NARX model-predictive control through expected free energy minimization
This work addresses control in uncertain environments for robotics or automation, but it appears incremental as it builds on existing model-predictive control and information-theoretic methods.
The paper tackles the problem of controlling systems with uncertain parameters by proposing an adaptive model-predictive controller that balances goal achievement and information-seeking, using an expected free energy functional with information-theoretic terms. Experiments on a pendulum swing-up task demonstrate how parameter uncertainty influences control objectives.
We propose an adaptive model-predictive controller that balances driving the system to a goal state and seeking system observations that are informative with respect to the parameters of a nonlinear autoregressive exogenous model. The controller's objective function is derived from an expected free energy functional and contains information-theoretic terms expressing uncertainty over model parameters and output predictions. Experiments illustrate how parameter uncertainty affects the control objective and evaluate the proposed controller for a pendulum swing-up task.