Cyber-physical systems (CPS) are highly vulnerable to disruptions and disturbances during decision-making processes. An important challenge in CPS management is to anticipate, absorb, adapt to, and rapidly recover from unexpected critical situations. This study proposes a decision support system (DSS) for CPS management and planning under uncertainty. The DSS architecture is developed based on Markov Decision Process (MDP) and it reinforces the model-based CPS management system in making optimal decisions by taking into account a wide range of disruptive scenarios existing in CPS operation. In the event of a disruption, an initial decision is chosen randomly, and then it is optimized gradually over time by transiting to new states and finding decisions with higher reward values. Two factors are considered for determining immediate rewards in the support system: execution time and cost of the decision. The proposed decision support system is applied to tackle disruptions (including fire hazard, power outage, pipe burst, appliance malfunction, natural disaster, and compromised network) in a smart building case study. In contrast to the existing decision support systems presented in the literature, the proposed architecture is provided in a more integrated and generalizable manner that can be applied to various smart building decision-making applications during disruptive/critical events.

One of the leading frontiers of the Internet of Things (IoT) era, smart building systems have made modern homes more innovative, interconnected, and autonomous. The goal of a smart home system is to enhance users’ comfort, safety, and efficiency. In this paper, a smart building testbed is designed and constructed as a cyber-physical system that allows testing and validating building control algorithms, communication networks, and user interfaces. The building control unit is responsible for optimizing the performance of testbed actuators (thermal, lighting, and access systems), whereby building parameters are aggregated via a set of distributed sensors and communicated to a management system. This data is observed and analyzed by the control system to produce optimal control commands. The building communication network is developed based on the Message Queuing Telemetry Transport (MQTT) protocol, in which subscribers receive the measurement data, format it, and send it to the user-interface unit. The user interface is implemented in a Node-RED platform, where the data is visualized in real-time, and users are capable of interacting with the automation system. Experimental results demonstrate the usefulness of such a prototype for smart building monitoring and control research.

Accurate forecasting of photovoltaic (PV) power generation is crucial for integrating this renewable energy into existing energy systems. Predicting PV output is a challenging task because of the uncertain and stochastic behavior of solar energy systems (e.g., unstable climatic conditions, errors in irradiance measurements). Conventional PV production forecasting approaches, such as non-deep neural networks, solely rely on high-precision and ideal data, and they may fail in predicting uncertain PV profiles. In this study, an uncertainty-aware deep neural network is proposed for predicting stochastic PV generation profiles. We combined convolutional neural networks (CNN) with long short-term memory (LSTM) to improve the robustness of our prediction model against uncertainties in the data. In the first step, CNN is utilized to capture local features in PV datasets. In the second stage, CNN outputs are injected into the LSTM neural network. LSTM is responsible for learning the temporal relationships in the data. To evaluate the performance of our proposed approach, it is applied to a real-world dataset. The effectiveness of our proposed hybrid model is compared to that of a baseline approach (a single LSTM neural network). In contrast to the existing PV forecasting approaches presented in the literature, our proposed framework provides highly accurate predictions of PV generation with flexible prediction horizons, and it is robust against noises in measurement devices and missing profiles.

This paper presents an open architecture testbed for smart cities, called OpenCity, which is hosted at Virginia Commonwealth University (VCU). The OpenCity platform consists of data collection and processing units, database management, distributed performance management algorithms, and real-time data visualization. This smart city testbed aims to support various educational and research activities related to smart city development. The testbed provides a near-real-life platform to allow students to learn about the unique features of smart cities and explore supporting technologies. In addition, it allows researchers to develop, deploy, and validate new techniques, tools, and technologies to support future smart city developments. The OpenCity platform will support various ongoing important research directions in smart cities, including smart homes and buildings, urban mobility, smart grid, and water management. In addition, it will be extendable to include other potential applications and components as needed. The testbed will be validated by developing and deploying a management system that focuses on users’ experience and resource efficiency. The management system incorporates learning techniques and model-based predictive control approaches to take into account the current and future information of uncertain parameters as well as the subjective data (e.g., user-related data) in the design. The OpenCity management structure enables real-time control and monitoring of complex components in the testbed.

