7,883 research outputs found
Planning and Resource Management in an Intelligent Automated Power Management System
Power system management is a process of guiding a power system towards the objective of continuous supply of electrical power to a set of loads. Spacecraft power system management requires planning and scheduling, since electrical power is a scarce resource in space. The automation of power system management for future spacecraft has been recognized as an important R&D goal. Several automation technologies have emerged including the use of expert systems for automating human problem solving capabilities such as rule based expert system for fault diagnosis and load scheduling. It is questionable whether current generation expert system technology is applicable for power system management in space. The objective of the ADEPTS (ADvanced Electrical Power management Techniques for Space systems) is to study new techniques for power management automation. These techniques involve integrating current expert system technology with that of parallel and distributed computing, as well as a distributed, object-oriented approach to software design. The focus of the current study is the integration of new procedures for automatically planning and scheduling loads with procedures for performing fault diagnosis and control. The objective is the concurrent execution of both sets of tasks on separate transputer processors, thus adding parallelism to the overall management process
Model Predictive Control for Signal Temporal Logic Specification
We present a mathematical programming-based method for model predictive
control of cyber-physical systems subject to signal temporal logic (STL)
specifications. We describe the use of STL to specify a wide range of
properties of these systems, including safety, response and bounded liveness.
For synthesis, we encode STL specifications as mixed integer-linear constraints
on the system variables in the optimization problem at each step of a receding
horizon control framework. We prove correctness of our algorithms, and present
experimental results for controller synthesis for building energy and climate
control
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
FlightGoggles is a photorealistic sensor simulator for perception-driven
robotic vehicles. The key contributions of FlightGoggles are twofold. First,
FlightGoggles provides photorealistic exteroceptive sensor simulation using
graphics assets generated with photogrammetry. Second, it provides the ability
to combine (i) synthetic exteroceptive measurements generated in silico in real
time and (ii) vehicle dynamics and proprioceptive measurements generated in
motio by vehicle(s) in a motion-capture facility. FlightGoggles is capable of
simulating a virtual-reality environment around autonomous vehicle(s). While a
vehicle is in flight in the FlightGoggles virtual reality environment,
exteroceptive sensors are rendered synthetically in real time while all complex
extrinsic dynamics are generated organically through the natural interactions
of the vehicle. The FlightGoggles framework allows for researchers to
accelerate development by circumventing the need to estimate complex and
hard-to-model interactions such as aerodynamics, motor mechanics, battery
electrochemistry, and behavior of other agents. The ability to perform
vehicle-in-the-loop experiments with photorealistic exteroceptive sensor
simulation facilitates novel research directions involving, e.g., fast and
agile autonomous flight in obstacle-rich environments, safe human interaction,
and flexible sensor selection. FlightGoggles has been utilized as the main test
for selecting nine teams that will advance in the AlphaPilot autonomous drone
racing challenge. We survey approaches and results from the top AlphaPilot
teams, which may be of independent interest.Comment: Initial version appeared at IROS 2019. Supplementary material can be
found at https://flightgoggles.mit.edu. Revision includes description of new
FlightGoggles features, such as a photogrammetric model of the MIT Stata
Center, new rendering settings, and a Python AP
Advanced tracking systems design and analysis
The results of an assessment of several types of high-accuracy tracking systems proposed to track the spacecraft in the National Aeronautics and Space Administration (NASA) Advanced Tracking and Data Relay Satellite System (ATDRSS) are summarized. Tracking systems based on the use of interferometry and ranging are investigated. For each system, the top-level system design and operations concept are provided. A comparative system assessment is presented in terms of orbit determination performance, ATDRSS impacts, life-cycle cost, and technological risk
Assume-Guarantee Testing
Verification techniques for component-based systems should ideally be able to predict properties of the assembled system through analysis of individual components before assembly. This work introduces such a modular technique in the context of testing. Assume-guarantee testing relies on the (automated) decomposition of key system-level requirements into local component requirements at design time. Developers can verify the local requirements by checking components in isolation; failed checks may indicate violations of system requirements, while valid traces from different components compose via the assume-guarantee proof rule to potentially provide system coverage. These local requirements also form the foundation of a technique for efficient predictive testing of assembled systems: given a correct system run, this technique can predict violations by alternative system runs without constructing those runs. We discuss the application of our approach to testing a multi-threaded NASA application, where we treat threads as components
Keeping intelligence under control
Modern software systems, such as smart systems, are based on a continuous interaction with the dynamic and partially unknown environment in which they are deployed. Classical development techniques, based on a complete description of how the system must behave in different environmental conditions, are no longer effective. On the contrary, modern techniques should be able to produce systems that autonomously learn how to behave in different environmental conditions.Machine learning techniques allow creating systems that learn how to execute a set of actions to achieve a desired goal. When a change occurs, machine learning techniques allow the system to autonomously learn new policies and strategies for actions execution. This flexibility comes at a cost: the developer has no longer full control on the system behaviour. Thus, there is no way to guarantee that the system will not violate important properties, such as safety-critical properties.To overcome this issue, we believe that machine learning techniques should be combined with suitable reasoning mechanisms aimed at assuring that the decisions taken by the machine learning algorithm do not violate safety-critical requirements. This paper proposes an approach that combines machine learning with run-time monitoring to detect violations of system invariants in the actions execution policies
Modeling and Simulation Methodologies for Digital Twin in Industry 4.0
The concept of Industry 4.0 represents an innovative vision of what will be the factory of the future. The principles of this new paradigm are based on interoperability and data exchange between dierent industrial equipment. In this context, Cyber- Physical Systems (CPSs) cover one of the main roles in this revolution. The combination of models and the integration of real data coming from the field allows to obtain the virtual copy of the real plant, also called Digital Twin. The entire factory can be seen as a set of CPSs and the resulting system is also called Cyber-Physical Production System (CPPS). This CPPS represents the Digital Twin of the factory with which it would be possible analyze the real factory. The interoperability between the real industrial equipment and the Digital Twin allows to make predictions concerning the quality of the products. More in details, these analyses are related to the variability of production quality, prediction of the maintenance cycle, the accurate estimation of energy consumption and other extra-functional properties of the system. Several tools [2] allow to model a production line, considering dierent aspects of the factory (i.e. geometrical properties, the information flows etc.) However, these simulators do not provide natively any solution for the design integration of CPSs, making impossible to have precise analysis concerning the real factory. Furthermore, for the best of our knowledge, there are no solution regarding a clear integration of data coming from real equipment into CPS models that composes the entire production line. In this context, the goal of this thesis aims to define an unified methodology to design and simulate the Digital Twin of a plant, integrating data coming from real equipment. In detail, the presented methodologies focus mainly on: integration of heterogeneous models in production line simulators; Integration of heterogeneous models with ad-hoc simulation strategies; Multi-level simulation approach of CPS and integration of real data coming from sensors into models. All the presented contributions produce an environment that allows to perform simulation of the plant based not only on synthetic data, but also on real data coming from equipments
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