The energy consumed by the residential and commercial building sectors in the United States has been increasing at around 1.3\% per year over the past decade, making efficient building operations more crucial than ever. Model predictive control (MPC), which is a model-based control method, has been proposed as a solution for the control and optimization of building operations due to its ability to optimize control actions based on constraints such as cost and energy. However, widespread adoption of MPC in buildings is limited by the challenges in developing and training a control-oriented building model. Building modeling is a challenging task due to the presence of unmeasured time-varying heat disturbances due to people, lighting, and electricity, and the lack of full state measurements, resulting in a coupled state, disturbance, and model parameter estimation problem.

Despite being unmeasured, these time-varying heat disturbances are correlated to certain time-features like the time of the day and the day of the week for several building types and occupancy patterns. Hence, hybrid models, which combine physics-based models, to capture the underlying dynamics of the system, and data-driven models, used to forecast the disturbances, have been proposed as a potential method for control-oriented modeling of buildings. In our previous work, a low-order thermal resistance-capacitance network was formulated to capture the dynamics of the building space and a feedforward neural network (FNN) was used to forecast the time-varying unmeasured disturbances.

This thesis presents a generalized hybrid modeling framework to identify models for systems that are subject to unmeasured time-varying disturbances. The proposed hybrid modeling framework combines a parameterized low-order physics-based model and a feedforward neural network (FNN) and utilizes a novel three-step training methodology to simultaneously estimate both the physics-based and FNN model parameters. The aim of the three-step training methodology is to provide better model predictions compared to the predictions made by the same model trained with alternative strategies. A model validation approach is also provided as part of the training methodology. The effectiveness of the proposed modeling and training approach is demonstrated by applying it to model the thermal dynamics of a building space. The time features, which provide the desired model predictions, are first determined. The superiority of the three-step training methodology is demonstrated by comparing the predictions generated by the models trained with alternative strategies to those generated by the model trained using the three-step training methodology. These results demonstrate that the hybrid modeling framework is suitable for modeling systems with unmeasured time-varying disturbances, and that the three-step training methodology results in models with minimal prediction errors, with fewer number of iterations as compared to its alternatives. The impact of unavailability of full state measurements is studied. Finally, the ability for the hybrid modeling framework to reproduce the results is evaluated.

The chemical industry is a vital sector of the US economy. Maintaining optimal chemical process operation is critical to the future success of the US chemical industry on a global market. Traditionally, economic optimization of chemical processes has been addressed in a two-layer hierarchical architecture. In the upper layer, real-time optimization carries out economic process optimization by computing optimal process operation set-points using detailed nonlinear steady-state process models. These set-points are used by the lower layer feedback control systems to force the process to operate on these set-points. While this paradigm has been successful, we are witnessing an increasing need for dynamic market and demand-driven operations for more efficient process operation, increasing response capability to changing customer demand, and achieving real-time energy management. To enable next-generation market-driven operation, economic model predictive control (EMPC), which is an model predictive control scheme formulated with a stage cost that represents the process economics, has been proposed to integrate dynamic economic optimization of processes with feedback control.

Motivated by these considerations, novel theory and methods needed for the design of computationally tractable economic model predictive control systems for nonlinear processes are developed in this dissertation. Specifically, the following considerations are addressed: a) EMPC structures for nonlinear systems which address: infinite-time and finite-time closed-loop economic performance and time-varying economic considerations such as changing energy pricing; b) two-layer (hierarchical) dynamic economic process optimization and feedback control frameworks that incorporate EMPC with other control strategies allowing for computational efficiency; and c) EMPC schemes that account for real-time computation requirements. The EMPC schemes and methodologies are applied to chemical process applications. The application studies demonstrate the effectiveness of the EMPC schemes to maintain process stability and improve economic performance under dynamic operation as well as to increase efficiency, reliability and profitability of processes, thereby contributing to the vision of Smart Manufacturing.

