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

Demilitarized network to secure the data stored in industrial networks

Currently, the data and variables of a control system are the most important elements to be safeguarded in an industrial network, so it is vitally important to ensure their safety. This paper presents the design and simulation of a demilitarized network (DMZ) using firewalls to control access to all the information that is stored in the servers of the industrial network of the Hermanos Díaz Refinery in Santiago de Cuba, Cuba. In addition, the characteristics, configurations, methods, and rules of DMZs and firewalls are shown, select the configuration with three multi-legged firewalls as the most appropriate for our application, since it allows efficient exchange of data guaranteeing security and avoiding the violation of the control system. Finally, the simulation of the proposed network is carried out

Impact of Communication Frequency on Remote Control of Automated Vehicles

This paper investigates the impact of the communication frequency on the remote control of automated vehicles. In particular, we consider a remote controller, which receives vehicles\u27 state information and issues control commands based on a model predictive control (MPC) framework, to steer the vehicles to reach their respective target position intervals at given specific times. We present a framework where both state information (from the vehicles to the controller) and control actions (from the controller to the vehicles) are communicated through a wireless network. Due to limited communication resources and possible channel impairments, information is not necessarily always provided to the destination (either the controller or the vehicles). Herein, we particularly focus on the communications to the controller and investigate the effect of frequency and last instant of communication. Our results quantify the impact of these factors on the system performance, and subsequently, underline the need for an efficient resource allocation scheme

Optimal scheduling and control for constrained multi-agent networked control systems

In this paper, we study optimal control and communication schedule co-design for multi-agent networked control systems, with assuming shared parallel communication channels and uncertain constrained linear time-invariant discrete-time systems. To that end, we specify the communication demand for each system using an associated robust control invariant set and reachability analysis. We use these communication demands and invariant sets to formulate tube-based model predictive control and offline/online communication schedule co-design problems. Since the scheduling part includes an infinite dimension integer problem, we propose heuristics to find suboptimal solutions that guarantee robust constraints satisfaction and recursive feasibility. The effectiveness of our approach is illustrated through numerical simulations

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 not only computes at every sampling instant 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 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. This 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 that guarantee conflict free transmissions on the network. It is shown that the optimization problem may be solved offline and that the controller can be implemented as a lookup table of state feedback gains. The simulation studies which compare the proposed algorithm to periodic sampling illustrate potential performance gains