Multiple Loop Self-Triggered Model Predictive Control for Network Scheduling and Control

TL;DR

This paper introduces a model predictive control algorithm for managing multiple processes over a shared network, enabling adaptive sampling and conflict-free communication to improve system stability and efficiency.

Contribution

It proposes a novel centralized MPC scheme that adaptively adjusts sampling intervals and ensures conflict-free network scheduling for multiple control processes.

Findings

Adaptive sampling increases as system state norm decreases.

Explicit stabilizing conditions are provided.

Simulation shows improved performance over periodic sampling.

Abstract

We present an algorithm for controlling and scheduling multiple linear time-invariant processes on a shared bandwidth limited communication network using adaptive sampling intervals. The controller is centralized and computes at every sampling instant not only the new control command for a process, but also decides the time interval to wait until taking the next sample. The approach relies on model predictive control ideas, where the cost function penalizes the state and control effort as well as the time interval until the next sample is taken. The latter is introduced in order to generate an adaptive sampling scheme for the overall system such that the sampling time increases as the norm of the system state goes to zero. The paper presents a method for synthesizing such a predictive controller and gives explicit sufficient conditions for when it is stabilizing. Further explicit conditions are given which guarantee conflict free transmissions on the network. It is shown that the optimization problem may be solved off-line and that the controller can be implemented as a lookup table of state feedback gains. Simulation studies which compare the proposed algorithm to periodic sampling illustrate potential performance gains.