Modeling and predictive control for compensating network-induced time-varying delays

Abstract

The goal of this paper is to provide a control design methodology that can assure the closed-loop performances of a physical plant, while compensating the timevarying delays introduced by the communication network that links the controller with the remote process. Firstly, the error caused by the time-varying delays is modeled as a disturbance and a novel method of bounding the disturbances is proposed. Then, a robust one step ahead predictive controller based on flexible control Lyapunov functions is designed, which explicitly takes into account the bounds of the disturbances caused by time-varying delays and guarantees also the input-to-state stability of the system in a non-conservative way. Moreover, it is shown that by choosing an appropriately Lyapunov function, the MPC algorithm amounts solving a single, lowcomplexity linear program each sampling instant. The modeling method and the control strategy were tested on a vehicle drivetrain controlled through CAN, with the aim of damping driveline oscillations, which is crucial in improving driveability and passenger comfort. Several TrueTime simulations based on realistic scenarios show that the proposed control scheme can handle both the performance/physical constraints and the strict limitations on the computational complexity.

Suboptimal Solutions for Time Varying Time Delayed MPC Controllers

“…In the last years, MPC controllers are often studied for time delay in sense of network control [5] where feasible networked MPC scheme is used for discrete time interconnected systems on which time varying transmission delay affects the network [6] via minimizing the upper bound of the cost function it is used as a robust one in discrete-time uncertain systems with time-varying delay, input constraints and bounded nonlinear perturbations. Reference [7] discusses delay compensation of the moving horizon estimator and aims to compensate the loss by updating the covariance matrix for those states if there is incoming data from the sensors for them.…”