Supervisory Control Research Articles

A multi-objective approach for manufacturing systems with multiple production routes based on supervisory control theory and heuristic algorithms

A multi-objective approach for manufacturing systems with multiple production routes based on supervisory control theory and heuristic algorithms

A Critical Perspective on Current Research Trends in Building Operation: Pressing Challenges and Promising Opportunities

Despite the development of increasingly efficient technologies and the ever-growing amount of available data from Building Automation Systems (BAS) and connected devices, buildings are still far from reaching their performance potential due to inadequate controls and suboptimal operation sequences. Advanced control methods such as model-based controls or model-based predictive controls (MPC) are widely acknowledged as effective solutions for improving building operation. Although they have been well-investigated in the past, their widespread adoption has yet to be reached. Based on our experience in this field, this paper aims to provide a broader perspective on research trends on advanced controls in the built environment to researchers and practitioners, as well as to newcomers in the field. Pressing challenges are explored, such as inefficient local controls (which must be addressed in priority) and data availability and quality (not as good as expected, despite the advent of the digital era). Other major hurdles that slow down the large-scale adoption of advanced controls include communication issues with BAS and lack of guidelines and standards tailored for controls. To encourage their uptake, cost-effective solutions and successful case studies are required, which need to be further supported by better training and engagement between the industry and research communities. This paper also discusses promising opportunities: while building modelling is already playing a critical role, data-driven methods and data analytics are becoming a popular option to improve buildings controls. High-performance local and supervisory controls have emerged as promising solutions. Energy flexibility appears instrumental in achieving decarbonization targets in the built environment.

Robust safety zones for manipulators with uncertain dynamics in collaborative robotics

ABSTRACT In this paper, an approach for computing online safety zones for collaborative robotics in a robust way, despite uncertain robot dynamics, is proposed. The strategy implements the speed and separation monitoring paradigm, and considers human and robot enclosed in bounding volumes. The human-robot collaboration is monitored by a supervisory controller that guides the robot to stop along a path-consistent trajectory in case of collision danger between human and robot. The size of the robot safety zone is minimized online according to the stop time of the manipulator, and the uncertain robot dynamics is considered using interval arithmetic to ensure compliance with the joint torques limits even in case of imperfect knowledge of the dynamic model parameters. The results verify the effectiveness of the proposed approach, and evaluate the influence of dynamics variations on human-robot collaboration.

Wind turbine fault detection based on spatial-temporal feature and neighbor operation state

Wind turbines (WTs) are complex pieces of engineering. They are generally exposed to harsh environmental conditions. Thus, major changeable working conditions and multidimensional dynamic correlation arise, resulting in unsatisfactory fault detection accuracy. A WT fault detection method based on spatio-temporal features and neighbor operation state is proposed in this study to tackle the aforementioned challenge. A spatio-temporal feature extraction method based on accumulated mutual information is proposed to describe the dynamic correlation of multiple features during the offline phase. Then, the spatio-temporal features are applied to build the weighted k-nearest neighbor fault detection model. In the online phase, the weighted distance between the online sample and the neighbor operation state is adopted to identify the anomaly state. Moreover, a dynamic threshold based on the neighbor sample is designed to cope with the changeable operating conditions. The proposed methods are demonstrated using FAST data and supervisory control and data acquisition data, which consider different fault types. The experiment results show that compared with similar methods and traditional fault detection methods, the operational characteristics of WTs’ components can be better described by the proposed spatio-temporal feature extraction method, and the false alarm rates can be considerably reduced by the dynamic threshold.

Advancements and challenges in hybrid energy storage systems: Components, control strategies, and future directions

Hybrid energy storage systems (HESSs) can considerably improve the dependability, efficiency, and sustainability of energy storage systems (ESSs). This study examines the components of HESS, including the different types of ESSs that are typically used in hybrid systems. The advantages of integrating various ESS types in hybrid systems are also covered, along with design considerations for HESS and the interconnection topologies, which include passive, active, and semi-active architectures. For HESS, several control techniques both traditional and intelligent-have been suggested, including filter-based control, droop control, sliding mode control, rule-based control, deadbeat control, model predictive control, fuzzy logic control, optimization-based control, supervisory control, feedforward control, unified control, and robust control. The current work also discusses HESS's difficulties and potential possibilities, including technological advancement, standardization, interdisciplinary cooperation, grid integration, environmental sustainability, security and dependability, and legislative and regulatory frameworks.

