On multi-step prediction models for receding horizon control

TL;DR

This paper develops a robust multi-step prediction modeling approach for linear systems, enabling improved receding horizon control by deriving models that guarantee worst-case error bounds and are identified via convex optimization.

Contribution

It introduces a method to derive multi-step models from data, ensuring robustness and convexity in identification, advancing model predictive control techniques.

Findings

Models guarantee smaller worst-case errors than iterated 1-step models.

Parameter identification is achieved through convex programs.

Approach is suitable for robust control design in MPC.

Abstract

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.