Output-Feedback Safe Control of Discrete-Time Stochastic Systems with Chance Constraints
It addresses the safety-critical control problem for stochastic systems with partial observations, enabling real-time safety enforcement for autonomous systems operating under uncertainty.
This paper develops an output-feedback control barrier function framework for discrete-time stochastic systems with incomplete information, ensuring safety constraints via state estimates from noisy measurements. The approach achieves fast online computation while providing reliable safety performance under process and measurement noise.
In this paper, we investigate safety-critical control problem of discrete-time stochastic systems with incomplete information, where safety constraints must be enforced using state estimates obtained from noisy measurements. We develop an output-feedback control barrier function (CBF) framework based on an expectation-based discrete-time barrier condition that explicitly incorporates estimation uncertainty through the evolving belief over the state. To enable real-time implementation, we derive deterministic sufficient conditions that conservatively enforce the expectation-based CBF by bounding the expectation with computable functions of the belief statistics using Jensen inequalities. The resulting safety filter is formulated as a tractable optimization problem compatible with standard online controllers. Numerical simulations demonstrate that the proposed output-feedback approach achieves fast online computation while providing reliable safety performance in the presence of process noise and measurement uncertainty.