Stochastic stability and performance estimates of packetized unconstrained model predictive control for networked control systems

In this work, we consider the control of discrete-time nonlinear systems over unreliable packet-based communication networks subject to random packet-dropouts. In order to mitigate the influence of the packet dropouts, the controller transmits packets containing control inputs for more than one future time instant. A suitable buffering is then applied at the plant actuator side. Since we do not assume the number of consecutive packet dropouts to be bounded, we are interested in stochastic stability of the closed-loop. For the calculation of the control inputs, we propose an unconstrained model predictive control (MPC) scheme without additional terminal weighting term. This unconstrained MPC scheme shows two significant advantages. First, we do not require the knowledge of a global control Lyapunov function, but instead only a less restrictive controllability assumption, in order to guarantee stochastic stability. Second, guaranteed performance bounds on the expected infinite horizon cost of the closed-loop can be obtained.