In a smart building, physical and computational elements are integrated to create an environment that is energy-efficient, comfortable, and safe for its occupants. The design and development of smart buildings is a complicated task. Every smart building is a unique structure from the requirements and characteristics standpoints. Therefore, achieving reliability and real-time adaptation to environmental conditions are some of the challenges involved in smart building development. Resolving these issues requires deep insights into control theory, machine learning, system specifications, and design requirements. To address this need, this paper proposes a real-time management system for controlling various aspects of smart buildings (indoor conditions, comfort criteria, security, safety, and costs), and also presents the performance specifications, design requirements, and operating constraints for these systems. The study aims to address two less-attended problems in the related literature of building management and control. First, only a few studies have attempted to include real-time learning of buildings' subjective parameters in the model-based control design. Second, to the best of the authors' knowledge, smart building management studies are primarily focused on optimizing thermal or visual aspects of buildings, and little attention is given to the simultaneous management of all building subsystems and objectives; i.e., considering buildings' physical models, environmental conditions, comfort specifications, and occupants’ preferences and safety in the design. Accordingly, in this paper, we combine machine learning with model-based control approaches to incorporate subjective environmental parameters into the building management structure. In addition, another benefit of this study is that it integrates model-based and learning-based control schemes in a unified management structure for controlling various aspects of building performance. The proposed building management system can be applied to a variety of smart buildings in which the building parameters can be monitored and self-tuned using a well-defined set of control inputs.

As urbanization moves towards globalization in the next century, the evolution of smart city technologies has also brought new approaches to traditional public wellbeing problems such as food accessibility at both global and local scales. Technology implemented using the Internet of Things and data analytics offers unique advantages and challenges to address issues related to food access. In addition, interdisciplinary efforts are necessary to effectively utilize emerging technologies to address the issue of food insecurity while considering the underlying complex social, economic, and environmental dimensions. In this paper, we discuss the multi-dimensional nature of the food accessibility problem in U.S. metropolitan regions and explore the connection between the fields of engineering, social science, agriculture, education, and life sciences, with respect to their collective impact on addressing the food accessibility problem. We also present our team’s ongoing efforts to identify and address food insecurity problems in Richmond, Virginia through interdisciplinary research.

This paper proposes a model predictive control (MPC) approach incorporated with machine learning to control the energy consumption and occupants' comfort (thermal and visual comfort) in a smart building. Neural networks (NN)s are developed to learn and predict the building's comfort specifications, environmental conditions, and power consumption. Based on the predicted data, MPC provides optimal control inputs for the thermal and lighting systems to achieve the desired performance. In contrast to the existing building control frameworks, our proposed learning-based control method incorporates the occupant-related parameters in the control loop, which enhances the prediction accuracy and control performance. Our proposed learning-based MPC approach is implemented on a building, simulated in EnergyPlus software, and its performance is compared with that of a model-based building control framework. From the simulation results, our control method performs significantly better than the conventional MPC in maintaining residents' comfort and reducing energy consumption.

This paper proposes a distributed model predictive control (DMPC) approach for an urban traffic network (UTN) system. The control objective is to minimize the traffic congestion and the total travel time spent (TTS) in each link. The proposed DMPC algorithm considers traffic demand and disturbance predictions. The CasADi optimization tool is used to solve the constrained optimization problem. The proposed distributed control approach achieved 60% less computation time, 14.3% less TTS, and 15.1% less queue length compared to the centralized approach. Moreover, while the centralized algorithm neglected the input and state constraints, the distributed approach resulted in the satisfaction of all the constraints over the whole horizon.

This paper proposes a learning-based model predictive control (MPC) approach for the thermal control of a four-zone smart building. The objectives are to minimize energy consumption and maintain the residents’ comfort. The proposed control scheme incorporates learning with the model-based control. The occupancy profile in the building zones are estimated in a long-term horizon through the artificial neural network (ANN), and this data is fed into the model-based predictor to get the indoor temperature predictions. The Energy Plus software is utilized as the actual dataset provider (weather data, indoor temperature, energy consumption). The optimization problem, including the actual and predicted data, is solved in each step of the simulation and the input setpoint temperature for the heating/cooling system, is generated. Comparing the results of the proposed approach with the conventional MPC results proved the significantly better performance of the proposed method in energy savings (40.56% less cooling power consumption and 16.73% less heating power consumption), and residents’ comfort.

