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

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.Comment: Accepted for publication in IEEE Transactions on Control Systems Technolog

Control and Communication-Schedule Co-design For Networked Control Systems

In a networked control system (NCS), the control loop is closed through a communication medium. This means that sensor measurements and/or control signals can be exchanged through a communication link. NCSs have many benefits, such as wiring reduction (elimination in the case of wireless communication), installation cost reduction, and simplification of upgrades and restructuring. However, network congestion, impairments of the wireless links (such as bandwidth limitations, packet losses, delays, and noises) may degrade system performance and even cause instability. These issues have motivated a great deal of research over the past 20 years and have given rise to a number of approaches to prevent congestion and compensate for delays and/or packet losses.An interesting class of NCSs that has not received enough attention is an NCS whose systems are uncertain and subject to state and inputs hard constraints.These hard constraints may stem from the system itself, its environment, or be proposed by the designer in order to guarantee safety or a certain performance.The contribution of this thesis is introducing a design framework that guarantees robust constraint satisfaction for a class of multi-agent NCSs with a shared communication medium that is subject to bandwidth limitation and prone to packet losses.The proposed framework is built upon reachability analysis to determine the communication demand for each system such that local constraints are satisfied and scheduling techniques to guarantee satisfaction of the communication demands. The thesis explores offline and online scheduling designs under various communication topologies, optimal control designs under state and output feedback, and scheduling and control co-design for NCSs with hard constraints

Sparse and Constrained Stochastic Predictive Control for Networked Systems

This article presents a novel class of control policies for networked control of Lyapunov-stable linear systems with bounded inputs. The control channel is assumed to have i.i.d. Bernoulli packet dropouts and the system is assumed to be affected by additive stochastic noise. Our proposed class of policies is affine in the past dropouts and saturated values of the past disturbances. We further consider a regularization term in a quadratic performance index to promote sparsity in control. We demonstrate how to augment the underlying optimization problem with a constant negative drift constraint to ensure mean-square boundedness of the closed-loop states, yielding a convex quadratic program to be solved periodically online. The states of the closed-loop plant under the receding horizon implementation of the proposed class of policies are mean square bounded for any positive bound on the control and any non-zero probability of successful transmission