Craig Rieger

Resilient control systems: Next generation design research

Since digital control systems were introduced to the market more than 30 years ago, the operational efficiency and stability gained through their use have fueled our migration and ultimate dependence on them for the monitoring and control of critical infrastructure. While these systems have been designed for functionality and reliability, a hostile cyber environment and uncertainties in complex networks and human interactions have placed additional parameters on the design expectations for control systems.

Notional examples and benchmark aspects of a resilient control system

Digital control system technology has pervaded most industries, leading to improvements in the efficiency and reliability of the associated operations. However, the ease of distributing and connecting related control systems for the purposes of increasing performance has resulted in interdependencies that can lead to unexpected conditions. Even with less complex designs, operators and engineers alike are often left with competing goals that are difficult to resolve. A fundamental reason for this dichotomy is that responsibilities lie with different disciplines, and operations are hosted on separate control systems. In addition, with the rising awareness of cyber security and diverse human interactions with control systems, an understanding of human actions from a malicious and benevolent standpoint is necessary. Resilience considers the multiple facets of requirements that drive the performance of control systems in a holistic fashion, whether they are security or stability, stability or efficiency, human interactions or complex interdependencies. As will be shown by example, current research philosophies lack the depth or the focus on the control system application to satisfy these requirements, such as graceful degradation of hierarchical control while under cyber attack. A resilient control system promises to purposefully consider these diverse requirements, developing an adaptive capacity to complex events that can lead to failure of traditional control system designs.

Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

Building Energy Management Systems (BEMSs) are essential components of modern buildings that are responsible for minimizing energy consumption while maintaining occupant comfort. However, since indoor environment is dependent on many uncertain criteria, performance of BEMS can be suboptimal at times. Unfortunately, complexity of BEMSs, large amount of data, and interrelations between data can make identifying these suboptimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD)-based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior; and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm-based BEMS. Six different scenarios were tested, and the presented Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more) than the alarm based BEMS. Furthermore, the Fuzzy-ADLD method identified cases that were missed by the alarm-based system, thus demonstrating potential for increased state-awareness of abnormal building behavior.

A hierarchical security architecture for cyber-physical systems

Security of control systems is becoming a pivotal concern in critical national infrastructures such as the power grid and nuclear plants. In this paper, we adopt a hierarchical viewpoint to these security issues, addressing security concerns at each level and emphasizing a holistic cross-layer philosophy for developing security solutions. We propose a bottom-up framework that establishes a model from the physical and control levels to the supervisory level, incorporating concerns from network and communication levels. We show that the game-theoretical approach can yield cross-layer security strategy solutions to the cyber-physical systems.

Advanced control strategies for heating, ventilation, air-conditioning, and refrigeration systems—An overview: Part I: Hard control

A chronological overview of the advanced control strategies for heating, ventilation, air-conditioning, and refrigeration (HVAC&R) is presented in this article. The overview focuses on hard-computing or control techniques, such as proportional-integral-derivative, optimal, nonlinear, adaptive, and robust; soft-computing or control techniques, such as neural networks, fuzzy logic, genetic algorithms; and on the fusion or hybrid of hard- and soft-control techniques. Thus, it is to be noted that the terminology “hard” and “soft” computing/control has nothing to do with the “hardware” and “software” that is being generally used. Part I of a two-part series focuses on hard-control strategies, and Part II focuses on soft- and fusion-control in addition to some future directions in HVAC&R research. This overview is not intended to be an exhaustive survey on this topic, and any omission of other works is purely unintentional.

Resilient control systems: A multi-agent dynamic systems perspective

“Resilience” describes how systems operate at an acceptable level of normalcy despite disturbances or threats. In this paper we first consider the interdependencies inherent in critical infrastructure systems and how resilience mitigates associated risks and then define “resilience” in distinction from convention control engineering. We then introduce the concepts “agent” and “multi-agent systems” (MAS) to consider the distributed nature of critical infrastructure control systems and illustrate the application of computational intelligence to MAS event-based dynamics (management, coordination) and time-based dynamics (execution) to manage policy and coordinate assets. In addition, we consider the optimal stabilization of the MAS and suggest the extension of graph theory to MAS execution layers. The closing discussion provides an overview of how to achieve critical infrastructure resilience through advanced control engineering.

Advanced control strategies for HVAC&R systems—An overview: Part II: Soft and fusion control

A chronological overview of the advanced control strategies for HVAC&R is presented. The overview focuses on hard-computing or control techniques, such as proportional-integral-derivative, optimal, nonlinear, adaptive, and robust; soft-computing or control techniques, such as neural networks, fuzzy logic, genetic algorithms; and the fusion or hybrid of hard and soft control techniques. Part I focused on hard-control strategies; Part II focuses on soft and fusion control and some future directions in HVA&R research. This overview is not intended to be an exhaustive survey on this topic, and any omissions of other works is purely unintentional.

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a fuzzyneural data fusion engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms; 2) anomalous behavior detector using self-organizing fuzzy logic system; and 3) artificial neural network-based system modeling and prediction. The improved control system stateawareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network-based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratorys hybrid energy system facility known as HYTEST. Experiment results demonstrate that the proposed FNDFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold-based alarm systems.

Computational intelligence based anomaly detection for Building Energy Management Systems

In the past several decades Building Energy Management Systems (BEMSs) have become vital components of most modern buildings. BEMSs utilize advanced microprocessor technology combined with extensive sensor data collection and communication to minimize energy consumption while maintaining high human comfort levels. When properly tuned and operated, BEMSs can provide significant energy savings. However, the complexity of the acquired sensory data and the overwhelming amount of presented information renders them difficult to adjust or even understand by responsible building managers. This inevitably results in suboptimal BEMS operation and performance. To address this issue, this paper reports on a research effort that utilizes Computational Intelligence techniques to fuse multiple heterogeneous sources of BEMS data and to extract relevant actionable information. This actionable information can then be easily understood and acted upon by responsible building managers. In particular, this paper describes the use of anomaly detection algorithms for improving the understandability of BEMS data and for increasing the state-awareness of building managers. The developed system utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to automatically build a model of normal BEMS operations and detect possible anomalous behavior. In addition, linguistic summaries based on fuzzy set representation of the input values are generated for the detected anomalies which increase the understandability of the presented results.

Resilient control system execution agent (ReCoSEA)

In an increasingly connected world, critical infrastructure systems suffer from two types of vulnerability. The first is the traditionally recognized problem of monitoring the systems for faults and failures, recognizing and analyzing data, and responding with real understanding to the problems of the system. Increasingly complex systems create the opportunity for single points of failure to cascade when inaccurate assessment of system health increases response time or leads to faulty analysis of the problems involved. A second problem involves vulnerability to cyber intrusion, in which malignant actors can mask system degradation or present false data about system status. A resilient system will protect stability, efficiency, and security. To ensure these three states, the system must react to changing conditions within the system with coordination: no one component of the system can be allowed to react to problems without real consideration of the effects of that action on other components within the system. Systems with multi-agent design typically have three layers of action, a management layer, a coordination layer, and an execution layer. A resilient multi-agent system will emphasize functions of the execution layer, which has the responsibility of initiating actions, monitoring, analyzing, and controlling its own processes, while feeding information back to the higher levels of management and coordination. The design concept of a resilient control system execution agent (ReCoSEA) grows out of these underpinnings, and through the use of computational intelligence techniques, this paper suggests an associated design methodology.