Model Predictive Control (MPC) computes a feedback control by repeatedly solving (open loop) optimal control problems
Numerical Optimal ControlCoordination: Prof. Dr. Kurt Chudej
Contact: Prof. Dr. Kurt Chudej
Represented in MODUS since 2016
Members with experience in this area:
The method "Numerical Optimal Control":
How does it work?
Standard optimal control problems s.t. ode-constraints can be solved today efficently by direct methods. Thereby the optimal control problem is substituted by a big nonlinear optimization problem. The solution of coupled ode-pde optimal control problems is more challenging. Here a detailed (index) analysis is necessary for finding a suitable formulation, which can be used afterwards by a suitable numerical algorithm.
Where have we applied it?
The members of MODUS have in particular made important contributions by finding new theoretical results and also by solving complicated real-life optimal control problems from a variety of applications, e.g.:
- flight mechanics, e.g. trajectory optimiziation of a hypersonic space vehicle s.t. heat constraints in the form of a pde (optimal control of a coupled ode-pde system), trajectory optimization of a hang-glider (optimal control s.t. ode-constraint), simultaneous optimization of trajectory and staging of a hypersonic space vehicle (optimal control s.t. ode-constraint), trajectory optimization of a supersonic aircraft s.t. nonlinear state-constraints.
- biomathematics, e.g., optimal and suboptimal treatment strategies for mathematical cancer models, optimal vaccination strategies for dengue fever.
- modeling, simulation, index analysis and optimal control of a family of complicated nonlinear molten carbonate fuel cell models
- solution of various economic optimal control problems
- solution of various optimal control problems governed by partial differential equations