Adaptive Real-Time Communication for Wireless Cyber-Physical Systems

Learning dynamical systems in noise using convolutional neural networks

The problem of distinguishing deterministic chaos from non-chaotic dynamics has been an area of active research in time series analysis. Since noise contamination is unavoidable, it renders deterministic chaotic dynamics corrupted by noise to appear in close resemblance to stochastic dynamics. As a result, the problem of distinguishing noise-corrupted chaotic dynamics from randomness based on observations without access to the measurements of the state variables is difficult. We propose a new angle to tackle this problem by formulating it as a multi-class classification task. The task of classification involves allocating the observations/measurements to the unknown state variables in order to find the nature of these unobserved internal state variables. We employ signal and image processing based methods to characterize the different system dynamics. A deep learning technique using a state-of-the-art image classifier known as the Convolutional Neural Network (CNN) is designed to learn the dynamics. The time series are transformed into textured images of spectrogram and unthresholded recurrence plot (UTRP) for learning stochastic and deterministic chaotic dynamical systems in noise. We have designed a CNN that learns the dynamics of systems from the joint representation of the textured patterns from these images, thereby solving the problem as a pattern recognition task. The robustness and scalability of our approach is evaluated at different noise levels. Our approach demonstrates the advantage of applying the dynamical properties of chaotic systems in the form of joint representation of UTRP images along with spectrogram to improve learning dynamical systems in colored noise.

Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees

Closing feedback loops fast and over long distances is key to emerging cyber-physical applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless communication technology is preferred for its low cost, small form factor, and flexibility, especially if the devices support multi-hop communication. Thus far, however, feedback control over multi-hop low-power wireless networks has only been demonstrated for update intervals on the order of seconds. To fill this gap, this article presents a wireless embedded system that supports dynamic mode changes and tames imperfections impairing control performance (e.g., jitter and message loss), and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics in the presence of mode changes. Using experiments on a cyber-physical testbed with 20 wireless devices and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination with mode changes over multi-hop networks for update intervals of 20 to 50 milliseconds.

The AirTight Protocol for Mixed Criticality Wireless CPS

This article describes the motivation, design, analysis, and configuration of the criticality-aware multi-hop wireless communication protocol AirTight. Wireless communication has become a crucial part of the infrastructure of many cyber-physical applications. Many of these applications are real-time and also mixed-criticality, in that they have components/subsystems with different consequences of failure. Wireless communication is inevitably subject to levels of external interference. In this article, we represent this interference using a criticality-aware fault model; for each level of temporal interference in the fault model, we guarantee the timing behaviour of the protocol (i.e., we guarantee that packet deadlines are satisfied for certain levels of criticality). Although a new protocol, AirTight is built upon existing standards such as IEEE 802.15.4. A prototype implementation and protocol-accurate simulator have been produced. This article develops a series of schedulability analysis techniques for single-channel and multichannel wireless Cyber-Physical Systems (CPS). Heuristics are specified and evaluated as the starting point of design space exploration. Genetic algorithms are then defined and evaluated to assess their performance in developing schedule tables incorporating multichannel allocations in these systems.

Leader-Following Consensus and Formation Control of VTOL-UAVs with Event-Triggered Communications †

This article presents the design and implementation of an event-triggered control approach, applied to the leader-following consensus and formation of a group of autonomous micro-aircraft with capabilities of vertical take-off and landing (VTOL-UAVs). The control strategy is based on an inner–outer loop control approach. The inner control law stabilizes the attitude and position of one agent, whereas the outer control follows a virtual leader to achieve position consensus cooperatively through an event-triggered policy. The communication topology uses undirected and connected graphs. With such an event-triggered control, the closed-loop trajectories converge to a compact sphere, centered in the origin of the error space. Furthermore, the minimal inter-sampling time is proven to be below bounded avoiding the Zeno behavior. The formation problem addresses the group of agents to fly in a given shape configuration. The simulation and experimental results highlight the performance of the proposed control strategy.