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A method is developed to numerically solve chance constrained optimal controlproblems. The chance constraints are reformulated as nonlinear constraints thatretain the probability properties of the original constraint. The reformulationtransforms the chance constrained optimal control problem into a deterministicoptimal control problem that can be solved numerically. The new methoddeveloped in this paper approximates the chance constraints using Markov ChainMonte Carlo (MCMC) sampling and kernel density estimators whose kernels haveintegral functions that bound the indicator function. The nonlinear constraintsresulting from the application of kernel density estimators are designed withbounds that do not violate the bounds of the original chance constraint. Themethod is tested on a non-trivial chance constrained modification of a softlunar landing optimal control problem and the results are compared with resultsobtained using a conservative deterministic formulation of the optimal controlproblem. The results show that this new method efficiently solves chanceconstrained optimal control problems.

Method for Chance Constrained Optimal Control Using Biased Kernel Density Estimators