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The advent of networked control systems urges the digital control design to incorporate communication constraints efficiently. In order to accommodate this requirement, this article studies the joint design of controller and event-trigger for linear stochastic systems in the presence of a resource-limited communication channel which exhibits packet dropouts and time-delay. The event-trigger situated at the sensor decides at every sampling instance, whether to send information over the communication channel to the controller. The design approach is formulated as a stochastic average-cost optimization problem, where the communication constraints are reflected as an additional cost penalty of the average transmission rate. Different conditions on the communication model are given where the joint optimal design can be split into a separate control and event-trigger design. Based on these results, two suboptimal design approaches are developed. By using drift criteria, stability guarantees of the closed-loop system for both approaches are derived in terms of bounded moment stability. Numerical simulations illustrate the efficacy of the event-triggered approach compared with optimal time-triggered controllers. [ABSTRACT FROM AUTHOR]

Audiovisual communications is at the core of multimedia systems that allow users to interact across distances. It is common understanding that both audio and video are required for high-quality interaction. While audiovisual information provides a user with a satisfactory impression of being present in a remote environment, physical interaction and manipulation is not supported. True immersion into a distant environment and efficient distributed collaboration require the ability to physically interact with remote objects and to literally get in touch with other people. Touching and manipulating objects remotely becomes possible if we augment traditional audiovisual communications by the haptic modality. Haptic communications is a relatively young field of research that has the potential to substantially improve human-human and human-machine interaction. In this paper, we discuss the state-of-the-art in haptic communications both from psychophysical and technical points of view. From a human perception point of view, we mainly focus on the multimodal integration of video and haptics and the improved performance that can be achieved when combining them. We also discuss how the human adapts to discrepancies and synchronization errors between different modalities, a research area which is typically referred to as perceptual learning. From a technical perspective, we address perceptual coding of haptic information and the transmission of haptic data streams over resource-constrained and potentially lossy networks in the presence of unpredictable and time-varying communication delays. In this context, we also discuss the need for objective quality metrics for haptic communication. Throughout the paper, we stress the fact that haptic communications is not meant as a replacement of traditional audiovisual communications but rather as an additional dimension for telepresence that will allow us to advance in our quest for truly immersive communication. [ABSTRACT FROM AUTHOR]

In the realm of the cooperative control of multiagent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound. Online learning, which involves incorporating newly acquired training data into GP models, promises to improve control performance by enhancing predictions during the operation. Therefore, this article investigates the online cooperative learning algorithm for MAS control. Moreover, an event-triggered data selection mechanism, inspired by the analysis of a centralized event-trigger (CET), is introduced to reduce the model update frequency and enhance the data efficiency. With the proposed learning-based control, the practical convergence of the MAS is validated with guaranteed tracking performance via the Lyapunov theory. Furthermore, the exclusion of the Zeno behavior for individual agents is shown. Finally, the effectiveness of the proposed event-triggered online learning method is demonstrated in simulations.