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

The Performance of Biomimicry Architecture in Sustainable Design for a Mixed-Use Workplace in Shanghai (Sustainable Design)

Abstract Sustainable architecture design is becoming more and more popular all over the world, especially in China. Active sustainable strategies play an important role in sustainable architecture design such as solar panels, wind turbines, and roof gardens. However, this Thesis will find some new passive ways to improve the sustainability of buildings by proving bionic technology. The thesis seeks to integrate living organisms into buildings to improve the sustainability of buildings and generate sustainable resources. This main focus is biomimetics. The technology used in the design of architecture sustainability. Bionics or biomimicry refers to artificial processes or systems that mimic nature. The thesis will develop a program that is about how to interpret biomimicry language to architecture language and apply it to the design of a building to improve its performance. The thesis finally mainly use three biomimicry technology to design the building. They are respectively (1) a termite mound structure to advance ventilation of the building, (2)algae to clean carbon dioxide, and (3) a three-leaf clover floor plan layout and building form. to create more fresh energy for the building. In addition, the thesis aims to use more biomimicry solutions to overcome those problems from site analysis

Full Proceedings, 2018

Full conference proceedings for the 2018 International Building Physics Association Conference hosted at Syracuse University

University-Based Smart Cities: from collective intelligence to smart crowd-conscience

Quality of life, economic, knowledge and human capitals ‘development are the main challenges of the new wave of smart cities. Hybrid strategies of cost leadership and innovation need to be aligned mostly by highly deliberate university creative services.  Physical, intellectual and social capitals are loosely coupled to better understanding of the urban fabric and norms of behavior. It requires the creation ofapplications enabling data collection and processing, web-based collaboration, and “real-time†mining of the collective intelligence of citizens. The Internet of Things (IoT) has been viewed as a promising technology with great potential for addressing many societal challenges, filling the gap in terms of citizen's sensitivity measurement. At the physical level of its ecosystem, buildings are responsible for about 40% of energy consumption in cities and more than 40% of greenhouse gas emissions. With recent products available today, energy consumption in buildings could be cut by up to 70 percent, but it requires an integrated and collective adaptive framework to show how buildings are operated, maintained and controlled with the support of IoT-based innovation and solutions. The number of new IoT protocols and applications has grown exponentially in recent years. However, IoT for smart cities needs accessible open data and open systems, so that industries and universities can develop new services and applications. The main aim is to develop energy efficient frameworks to improve energy efficiency by using innovative integrated IoT techniques. These techniques could integrate technologies from context-aware computing, context-dependent user expectation and profile and occupants' actions and behaviors. This paper tend to present in what extent a case of university-based smart city would invest in IoT as both strategy and process in order to enhance efficiency, innovative education and attractiveness for its current and future citizens

Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data

The detection of spatially dependent heavy-tailed signals is considered in this dissertation. While the central limit theorem, and its implication of asymptotic normality of interacting random processes, is generally useful for the theoretical characterization of a wide variety of natural and man-made signals, sensor data from many different applications, in fact, are characterized by non-Gaussian distributions. A common characteristic observed in non-Gaussian data is the presence of heavy-tails or fat tails. For such data, the probability density function (p.d.f.) of extreme values decay at a slower-than-exponential rate, implying that extreme events occur with greater probability. When these events are observed simultaneously by several sensors, their observations are also spatially dependent. In this dissertation, we develop the theory of detection for such data, obtained through heterogeneous sensors. In order to validate our theoretical results and proposed algorithms, we collect and analyze the behavior of indoor footstep data using a linear array of seismic sensors. We characterize the inter-sensor dependence using copula theory. Copulas are parametric functions which bind univariate p.d.f. s, to generate a valid joint p.d.f. We model the heavy-tailed data using the class of alpha-stable distributions. We consider a two-sided test in the Neyman-Pearson framework and present an asymptotic analysis of the generalized likelihood test (GLRT). Both, nested and non-nested models are considered in the analysis. We also use a likelihood maximization-based copula selection scheme as an integral part of the detection process. Since many types of copula functions are available in the literature, selecting the appropriate copula becomes an important component of the detection problem. The performance of the proposed scheme is evaluated numerically on simulated data, as well as using indoor seismic data. With appropriately selected models, our results demonstrate that a high probability of detection can be achieved for false alarm probabilities of the order of 10^-4. These results, using dependent alpha-stable signals, are presented for a two-sensor case. We identify the computational challenges associated with dependent alpha-stable modeling and propose alternative schemes to extend the detector design to a multisensor (multivariate) setting. We use a hierarchical tree based approach, called vines, to model the multivariate copulas, i.e., model the spatial dependence between multiple sensors. The performance of the proposed detectors under the vine-based scheme are evaluated on the indoor footstep data, and significant improvement is observed when compared against the case when only two sensors are deployed. Some open research issues are identified and discussed