Motivated by the physical exchange of energy and its dissipation in electro-mechanical systems, we propose a new fault detection method based on data-driven dissipativity analysis. We first identify a dissipativity inequality using one or multiple shots of data obtained from a linear time-invariant system. This dissipativity inequality's storage and supply rate functions assume generic quadratic difference forms encompassing all LTI systems. By analysing the norm of the identified dissipative inequality as the residual function, we can detect the occurrence of faults in real-time without the need to model each fault the system is subjected to. Through academic examples, we demonstrate how we can identify supply rate and storage functions from persistently exciting data shots. We present a practical example of detecting faults on a two-degree-of-freedom planar manipulator with zero missed fault detection rate, which is compared to a standard PCA-based fault detection algorithm.

Flexible piezocapacitive sensors utilizing nanomaterial-polymer composite-based nanofibrous membranes offer an attractive alternative to more traditional piezoelectric and piezoresistive wearable sensors owing to their ultralow powered nature, fast response, low hysteresis, and insensitivity to temperature change. In this work, we propose a facile method of fabricating electrospun graphene-dispersed PVAc nanofibrous membrane-based piezocapacitive sensors for applications in IoT-enabled wearables and human physiological function monitoring. A series of electrical and material characterization experiments were conducted on both the pristine and graphene-dispersed PVAc nanofibers to understand the effect of graphene addition on nanofiber morphology, dielectric response, and pressure sensing performance. Dynamic uniaxial pressure sensing performance evaluation tests were conducted on the pristine and graphene-loaded PVAc nanofibrous membrane-based sensors for understanding the effect of two-dimensional (2D) nanofiller addition on pressure sensing performance. A marked increase in the dielectric constant and pressure sensing performance was observed for graphene-loaded spin coated membrane and nanofiber webs respectively, and subsequently the micro dipole formation model was invoked to explain the nanofiller-induced dielectric constant enhancement. The robustness and reliability of the sensor have been underscored by conducting accelerated lifetime assessment experiments entailing at least 3000 cycles of periodic tactile force loading. A series of tests involving human physiological parameter monitoring were conducted to underscore the applicability of the proposed sensor for IoT-enabled personalized health care, soft robotics, and next-generation prosthetic devices. Finally, the easy degradability of the sensing elements is demonstrated to emphasize their suitability for transient electronics applications.

Recently, 3D porous graphene-polymer composite-based piezoresistive sensors have gained significant traction in the field of flexible electronics owing to their ultralightweight nature, high compressability, robustness, and excellent electromechanical properties. In this work, we present an improved facile recipe for developing repeatable, reliable, and linear 3D graphene-polydimethylsiloxane (PDMS) spongy sensors for internet of things (IoT)-enabled wearable systems and smart consumer products. Fundamental morphological characterization and sensing performance assessment of the piezoresistive 3D graphene-polymer sensor were conducted to establish its suitability for the development of squeezable, flexible, and skin-mountable human motion sensors. The density and porosity of the sponges were determined to be 250 mg cm-3 and 74% respectively. Mechanical compressive loading tests conducted on the sensors revealed an average elastic modulus as low as ∼56.7 kPa. Dynamic compressive force-resistance change response tests conducted on four identical sensors revealed a linear piezoresistive response (in the compressive load range 0.42-2.18 N) with an average force sensitivity of 0.3470 ± 0.0794 N-1. In addition, an accelerated lifetime test comprising 1500 compressive loading cycles (at 3.90 N uniaxial compressive loading) was conducted to demonstrate the long-term reliability and stability of the sensor. To test the applicability of the sensors in smart wearables, four identical graphene-PDMS sponges were configured on the fingertip regions of a soft nitrile glove to develop a pressure sensing smart glove for real-time haptic pressure monitoring. Similarly, the sensors were also integrated into the Philips 9000 series electric shaver to realize smart shaving applications with the ability to monitor shaving motions. Furthermore, the readiness of our system for next-generation IoT-enabled applications was demonstrated by integrating the smart glove with an embedded system software utilizing the an open source microcontroller platform. The system was capable of identifying real-time qualitative pressure distribution across the fingertips while grasping daily life objects, thus establishing the suitability of such sensors for next-generation wearables for prosthetics, consumer devices, and personalized healthcare monitoring devices.

This article focuses on the stability analysis of a formation shape displayed by a team of mobile robots that uses a heterogeneous sensing mechanism. For the setups consisting of three robots, we show that the use of heterogeneous gradient-based control laws can give rise to undesired invariant sets where a distorted formation shape is possibly moving at a constant velocity. We guarantee local asymptotic stability for the correct and desired formation shape. For the setup with one distance and two bearing robots, we identify the conditions such that an incorrect moving formation is locally attractive.

We present the main results of the performance test campaign of the Mid-Infrared European Extremely Large Telescope Imager and Spectrograph (METIS) Cold Chopper Demonstrator (MCCD). This tip/tilt mirror, which operates at a temperature of 77 K, is one of the critical components in the METIS for the European Extremely Large Telescope. The performance requirements of the MCCD relate to the field of fast and very accurate reference tracking. We discuss the applicability of different high-performance motion control strategies and describe the control synthesis of a repetitive and of a novel hybrid controller. We identified the presence of nonlinearities in the plant, which limits the performance of the hybrid controller. The repetitive controller shows very promising results and can handle the nonlinearities in the system. This experimental phase concludes the MCCD program, which was initiated to verify the feasibility of a high-performance cryogenic tip/tilt mirror at an early stage in the METIS development. Because of the very promising test results, no significant changes to the hardware will be implemented. We believe that minor adjustments will suffice to meet all requirements of the final hardware after integration with the METIS instrument.