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This paper presents an algorithm to apply nonlinear control design approachesin the case of stochastic systems with partial state observation. Deterministicnonlinear control approaches are formulated under the assumption of full stateaccess and, often, relative degree one. We propose a control design approachthat first generates a control policy for nonlinear deterministic models withfull state observation. The resulting control policy is then used to build animportance-like probability distribution over the space of control sequenceswhich are to be evaluated for the true stochastic and state-uncertain dynamics.This distribution serves in the sampling step within a random search controloptimization procedure, to focus the exploration effort on certain regions ofthe control space. The sampled control sequences are assigned costs determinedby a prescribed finite-horizon performance and safety measure, which is basedon the stochastic dynamics. This sampling algorithm is parallelizable and shownto have computational complexity indifferent to the state dimension, and to beable to guarantee safety over the prescribed prediction horizon. A numericalsimulation is provided to test the applicability and effectiveness of thepresented approach and compare it to a certainty equivalence controller.

A Control Approach for Nonlinear Stochastic State Uncertain Systems with Probabilistic Safety Guarantees