In this paper, a distributed Model Predictive Control (DMPC) strategy is developed for a multi-zone building plant with disturbances. The control objective is to maintain each zone's temperature at a specified level with the minimum cost of the underlying HVAC system. The distributed predictive framework is introduced with stability proofs and disturbances prediction, which have not been considered in previous related works. The proposed distributed MPC performed with 48% less computation time, 25.42% less energy consumption, and less tracking error compared with the centralized MPC. The controlled system is implemented in a smart building test bed.

In this paper, an indirect adaptive fuzzy controller is introduced for controlling a nonlinear rotational inverted pendulum with time-varying parameters. In fact, choosing this type of controller is regarded as a wise choice since this particular system performance is highly sensitive to unavoidable unknown model changes. Hence, a conventional controller is firstly designed through feedback linearization method, and applied to the system. Feedback linearization method here is used for two purposes; to attain an approximation of necessary system dynamics and to assess the performance of the proposed adaptive fuzzy controller by comparing the results of both adaptive fuzzy and feedback linearization controllers. An indirect adaptive fuzzy controller, resistant to parameter variations is then proposed. The general structure of the adaptive controller is specified in the first stage. In the second stage, its parameters are regulated with the aid of two fuzzy systems. Parameters are regulated based on the Lyapunov stability theorem such that the closed loop system is stabilized and zero tracking error is attained. Finally, the results of the proposed and the conventional approaches are compared. Results proved that the adaptive fuzzy controller performed much more efficiently than the classical controller, especially against parameters variations.

This paper addresses the design and implementation of a smart building prototype. The implementation utilizes Internet of Things (IoT) solutions to collect, analyze, and manage data from building systems in a smart city environment. The developed smart building prototype is capable of realtime interactions with the residents. The main objective is to adapt the building settings to the residents' needs and provide the maximum comfort level with minimum operational costs. For this purpose, building parameters are collected via a set of sensors and transferred to a database in real-time, which can be accessed, analyzed and visualized. Environment properties such as temperature, light, humidity, audio, video, surveillance, and access status are managed through a model-based controller. The developed testbed and control scheme are generic and modular. The prototype can also be utilized for testing cyber-physical systems' monitoring and management technologies.

Intelligent transportation systems (ITSs) and other smart-city technologies are increasingly advancing in capability and complexity. While simulation environments continue to improve, their fidelity and ease of use can quickly degrade as newer systems become increasingly complex. To remedy this, we propose a hardware- and software-based traffic management system testbed as part of a larger smart-city testbed. It comprises a network of connected vehicles, a network of intersection controllers, a variety of control services, and data analytics services. The main goal of our testbed is to provide researchers and students with the means to develop novel traffic and vehicle control algorithms with higher fidelity than what can be achieved with simulation alone. Specifically, we are using the testbed to develop an integrated management system that combines model-based control and data analytics to improve the system performance over time. In this paper, we give a detailed description of each component within the testbed and discuss its current developmental state. Additionally, we present initial results and propose future work.

This paper introduces an indirect adaptive fuzzy model predictive control strategy for a nonlinear rotational inverted pendulum with model uncertainties. In the first stage, a nonlinear prediction model is provided based on the fuzzy sets, and the model parameters are tuned through the adaption rules. In the second stage, the model predictive controller is designed based on the predicted inputs and outputs of the system. The control objective is to track the desired outputs with minimum error and to maintain closed-loop stability based on the Lyapunov theorem. Combining the adaptive Mamdani fuzzy model with the model predictive control method is proposed for the first time for the nonlinear inverted pendulum. Moreover, the proposed approach considers the disturbances predictions as part of the system inputs which have not been considered in the previous related works. Thus, more accurate predictions resistant to the parameters variations enhance the system performance using the proposed approach. A classical model predictive controller is also applied to the plant, and the results of the proposed strategy are compared with the results from the classical approach. Results proved that the proposed algorithm improves the control performance significantly with guaranteed stability and excellent tracking. Keywords: Indirect adaptive fuzzy; Model predictive control; Nonlinear rotational inverted pendulum; Model uncertainties; Lyapunov stability theorem.

