Modern wireless systems can accurately estimate network latencies; this information could help design better controllers for systems prone to large delays. In this paper, we consider the problem of event-triggered control of linear networked systems with known input delays and address it via hybrid system approach. Specifically, we design a predictor-based state observer that incorporates input delay and propagated outputs to estimate the future state of the system. Subsequently, this estimated state is utilized to generate control inputs for transmissions over the network. In order to reduce the number of transmissions, we design a dynamic event-triggering mechanism (ETM) which makes decisions on whether or not to transmit the control inputs at pre-defined instants. The ETM, by its design, is devoid of Zeno behavior.

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.

This paper considers dynamic periodic eventtriggered control for nonlinear networked control systems. A model-based periodic event-triggering mechanism is proposed to potentially reduce the consumption of transmission resources for the networked control systems that are subject to time-varying inter-sampling intervals, transmission delays, and scheduling protocols. Furthermore, to compensate for the adverse effects of delays, the controller node is equipped with two specialized units: a propagation unit and a model unit. The role of propagation units is to work with the delayed data, using the data to estimate its current value and subsequently updating the state within the model unit. A predictor unit is employed at the sensor node to configure event-triggering conditions. A hybrid system framework is used to model the networked control systems. Moreover, sufficient conditions on the transmission intervals, delays and dynamic event-triggered control are given to ensure closed-loop asymptotic stability. Additionally, an allocation framework of the event-triggering mechanism is proposed, taking into account the information available at the sensor node. Finally, an example of a singlelink robot arm is simulated to illustrate the effectiveness and feasibility of the theoretical results.