3 research outputs found
Learning-based predictive control for linear systems: a unitary approach
A comprehensive approach addressing identification and control for
learningbased Model Predictive Control (MPC) for linear systems is presented.
The design technique yields a data-driven MPC law, based on a dataset collected
from the working plant. The method is indirect, i.e. it relies on a model
learning phase and a model-based control design one, devised in an integrated
manner. In the model learning phase, a twofold outcome is achieved: first,
different optimal p-steps ahead prediction models are obtained, to be used in
the MPC cost function; secondly, a perturbed state-space model is derived, to
be used for robust constraint satisfaction. Resorting to Set Membership
techniques, a characterization of the bounded model uncertainties is obtained,
which is a key feature for a successful application of the robust control
algorithm. In the control design phase, a robust MPC law is proposed, able to
track piece-wise constant reference signals, with guaranteed recursive
feasibility and convergence properties. The controller embeds multistep
predictors in the cost function, it ensures robust constraints satisfaction
thanks to the learnt uncertainty model, and it can deal with possibly
unfeasible reference values. The proposed approach is finally tested in a
numerical example
On multi-step prediction models for receding horizon control
The derivation of multi-step-ahead prediction models from sampled data of a
linear system is considered. A dedicated prediction model is built for each
future time step of interest. In addition to a nominal model, the set of all
models consistent with data and prior information is derived as well, making
the approach suitable for robust control design within a Model Predictive
Control framework. The resulting parameter identification problem is solved
through a sequence of convex programs, overcoming the non-convexity arising
when identifying 1-step prediction models with an output-error criterion. At
the same time, the derived models guarantee a worst-case error which is always
smaller than the one obtained by iterating models identified with a 1-step
prediction error criterion.Comment: This manuscript contains technical details of recent results
developed by the authors on learning-based model predictive control for
linear time invariant system