This paper addresses the problem of transforming a single-input single-output discrete-time system into the extended observer form which comprises a linear observable component and a nonlinear injection term depending on the input, output and their forward shifts up to a finite order. The necessary and sufficient conditions for the existence of the extended observer form are provided in terms of vector fields. The algorithm is presented to find a parametrised state transformation necessary to transform the system into the extended observer form. The obtained results are applicable also in case of non-reversible systems.

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Tactile Internet (TI) envisions communicating haptic sensory information and kinesthetic feedback over the network and is expected to transfer human skills remotely. For mission-critical TI applications, the network latency is commonly mandated to be between 1-10 ms, due to the sensitivity of human touch, and the packet delivery ratio to be 99.99999%, failing which can lead to catastrophic outcomes. However, with humans-in-the-loop, their dexterity and adaptability to varying responses to stimuli under different network conditions, measuring the performance of a TI session only with latency and packet losses are insufficient and presents an incorrect representation of the experience of the TI application. To develop an objective measure of the quality of TI sessions, we propose a framework that models TI applications as networked control systems, including humans-in-the-loop. We derive a closed-form expression for measuring the difference between the application performance in ideal and non-ideal network conditions. Based on Weber’s law of Just Noticeable Difference, we provide a metric called TIM to estimate the impact of the network on haptic feedback. We implemented TIM on multiple applications on a TI testbed to show that our approach is feasible and TIM strongly follows real subjective measurements. Further, we propose a channel compensation spring based on TIM, to alleviate the network conditions’ negative effects. We demonstrate the efficacy of the channel compensation spring in improving the user experience. We also present implementation notes for TI application developers.

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UAVs are becoming versatile and valuable platforms for various applications. However, the main limitation is their flying time. We present BEAVIS, a novel aerial robotic platform striking an unparalleled trade-off between the maneuverability of drones and the long-lasting capacity of blimps. BEAVIS scores highly in applications where drones enjoy unconstrained mobility yet suffer from limited lifetime. A nonlinear flight controller exploiting novel, unexplored, aerodynamic phenomena to regulate the ambient pressure and enable all translational and yaw degrees of freedom is proposed without direct actuation in the vertical direction. BEAVIS has built-in rotor fault detection and tolerance. We explain the design and the necessary background in detail. We verify the dynamics of BEAVIS and demonstrate its distinct advantages, such as agility, over existing platforms including the degrees of freedom akin to a drone with 11.36 increased lifetime. We exemplify the potential of BEAVIS to become an invaluable platform for many applications.@en

—This technical note addresses the problem of transforming a single-input–single-output discrete-time system into the extended observer form, which comprise a linear time-invariant observable component, and a nonlinear injection term, which depends on the input, output, and their forward shifts up to a finite order. Intrinsic necessary and sufficient conditions are provided for obtaining the extended observer form via a parametrized state transformation. The conditions are formulated directly in terms of the state equations and do not rely on input–output equations as in the earlier papers. Further, an algorithm for obtaining the required transformation is presented. Unlike the existing results on observer forms, the results are not restricted to reversible systems but to more general submersive systems, i.e., to systems, which are reversible via static state feedback.

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In recent times, Unmanned Aerial Vehicles (UAVs) have been intensively studied for a wide range of military and civil applications such as surveillance and reconnaissance missions, agriculture, traffic monitoring, pollution control, meteorological data collection, pipeline survey, early fire detection, rescue missions, wildlife population monitoring, etc. The above tasks can be carried out faster and more efficiently by a group of UAVs flying in formation. This paper presents the application of two stage switching controller in autonomous formation flight of UAVs based on the leader follower configuration. The constructed UAV airframe, as well as the autopilot, formation flight control unit and wireless communication link for data sharing between UAVs were presented and described. The advantages of the switched control were demonstrated in terms of a significantly larger region of stability in maintaining formation, as opposed to the standard, one stage control laws with classical compensators. Hardware In The Loop (HIL) tests of proposed formation flight control were performed, during which the inertial states and flight parameters were logged. The HIL test rig enabled us to verify the formation flight control performance which was further substantiated by actual flight tests.@en

In this letter, we present Hermes - a novel, low-cost, wireless, batteryless, energy harvesting system for aerial vehicles for sensing wind speed and Angle of Attack (AoA) concurrently. Hermes comprises a set of piezoelectric films which flutter due to incoming wind and the characteristics of this aeroelastic flutter are utilized for determining the wind speed and AoA of the head-wind. Note that in our work we restrict the notion of flutter to high frequency oscillations due to incoming air flow. Hermes consists of five piezoelectric flags that are mounted on rigid clamps specifically placed at different angles. We designed Hermes to maximize the sensing performance and energy harvesting capability simultaneously, without compromising either accuracy or harvesting efficiency. Our current prototype can harvest the power of 440 $\mu$W on average. Over a wide range AoA from $-10^{\circ }$ to $30^{\circ }$, the estimation of the wind speed is within 0.7 km/h error with 90% probability, and AoA error is within $1.2^{\circ }$ with 90% probability. Since Hermes necessitates no wires and batteries and is a low-cost sensor, it is well suited for a range of UAVs, gliders, and aircraft, which require flexible sensor placement and do not require new wiring, which is often complex in aircraft. Hermes is the first of its kind that exploits piezoelectric energy harvesting to simultaneously sense AoA and wind speed. This work is expected to open up new avenues for interdisciplinary research on embedded computing devices for aerospace applications.

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Electrocardiogram (ECG) is an important health monitoring signal that is used in various medical diagnosis, especially identifying potential possibility of heart attacks and strokes. Moreover, many patients are in remote places and in many countries the patients to doctors ration is very poor which calls for a miniature hardware that remotely captures ECG and transmits data to the doctors. However, the exact reproduction of ECG requires high bit rate and thus requires transmitting a compressed set of parameters. Further, sending large volumes of annotated raw data to train diagnostic models also compromises the patients privacy. We design and present a system that generates synthetic ECG signals from clinical data in real-time using a highly minimized set of parameters. The system comprises a nonlinear dynamical model whose parameters are trained in real-time to synthesize a signal which matches clinical data with high accuracy. The parameters of the trained system are then transmitted in each cycle of the ECG wave to reconstruct the original signal using the same model at the medical practitioners’ location. The parameter learning problem is highly complicated as one needs to solve a nonlinear, non-convex dynamic optimization problem, which usually only converges to local optima. To address this issue, we propose a novel two-stage algorithm that automatically chooses an initial set of parameters in the vicinity of the global optimum and then performs stochastic gradient descent iterations. We perform experiments to demonstrate the accuracy and real-time performance of the system. We show that on average our system processes clinical data of one second in 0.68s on a microcontroller, with an RMSE error of 0.0038 the average, and 17 parameters per ECG cycle. Our system is also easy to implement, requires minimal storage i.e. only one ECG cycle at any given time, and does not depend on offline training, unlike existing methods.@en