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This paper studies the problem of developing computationally efficientsolutions for steering the distribution of the state of a stochastic, lineardynamical system between two boundary Gaussian distributions in the presence ofchance-constraints on the state and control input. It is assumed that the stateis only partially available through a measurement model corrupted with noise.The filtered state is reconstructed with a Kalman filter, the chanceconstraints are reformulated as difference of convex (DC) constraints, and theresulting covariance control problem is reformulated as a DC program, which issolved using successive convexification. The efficiency of the proposed methodis illustrated on a double integrator example with varying time horizons, andis compared to other state-of-the-art chance constrained covariance controlmethods.

Computationally Efficient Chance Constrained Covariance Control with Output Feedback