Learning-based Predictive Control Approach for Real-time Management of Cyber-physical Systems

Cyber-physical systems (CPSs) are composed of heterogeneous, and networked hardware and software components tightly integrated with physical elements [72]. Large-scale CPSs are composed of complex components, subject to uncertainties [89], as though their design and development is a challenging task. Achieving reliability and real-time adaptation to changing environments are some of the challenges involved in large-scale CPSs development [51]. Addressing these challenges requires deep insights into control theory and machine learning. This research presents a learning-based control approach for CPSs management, considering their requirements, specifications, and constraints. Model-based control approaches, such as model predictive control (MPC), are proven to be efficient in the management of CPSs [26]. MPC is a control technique that uses a prediction model to estimate future dynamics of the system and generate an optimal control sequence over a prediction horizon. The main benefit of MPC in CPSs management comes from its ability to take the predictions of system’s environmental conditions and disturbances into account [26]. In this dissertation, centralized and distributed MPC strategies are designed for the management of CPSs. They are implemented for the thermal management of a CPS case study, smart building. The control goals are optimizing system efficiency (lower thermal power consumption in the building), and improving users’ convenience (maintaining desired indoor thermal conditions in the building). Model-based control strategies are advantageous in the management of CPSs due to their ability to provide system robustness and stability. The performance of a model-based controller strongly depends on the accuracy of the model as a representation of the system dynamics [26]. Accurate modeling of large-scale CPSs is difficult (due to the existence of unmodeled dynamics and uncertainties in the modeling process); therefore, modelbased control approach is not practical for these systems [6]. By incorporating machine learning with model-based control strategies, we can address CPS modeling challenges while preserving the advantages of model-based control methods. In this dissertation, a learning-based modeling strategy incorporated with a model-based control approach is proposed to manage energy usage and maintain thermal, visual, and olfactory performance in buildings. Neural networks (NNs) are used to learn the building’s performance criteria, occupant-related parameters, environmental conditions, and operation costs. Control inputs are generated through the model-based predictive controller and based on the learned parameters, to achieve the desired performance. In contrast to the existing building control systems presented in the literature, the proposed management system integrates current and future information of occupants (convenience, comfort, activities), building energy trends, and environment conditions (environmental temperature, humidity, and light) into the control design. This data is synthesized and evaluated in each instance of decision-making process for managing building subsystems. Thus, the controller can learn complex dynamics and adapt to the changing environment, to achieve optimal performance while satisfying problem constraints. Furthermore, while many prior studies in the filed are focused on optimizing a single aspect of buildings (such as thermal management), and little attention is given to the simultaneous management of all building objectives, our proposed management system is developed considering all buildings’ physical models, environmental conditions, comfort specifications, and occupants’ preferences, and can be applied to various building management applications. The proposed control strategy is implemented to manage indoor conditions and energy consumption in a building, simulated in EnergyPlus software. In addition, for comparison purposes, we designed and simulated a baseline controller for the building under the same conditions

Development of engineering tools to analyze and design flexible structures in open ocean environments

Methods to effectively predict system response in marine settings are critical in the engineering design process. The high energy ocean environment can subject structures to large wave and current forces, causing complex coupled motions and loads. This research focused on the development of effective methods to predict flexible system response and the structural integrity of marine High Density Polyethylene (HDPE) components. Numerical modeling tools were developed to analyze and design flexible structures in open ocean environments. Enhancements to the University of New Hampshire\u27s Aqua-FE finite element computer program were performed, including expansion of the element library to include spherical geometries and implementation of various hydrodynamic effects such as Stokes 2nd order waves and water velocity reduction due to component shadowing. Two case studies, involving laboratory and field experiments, were performed evaluating the software modifications and examining the response of flexible systems in various environmental conditions. Practical applications of the numerical model are presented, focusing on the design, analysis and deployment of a submerged grid mooring 10 km from Portsmouth, NH. The system was recovered after a seven year deployment and inspected. The numerical model proved to be a valuable engineering tool for investigating a system\u27s motion dynamics and mooring tension response in marine environments. High density polyethylene is a primary structural component for marine systems such as fish containment, wave attenuators and marine defense barrier systems. The fundamental engineering issues with the compliant HDPE material are associated with how the material changes its stiffness and strength depending upon the service life, load rate and temperature. Structural modeling techniques were developed to determine effective methods of analyzing marine systems constructed of HDPE. This included the investigation of the mechanical behavior of new and environmentally fatigued HDPE specimens, obtained from commercial fish farms, at different strain rates and validation of the modeling approach with laboratory experiments. The operational limits, loads and modes of a failure of the HDPE cage frame were estimated, providing valuable information on the survivability of these large, flexible systems in offshore environments

