Nonlinear predictive control of autonomous soaring UAVs using 3DOF models

We design a nonlinear model predictive control (NMPC) system for a soaring UAV in order to harvest the energy from the atmospheric updrafts. Our control framework combines an online estimation with a heuristic search method to obtain the UAV optimal trajectory. To allow for real-time computation of the control commands we solve the optimal control problem using a 3 degrees-of-freedom (DOF) model but apply the inputs to a more realistic 6DOF model. Hence, we design a 3DOF-6DOF model interaction strategy. Simulations show how the control system succeeds in energy extraction in a challenging dynamic atmospheric environment while satisfying its real-time contraints

Constrained LQR for Low-Precision Data Representation

Performing computations with a low-bit number representation results in a faster implementation that uses less silicon, and hence allows an algorithm to be implemented in smaller and cheaper processors without loss of performance. We propose a novel formulation to efficiently exploit the low (or non-standard) precision number representation of some computer architectures when computing the solution to constrained LQR problems, such as those that arise in predictive control. The main idea is to include suitably-defined decision variables in the quadratic program, in addition to the states and the inputs, to allow for smaller roundoff errors in the solver. This enables one to trade off the number of bits used for data representation against speed and/or hardware resources, so that smaller numerical errors can be achieved for the same number of bits (same silicon area). Because of data dependencies, the algorithm complexity, in terms of computation time and hardware resources, does not necessarily increase despite the larger number of decision variables. Examples show that a 10-fold reduction in hardware resources is possible compared to using double precision floating point, without loss of closed-loop performance

Energy-aware MPC co-design for DC-DC converters

In this paper, we propose an integrated controller design methodology for the implementation of an energy-aware explicit model predictive control (MPC) algorithms, illustrat- ing the method on a DC-DC converter model. The power consumption of control algorithms is becoming increasingly important for low-power embedded systems, especially where complex digital control techniques, like MPC, are used. For DC-DC converters, digital control provides better regulation, but also higher energy consumption compared to standard analog methods. To overcome the limitation in energy efficiency, instead of addressing the problem by implementing sub-optimal MPC schemes, the closed-loop performance and the control algorithm power consumption are minimized in a joint cost function, allowing us to keep the controller power efficiency closer to an analog approach while maintaining closed-loop op- timality. A case study for an implementation in reconfigurable hardware shows how a designer can optimally trade closed-loop performance with hardware implementation performance

Insight into CO2 dissociation in plasmas from numerical solution of a vibrational diffusion equation

The dissociation of CO2 molecules in plasmas is a subject of enormous importance for fundamental studies and the recent interest in carbon capture and carbon-neutral fuels. The vibrational excitation of the CO2 molecule plays an important role in the process. The complexity of the present state-to-state (STS) models makes it difficult to find out the key parameters. In this paper we propose as an alternative a numerical method based on the diffusion formalism developed in the past for analytical studies. The non-linear Fokker-Planck equation is solved by the time-dependent diffusion Monte Carlo method. Transport quantities are calculated from STS rate coefficients. The asymmetric stretching mode of CO2 is used as a test case. We show that the method reproduces the STS results or a Treanor distribution depending on the choice of the boundary conditions. A positive drift, whose energy onset is determined by the vibrational to translational temperature ratio, brings molecules from mid-energy range to dissociation. The high-energy fall of the distribution is observed even neglecting VT processes which are normally believed to be its cause. Our study explains several puzzling features of previous studies, provides new insights into the control of the dissociation rate and a much sought compression of the required data for modeling

Robust explicit MPC design under finite precision arithmetic

We propose a design methodology for explicit Model Predictive Control (MPC) that guarantees hard constraint satisfaction in the presence of finite precision arithmetic errors. The implementation of complex digital control techniques, like MPC, is becoming increasingly adopted in embedded systems, where reduced precision computation techniques are embraced to achieve fast execution and low power consumption. However, in a low precision implementation, constraint satisfaction is not guaranteed if infinite precision is assumed during the algorithm design. To enforce constraint satisfaction under numerical errors, we use forward error analysis to compute an error bound on the output of the embedded controller. We treat this error as a state disturbance and use this to inform the design of a constraint-tightening robust controller. Benchmarks with a classical control problem, namely an inverted pendulum, show how it is possible to guarantee, by design, constraint satisfaction for embedded systems featuring low precision, fixed-point computations

Occupancy Estimation Using Low-Cost Wi-Fi Sniffers

Real-time measurements on the occupancy status of indoor and outdoor spaces can be exploited in many scenarios (HVAC and lighting system control, building energy optimization, allocation and reservation of spaces, etc.). Traditional systems for occupancy estimation rely on environmental sensors (CO2, temperature, humidity) or video cameras. In this paper, we depart from such traditional approaches and propose a novel occupancy estimation system which is based on the capture of Wi-Fi management packets from users' devices. The system, implemented on a low-cost ESP8266 microcontroller, leverages a supervised learning model to adapt to different spaces and transmits occupancy information through the MQTT protocol to a web-based dashboard. Experimental results demonstrate the validity of the proposed solution in four different indoor university spaces.Comment: Submitted to Balkancom 201

Data Driven Discovery in Astrophysics

We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" science, with exponentially growing data volumes and data rates, and an ever-increasing complexity, now entering the Petascale regime. Telescopes and observatories from both ground and space, covering a full range of wavelengths, feed the data via processing pipelines into dedicated archives, where they can be accessed for scientific analysis. Most of the large archives are connected through the Virtual Observatory framework, that provides interoperability standards and services, and effectively constitutes a global data grid of astronomy. Making discoveries in this overabundance of data requires applications of novel, machine learning tools. We describe some of the recent examples of such applications.Comment: Keynote talk in the proceedings of ESA-ESRIN Conference: Big Data from Space 2014, Frascati, Italy, November 12-14, 2014, 8 pages, 2 figure

High-Definition Optical Coherence Tomography for the in vivo Detection of Demodex Mites

Background: Demodex mites are involved in different skin diseases and are commonly detected by skin scrape tests or superficial biopsies. A new high-definition optical coherence tomography (HD-OCT) with high lateral and axial resolution in a horizontal (en-face) and vertical (slice) imaging mode might offer the possibility of noninvasive and fast in vivo examination of demodex mites. Methods: Twenty patients with demodex-related skin diseases and 20 age- and gender-matched healthy controls were examined by HD-OCT. Mites per follicle and follicles per field of view were counted and compared to skin scrape tests. Results: HD-OCT images depicted mites in the en-face mode as bright round dots in groups of 3-5 mites per hair follicle. In the patients with demodex-related disease, a mean number of 3.4 mites per follicle were detected with a mean number of 2.9 infested follicles per area of view compared to a mean of 0.6 mites in 0.4 infested follicles in the controls. The skin scrape tests were negative in 21% of the patients. Conclusion: The innovative HD-OCT enables fast and noninvasive in vivo recognition of demodex mites and might become a useful tool in the diagnosis and treatment monitoring of demodex-related skin diseases. Copyright (C) 2012 S. Karger AG, Base