Paper in proceedings, 2017

We consider state feedback of stochastic dynamic systems. Optimal control of such systems in general is acknowledged to be difficult, and in particular so when there are state constraints. The common way to solve the problem is to numerically solve the corresponding Hamilton-Jacobi-Bellman (HJB) equation and the main difficulty then is that the state constraints translate into infinite boundary conditions. In a series of work we have used transformations of the HJB equation to get around this problem, each having its limitations. The method to solve the most general problem (Rutquist et al., 2014), however, comes at the cost of having to solve n2 + 1 partial differential equations (PDEs), where n is the number of states. Rather than starting from the Hamilton-Jacobi-Bellman equation we now start from the Fokker-Planck equation, and compute the optimal control policy numerically. Then only one PDE needs to be solved and infinite boundary conditions are still avoided. Preliminary testing indicates that this method is not only much faster but also more robust.

OptimalControl

Hamilton-Jacobi-Bellman Equation

StochasticSystems

StateConstraints

Fokker-PlanckEquation

Electrical Engineering

Chalmers, Signals and Systems, Systems and control, Automatic Control

Basic sciences

Electrical Engineering, Electronic Engineering, Information Engineering

Control Engineering

10.1016/j.ifacol.2017.08.893