A Heuristic for Dynamic Output Predictive Control Design for Uncertain Nonlinear Systems
This work addresses the problem of designing predictive controllers for uncertain nonlinear systems, offering a more efficient approach for control engineers.
This paper proposes a heuristic for designing uncertainty-aware predictive controllers for nonlinear systems with uncertain parameters. The method approximates ideal deterministic MPC solutions using machine learning, achieving up to 78% of the advantage of perfect parameter knowledge compared to nominal design.
In this paper, a simple heuristic is proposed for the design of uncertainty aware predictive controllers for nonlinear models involving uncertain parameters. The method relies on Machine Learning-based approximation of ideal deterministic MPC solutions with perfectly known parameters. An efficient construction of the learning data set from these off-line solutions is proposed in which each solution provides many samples in the learning data. This enables a drastic reduction of the required number of Non Linear Programming problems to be solved off-line while explicitly exploiting the statistics of the parameters dispersion. The learning data is then used to design a fast on-line output dynamic feedback that explicitly incorporate information of the statistics of the parameters dispersion. An example is provided to illustrate the efficiency and the relevance of the proposed framework. It is in particular shown that the proposed solution recovers up to 78\% of the expected advantage of having a perfect knowledge of the parameters compared to nominal design.