Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes

Optimization of experiments for nonlinear dynamic processes to maximize the reliability of parameter estimates subject to cost and other inequality-constraints leads to very complex optimal control problems. First, the objective function already depends on a generalized inverse of the Jacobian of the underlying parameter estimation problem. Second, optimization results depend on the assumed parameter values which are only known to lie in a confidence region. Hence robust optimal experiments are required. New efficient methods and numerical results are presented.