https://www.heemels.tue.nl/content/papers/BruHee_ECC14a.pdf

Robust self-triggered MPC for constrained linear systems

Model Reduction For Control System Design

Industrial wireless sensor and actuator networks in industry 4.0: Exploring requirements, protocols, and challenges—A MAC survey

We consider a networked control system where a plant is connected to a remote controller via a shared network that allows only one user to transmit at a given time. At each transmission time, the controller decides between sampling one of the plant's sensors or transmitting control data to the plant. We tackle the problem of simultaneously designing a policy for scheduling decisions and a policy for control inputs so as to optimize a quadratic objective. Using the framework of dynamic programming, we propose a rollout strategy by which the scheduling and control decisions are determined at each transmission time as the ones that lead to optimal performance over a given horizon assuming that from then on controller and sensors transmit in a periodic order and the control law is a standard optimal law for periodic systems. We show that this rollout strategy results in a protocol where scheduling decisions are based on the state estimate and error covariance matrix of a Kalman estimator, and must be determined on-line. We contrast the solution to this problem with the solution to the seemingly similar sensor scheduling problem where optimal scheduling decisions can be determined off-line. We highlight how the protocol obtained from the rollout algorithm can be implemented in a distributed way in broadcast networks. Moreover, it follows by construction of rollout algorithms that our proposed scheduling method can outperform any periodic scheduling of transmissions.

Scheduling measurements and controls over networks — Part II: Rollout strategies for simultaneous protocol and controller design

wirelessHART in der Prozessregelung

Wireless networks in industrial process control enable new system architectures and designs. However, wireless control systems can be severely affected by the imperfections of the communication links. This thesis proposes new methods to handle such imperfections by adding additional components in the control loop, or by adapting sampling intervals and control actions.First, the predictive outage compensator is proposed. It is a filter which is implemented at the receiver side of networked control systems. There it generates predicted samples when data are lost, based on past data. The implementation complexity of the predictive outage compensator is analyzed. Simulation and experimental results show that it can considerably improve the closed-loop control performance under communication losses.The thesis continues with presenting an algorithm for controlling multiple processes on a shared communication network, using adaptive sampling intervals. The methodology is based on model predictive control, where the controller jointly decides the optimal control signal to be applied as well as the optimal time to wait before taking the next sample. The approach guarantees conflict-free network transmissions for all controlled processes. Simulation results show that the presented control law reduces the required amount of communication, while maintaining control performance.The third contribution of the thesis is an event-triggered model predictive controller for use over a wireless link. The controller uses open-loop optimal control, re-computed and communicated only when the system behavior deviates enough from a prediction. Simulations underline the methods ability to significantly reduce computation and communication effort, while guaranteeing a desired level of system performance.The thesis is concluded by an experimental validation of wireless control for a physical lab process. A hybrid model predictive controller is used, connected to the physical system through a wireless medium. The results reflect the advantages and challenges in wireless control.

http://kth.diva-portal.org/smash/get/diva2:697129/FULLTEXT01.pdf

Predictive Control for Wireless Networked Systems in Process Industry

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Predictive Control for Wireless Networked Systems in Process Industry