A Constrained Model-Predictive Approach to Coordinated Control

We consider constrained Model Predictive Control (MPC) as a local control law for application in coordinated control of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. Restricted communication bandwidth is available and forces the subsystems to avail themselves of limited-quality estimates of their neighbors’ states in computing this constrained control. Stability theorems are derived for the subsystems and illustrate the conditions required of the constraint set in order to guarantee local stability or convergence to a target state. If these conditions are met for all subsystems, then this stability is inherited by the overall system. Disturbance attenuation, or “string stability”, is studied in this framework and it is shown that inactivity of the MPC constraints implies stability. This then provides a connection between control objective, communications resource assignment and performance. A one-dimensional vehicle example is computed to crystallize ideas.