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Modern industrial control systems use a multitude of spatially distributed sensors and actuators to continuously monitor and control physical processes. Information exchange among control system components is traditionally done through physical wires. The need to physically wire sensors and actuators limits flexibility, scalability and reliability, since the cabling cost is high, cable connectors are prone to wear and tear, and connector failures can be hard to isolate. By replacing some of the cables with wireless communication networks, costs and risks of connector failures can be decreased, resulting in a more cost-efficient and reliable system. Integrating wireless communication into industrial control systems is challenging, since wireless communication channels introduce imperfections such as stochastic delays and information losses. These imperfections deteriorate the closed-loop control performance, and may even cause instability. In this thesis, we aim at developing design frameworks that take these imperfections into account and improve the performance of closed-loop control systems. The thesis first considers the joint design of packet forwarding policies and controllers for wireless control loops where sensor measurements are sent to the controller over an unreliable and energy-constrained multi-hop wireless network. For a fixed sampling rate of the sensor, the co-design problem separates into two well-defined and independent subproblems: transmission scheduling for maximizing the deadline-constrained reliability and optimal control under packet losses. We develop optimal and implementable solutions for these subproblems and show that the optimally co-designed system can be obtained efficiently. The thesis continues by examining event-triggered control systems that can help to reduce the energy consumption of the network by transmitting data less frequently. To this end, we consider a stochastic system where the communication between the controller and the actuator is triggered by a threshold-based rule. The communication is performed across an unreliable link that stochastically erases transmitted packets. As a partial protection against dropped packets, the controller sends a sequence of control commands to the actuator in each packet. These commands are stored in a buffer and applied sequentially until the next control packet arrives. We derive analytical expressions that quantify the trade-off between the communication cost and the control performance for this class of event-triggered control systems. The thesis finally proposes a supervisory control structure for wireless control systems with time-varying delays. The supervisor has access to a crude indicator of the overall network state, and we assume that individual upper and lower bounds on network time-delays can be associated to each value of the indicator. Based on this information, the supervisor triggers the most appropriate controller from a multi-controller unit. The performance of such a supervisory controller allows for improving the performance over a single robust controller. As the granularity of the network state measurements increases, the performance of the supervisory controller improves at the expense of increased computational complexity. / <p>QC 20150504</p>

In the uplink direction of 5G New Radio, signals are sent between Ra-dio Units and Digital Units. The production of these signals is non-deterministic, leading to signals often being produced in bursts. Thesesignal bursts can lead to exceeding the Data Link Layer capacity, whichcauses packet losses. It is possible to control the burstiness by delay-ing signals over time. However, excessive delays should be avoidedsince the processing of signals must be completed within strict time con-straints. In this paper, two machine-learning-based algorithms with theobjective of avoiding packet losses by introducing delays to signals wereproposed. One algorithm was based on the symbol number of the sig-nals, and the other one used a queue-based approach. Only the symbol-based algorithm was thoroughly evaluated. Visualizations of test data,as well as lab tests, showed that the symbol-based algorithm was ableto efficiently delay signals in order to reduce the maximum load on theData Link Layer.