Stochastic output feedback MPC with intermittent observations

• Main Authors: Cannon, M. (Author), Goulart, P.J (Author), Yan, S. (Author)
• Format: Article
• Language: English
• Published: Elsevier Ltd 2022
• Subjects: Chance constraint; Chance constraints; Constraint Satisfaction; Convex optimisation; Convex optimization; Discounted costs; Feedback; Linear systems; Model predictive control; Model-predictive control; Optimal control systems; Output feedback; Output-feedback; Packet drops; Packet loss; Predictive control systems; Quadratic programming; Sensors data; Stochastic control systems; Stochastic models; Stochastic systems; Stochastics; Uncertainty; Uncertainty analysis
• Online Access: View Fulltext in Publisher

 This paper designs a model predictive control (MPC) law for constrained linear systems with stochastic additive disturbances and noisy measurements, minimising a discounted cost subject to a discounted expectation constraint. It is assumed that sensor data is lost with a known probability. Taking into account the data losses modelled by a Bernoulli process, we parameterise the predicted control policy as an affine function of future observations and obtain a convex linear-quadratic optimal control problem. Constraint satisfaction and a discounted cost bound are ensured without imposing bounds on the distributions of the disturbance and noise inputs. In addition, the average long-run undiscounted closed loop cost is shown to be finite if the discount factor takes appropriate values. We analyse robustness of the proposed control law with respect to possible uncertainties in the arrival probability of sensor data and we bound the impact of these uncertainties on constraint satisfaction and the discounted cost. Numerical simulations are provided to illustrate these results. © 2022 Elsevier Ltd