Lyapunov-Based Predictive Control Methodologies for Networked Control Systems

Abstract

The problem considered in this chapter is to control a vehicle drivetrain in order to minimize its oscillations while coping with the time-varying delays introduced by the CAN communication network and the strict timing limitations. As such, two Lyapunov-based model predictive control design methodologies are presented: one based on modeling the network-induced time-varying delays using a polytopic approximation technique and the second one based on modeling the delays as disturbances. Several tests performed using an industry validated drivetrain model indicate that the proposed design methodologies can handle both the performance/physical constraints and the strict limitations on the computational complexity, while effectively coping with the time-varying delays. Moreover, a comparative analysis between the two Lyapunov-based model predictive control design methodologies in terms of computational complexity, number of optimization variables, and obtained performances is carried out.