Computationally Efficient Chance Constrained Covariance Control with Output Feedback
arxiv(2023)
摘要
This paper studies the problem of developing computationally efficient
solutions for steering the distribution of the state of a stochastic, linear
dynamical system between two boundary Gaussian distributions in the presence of
chance-constraints on the state and control input. It is assumed that the state
is only partially available through a measurement model corrupted with noise.
The filtered state is reconstructed with a Kalman filter, the chance
constraints are reformulated as difference of convex (DC) constraints, and the
resulting covariance control problem is reformulated as a DC program, which is
solved using successive convexification. The efficiency of the proposed method
is illustrated on a double integrator example with varying time horizons, and
is compared to other state-of-the-art chance constrained covariance control
methods.
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