Data-driven control design for nonlinear systems

Standard approaches to control are based on first-principle models: they assume that a somewhat accurate physical model describing the dynamics of the plant to control is available. However, in many real-world applications, deriving an accurate model is an extremely difficult task, since the system dynamics is not well known and/or too complex. In this activity, a polynomial model predictive control (PMPC) approach for nonlinear systems is being developed, relying on the identification from data of polynomial prediction models. The main advantages of this approach over the standard methods are that it does not require a detailed knowledge of the plant to control and it is computationally efficient.

ERC sector:  

  • PE7_1 Control engineering
  • PE7_3 Simulation engineering and modelling
  • PE1_19 Control theory and optimisation

Free keywords:               

  • System identification
  • Data-driven control design, nonlinear systems

Research groups