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Towards the Integration of APECS and VE-Suite for Virtual Power Plant Co-Simulation

Process modeling and simulation tools are widely used for the design and operation of advanced power generation systems. These tools enable engineers to solve the critical process systems engineering problems that arise throughout the lifecycle of a power plant, such as designing a new process, troubleshooting a process unit or optimizing operations of the full process. To analyze the impact of complex thermal and fluid flow phenomena on overall power plant performance, the Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) has developed the Advanced Process Engineering Co-Simulator (APECS). The APECS system is an integrated software suite that combines process simulation (e.g., Aspen Plus) and high-fidelity equipment simulations such as those based on computational fluid dynamics (CFD), together with advanced analysis capabilities including case studies, sensitivity analysis, stochastic simulation for risk/uncertainty analysis, and multi-objective optimization. In this paper we discuss the initial phases of the integration of the APECS system with the immersive and interactive virtual engineering software, VE-Suite, developed at Iowa State University and Ames Laboratory. VE-Suite uses the ActiveX (OLE Automation) controls in the Aspen Plus process simulator wrapped by the CASI library developed by Reaction Engineering International to run process/CFD co-simulations and query for results. This integration represents a necessary step in the development of virtual power plant co-simulations that will ultimately reduce the time, cost, and technical risk of developing advanced power generation systems

Overview on agent-based social modelling and the use of formal languages

Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft

Mathematical Modeling of Trending Topics on Twitter

Created in 2006, Twitter is an online social networking service in which users share and read 140-character messages called Tweets. The site has approximately 288 million monthly active users who produce about 500 million Tweets per day. This study applies dynamical and statistical modeling strategies to quantify the spread of information on Twitter. Parameter estimates for the rates of infection and recovery are obtained using Bayesian Markov Chain Monte Carlo (MCMC) methods. The methodological strategy employed is an extension of techniques traditionally used in an epidemiological and biomedical context (particularly in the spread of infectious disease). This study, which addresses information spread, presents case studies pertaining to the prevalence of several “trending” topics on Twitter over time. The study introduces a framework to compare information dynamics on Twitter based on the topical area as well as a framework for the prediction of topic prevalence. Additionally, methodological and results-based comparisons are drawn between the spread of information and the spread of infectious disease

Analytical modeling of spindle-tool dynamics on machine tools using Timoshenko beam model and receptance coupling for the prediction of tool point FRF

Regenerative chatter is a well-known machining problem that results in unstable cutting process, poor surface quality and reduced material removal rate. This undesired self-excited vibration problem is one of the main obstacles in utilizing the total capacity of a machine tool in production. In order to obtain a chatter-free process on a machining center, stability diagrams can be used. Numerically or analytically, constructing the stability lobe diagram for a certain spindleholdertool combination implies knowing the system dynamics at the tool tip; i.e., the point frequency response function (FRF) that relates the dynamic displacement and force at that point. This study presents an analytical method that uses Timoshenko beam theory for calculating the tool point FRF of a given combination by using the receptance coupling and structural modication methods. The objective of the study is two fold. Firstly, it is aimed to develop a reliable mathematical model to predict tool point FRF in a machining center so that chatter stability analysis can be done, and secondly to make use of this model in studying the effects of individual bearing and contact parameters on tool point FRF so that better approaches can be found in predicting contact parameters from experimental measurements. The model can also be used to study the effects of several spindle, holder and tool parameters on chatter stability. In this paper, the mathematical model, as well as the details of obtaining the system component (spindle, holder and tool) dynamics and coupling them to obtain the tool point FRF are given. The model suggested is veried by comparing the natural frequencies of an example spindleholdertool assembly obtained from the model with those obtained from a nite element software

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone