Probabilistic inequality constraints in stochastic optimal control theory

AbstractThere are many optimal control problems in which it is necessary or desirable to constrain the range of values of state variables. When stochastic inputs are involved, these inequality constraint problems are particularly difficult. Sometimes the constraints must be modeled as hard constraints which can never be violated, and other times it is more natural to prescribe a probability that the constraints will not be violated. This paper treats general problems of the latter type, in which probabilistic inequality constraints are imposed on the state variables or on combinations of state and control variables. A related class of problems in which the state is required to reach a target set with a prescribed probability is handled by the same methods. It is shown that the solutions to these problems can be obtained by solving a comparatively simple bilinear deterministic control problem