This paper proposes a distributed model predictive control (DMPC) approach for an urban traffic network (UTN) system. The control objective is to minimize the traffic congestion and the total travel time spent (TTS) in each link. The proposed DMPC algorithm considers traffic demand and disturbance predictions. The CasADi optimization tool is used to solve the constrained optimization problem. The proposed distributed control approach achieved 60% less computation time, 14.3% less TTS, and 15.1% less queue length compared to the centralized approach. Moreover, while the centralized algorithm neglected the input and state constraints, the distributed approach resulted in the satisfaction of all the constraints over the whole horizon.

In this paper, a nonlinear rotational inverted pendulum with time-varying parameters is controlled using the indirect adaptive fuzzy controller design. This type of controller is chosen because this particular system performance is highly sensitive to unavoidable unknown model changes. So, a conventional controller is firstly designed through feedback linearization method, and applied to the system. Feedback linearization method here is used for two purposes; to attain an approximation of necessary system dynamics and to assess the performance of the proposed adaptive fuzzy controller by comparing the results of both adaptive fuzzy and feedback linearization controllers. An indirect adaptive fuzzy controller, resistant to parameter variations is then proposed. The general structure of the adaptive controller is specified in the first stage. In the second stage, its parameters are regulated with the aid of two fuzzy systems. Lyapunov stability theorem is used to regulate the system parameters such that the closed loop system is stabilized and zero tracking error is attained. Finally, the results of the proposed and the conventional approaches are compared. Results showed that the adaptive fuzzy controller performed more efficiently than the classical controller, with existing parameters variations.

In this paper a new strategy is proposed to design a fixed-structur e robust controller for a flexible beam. Robust controller designed by the conventional loop shaping method is not appropriate for a beam because of its high order and complicated form. Fixed-structure loop shaping control in conjunction with particle swarm optimization (PSO)algorithm is used to overcome this drawback. The performance and robust stability conditions of the loop shaping controller are formulated as the cost function in the optimization problem. PSO is adopted to optimize the parameters and cost function. The proposed control design and loop shaping method are successfully applied on the flexible beam, and results of the two approaches are compared. Simulation results show the superiorities of the proposed controller in terms of having a lower order and simple structure; besides the beam stability and robust performance are retained as well. Also in comparison to the solutions based on genetic algorithms, the use of PSO shows better efficiency in terms of computational time.

In this paper a new strategy is proposed to design a fixed-structure robust controller for a flexible beam. Robust controller designed by the conventional loop shaping method is not appropriate for a beam because of its high order and complicated form. Fixed-structure loop shaping control in conjunction with particle swarm optimization (PSO)algorithm is used to overcome this drawback. The performance and robust stability conditions of the loop shaping controller are formulated as the cost function in the optimization problem. PSO is adopted to optimize the parameters and cost function. The proposed control design and loop shaping method are successfully applied on the flexible beam, and results of the two approaches are compared. Simulation results show the superiorities of the proposed controller in terms of having a lower order and simple structure; besides the beam stability and robust performance are retained as well. Also in comparison to the solutions based on genetic algorithms, the use of PSO shows better efficiency in terms of computational time.

Th is paper modifies parameter identificat ion of singular systems with the aid of transformation of singular system to a new Strong equivalent counterpart. Singular systems should be transformed to an equivalent model in the first step of identification process. In fact choosing an appropriate equivalent singular model is of crucial importance. Inconvenient equivalent model may lead to divergence, excessive computation time and imp recise estimat ion results. Indeed a more desirable estimat ion result would be attained by reducing the number of init ial conditions. Tradit ional reduction methods used before for this purpose, but they resulted low accurate estimations because important dynamics of system have been omitted wrongly using those equivalencies. In this paper, a mo re accurate equivalency transformat ion of singular systems called Strong equivalency in co mbination with the Least Square identification algorith m is performed with the aim of revising the mentioned problems. This combination of the Strong equivalency together with the identification procedure is used for the first time. Thus this new configuration imp roves not only the estimation error convergence, but also the output tracking. Performance of the proposed method is illustrated in a practical singular electric network.