Increasing reliance on wireless communication and complexity of cyberattacks have rendered industrial control systems (ICSs) such as process control systems (PCSs) (which are ICSs that operate chemical manufacturing processes) vulnerable to cyberattacks by malicious agents. In the past decade, several highly sophisticated cyberattacks (e.g., Stuxnet virus (2010), German steel mill attack (2014), Ukrainian power grid attack (2015), TRITON (2017)) have demonstrated that information technology (IT) infrastructure-based solutions to handling cyberattacks on control systems are insufficient on their own. An increasing body of research has focused on developing operational technology (OT)-based approaches to enhance the cyberattack resilience of PCSs. Cyberattack resilience here is defined as the ability of a PCS to minimize the impact of a cyberattack and recover from it. Research on cyberattack resilience of PCSs involves approaches that range from designing PCSs that are inherently attack-resilient to developing cyberattack detection, identification and mitigation schemes. Cyberattack detection schemes are OT-based anomaly detection schemes that reveal the presence of a cyberattack on a PCS by monitoring the process operational data for anomalies and are an important component of a cyberattack resilient PCS.

The motivating realization behind the work presented in this dissertation is that the influence of PCS design parameters may be exploited to reveal the presence of an ongoing cyberattack on a PCS. In the chapters that follow, several approaches for cyberattack detection are presented. First, a control screening approach that may be used to incorporate attack detectability within the conventional PCS design considerations is presented. The screening algorithm is based on a characterization of the interdependence between the PCS design parameters, and the ability of the detection scheme to detect the attack (attack detectability). Next, for a certain class of detection schemes monitoring a process, the relationship between the PCS design parameters, the closed-loop stability of the attacked process, and the detectability of certain attacks is rigorously characterized. Based on the characterization, for attack detection, it may be preferred to operate the process under performance degrading ``attack-sensitive'' parameters. To manage a potential tradeoff between attack detection and closed-loop performance, an active detection method utilizing switching between two control modes is developed. Under the active detection method, extended process operation is under a first (nominal) mode, the control parameters (called nominal parameters) for which are selected to meet traditional control design criteria. Under the second (attack-sensitive) mode, the process is operated with attack-sensitive parameters. The process is operated under the attack-sensitive mode intermittently to probe the process for an ongoing attack. Control parameter switching on a process under steady-state operation may induce transient behavior, which may trigger false alarms in the class of detection schemes. For processes with an invertible output matrix, a switching condition is imposed to select control parameter switching instances such that false alarms in the system are minimized.

To eliminate false alarms due to control switching on processes with a non-invertible output matrix, a reachable set-based detection scheme is developed. The reachable set-based cyberattack detection scheme guarantees a zero false alarm rate during transient attack-free process operation by tracking the evolution of the monitoring variable values with respect to their reachable sets of the attack-free process at each time step. Following this, a switching-enabled active detection method that utilizes the reachable set-based detection scheme to enable attack detection with a zero false alarm rate is presented. Furthermore, the control parameter switching instances between the nominal to attack-sensitive modes are randomized, thereby preserving the confidentiality of the detection method. Destabilization of a process for attack detection (as with operation under attack-sensitive mode) may not always be preferred. Two different alternate control modes that may be used to induce perturbations for active attack detection without destabilizing the attacked process are presented. To guarantee attack detection, the alternate control mode selected must induce ``attack-revealing'' perturbations in the process. Reachability analysis is used to present a set-based condition that if satisfied means that the control mode selected induces attack-revealing perturbations. Different models of false data injection attacks are considered. A screening algorithm that may be used to select an attack-revealing control mode for the active detection of attacks is presented. The application of all methods are applied to simulations of different illustrative processes to demonstrate their attack detection capabilities.

Carbon dioxide (CO2)-based demand control ventilation (DCV) automatically adjusts building ventilation rates based on indoor CO2 concentration. Since the indoor CO2 concentration is directly related to the occupancy, the purpose of CO2-based DCV is to conserve energy by reducing the ventilation rates during periods of low occupancy. In this work, two standard testing protocols for CO2 sensors and CO2-based DCV system controllers are developed and performed on several currently available CO2 sensors and DCV system controllers. Test results are provided and discussed.

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