An event-triggered MPC scheme for discrete pLPV systems: Application in auto-scaling of compute nodes in a fog cluster

This paper proposes a control-theoretic approach for discrete polytopic linear parameter varying (pLPV) systems with time-varying characteristics. The approach employs event-triggered model predictive control (ET-MPC) to uphold the performance while minimising the frequency of controller updates. Initially, a model predictive control (MPC) is designed to predict the system’s dynamics in the future. An event-triggering mechanism is also incorporated in the design, which uses a parameter-varying Lyapunov function to lessen the burden of online optimization. The design of an ET-MPC is based on the global stability and feasibility of the pLPV system. Finally, the control-theoretic approach is experimentally validated on a distributed fog computing platform to enable the auto-scaling of compute nodes for mobile robot vision applications.