We present an output feedback stochastic model predictive controller (SMPC)
for constrained linear time-invariant systems. The system is perturbed by
additive Gaussian disturbances on state and additive Gaussian measurement noise
on output. A Kalman filter is used for state estimation and an SMPC is designed
to satisfy chance constraints on states and hard constraints on actuator
inputs. The proposed SMPC constructs bounded sets for the state evolution and a
tube-based constraint tightening strategy where the tightened constraints are
time-invariant. We prove that the proposed SMPC can guarantee an infeasibility
rate below a user-specified tolerance. We numerically compare our method with a
classical output feedback SMPC with simulation results which highlight the
efficacy of the proposed algorithm.Comment: IEEE American Control Conference (ACC) 2023, May 31 - June 2, San
Diego, CA, US