Improving the operational efficiency of water desalination systems through the integration of GIS-SCADA

ABSTRACT The overlap of different fields has made it easy to introduce a real development of new technologies. New techniques used in the water sector are introduced; the importance of integration with each other will be increased to work as one unit with more efficiency and effectiveness. Integrating the real-time control of a supervisory control and data acquisition system (SCADA) and the data management and analysis capabilities of a geographic information system (GIS) can assist operators and decision-makers in disaster response and better system control. The objective of this paper is to evaluate the potential use of SCADA in conjunction with GIS to control and manage a water desalination plant network. The aid of the developed integrated system in the management of a water desalination system can be accessed on an interactive single dashboard for the network to simulate real situations through the data collected at each site that can be provided in real-time by SCADA, in addition to the results of analysis of these data that help to take the right decision and deliver the instructions thereon directly to the field devices to deal with any case of emergencies. In addition, this paper introduces an implementation approach of a new category of decision-support tools based on the integration of GIS and SCADA in implemented water desalination plants at the National Water Research Center (NWRC). A general review of both systems including their potentials in water desalination systems and the possibility of integration with each other has been introduced. This study demonstrates how integrating GIS and SCADA into a single dashboard can aid in data acquisition and provide a better understanding of the communication between the various sites and systems’ facility administration, despite the fact that such integration is uncommon because both systems use different IT platforms.

Supervisory control of business processes with resources, parallel and mutually exclusive branches, loops, and uncertainty

A recent direction in Business Process Management studied methodologies to control the execution of Business Processes under several sources of uncertainty in order to always get to the end by satisfying all constraints. Current approaches encode business processes into temporal constraint networks or timed game automata in order to exploit their related strategy synthesis algorithms. However, the proposed encodings can only synthesize single-strategies and fail to handle loops. To overcome these limits we propose an approach based on supervisory control. We consider structured business processes with resources, parallel and mutually exclusive branches, loops, and uncertainty. We provide an encoding into finite state automata and prove that their concurrent behavior models exactly all possible executions of the process. After that, we introduce tentative commitment constraints as a new class of constraints restricting the executions of a process. We define a tree decomposition of the process that plays a central role in modular supervisory control, and we prove that this modular approach is equivalent to the monolithic one. We provide an algorithm to compute the finest tree decomposition to reduce the computational effort of synthesizing supervisors.

Gorillas on a boat: Inattentional blindness during supervisory control of autonomous vessels

When focused on a task, unexpected visual stimuli can go by unnoticed. Inattentional blindness may be problematic for supervisory controllers of autonomous vessels, because this role specifically relies on identifying unexpected stimuli in case intervention is needed. In a simulation-based experiment (n=32), we show that 50% of participants did not perceive an unexpected visual stimulus (a gorilla passenger) when assigned the task of supervisory controller for an autonomous ferry. Additionally, eye-tracking showed that 12 of the 16 players who did not report the gorilla in fact gazed directly at it. Results also showed no correlation to skillset (gamer or navigator) nor to vigilance (5- or 30-minute scenarios); they did, however, show positive correlation to multitasking (1 or 3 supervised vessels). We discuss implications for designing work tasks for supervisory control of remotely operated and autonomous vessels.

Hybrid Pressure Sensor Based on Carbon Nano-Onions and Hierarchical Microstructures with Synergistic Enhancement Mechanism for Multi-Parameter Sleep Monitoring.

With the existing pressure sensors, it is difficult to achieve the unification of wide pressure response range and high sensitivity. Furthermore, the preparation of pressure sensors with excellent performance for sleep health monitoring has become a research difficulty. In this paper, based on material and microstructure synergistic enhancement mechanism, a hybrid pressure sensor (HPS) integrating triboelectric pressure sensor (TPS) and piezoelectric pressure sensor (PPS) is proposed. For the TPS, a simple, low-cost, and structurally controllable microstructure preparation method is proposed in order to investigate the effect of carbon nano-onions (CNOs) and hierarchical composite microstructures on the electrical properties of CNOs@Ecoflex. The PPS is used to broaden the pressure response range and reduce the pressure detection limit of HPS. It has been experimentally demonstrated that the HPS has a high sensitivity of 2.46 V/104 Pa (50-600 kPa) and a wide response range of up to 1200 kPa. Moreover, the HPS has a low detection limit (10 kPa), a high stability (over 100,000 cycles), and a fast response time. The sleep monitoring system constructed based on HPS shows remarkable performance in breathing state recognition and sleeping posture supervisory control, which will exhibit enormous potential in areas such as sleep health monitoring and potential disease prediction.

A Real-Time Supervisory Control Strategy for Roll-to-Roll Dry Transfer of 2D Materials and Printed Electronics

With the advancement of manufacturing technology in transfer printing and two-dimensional materials fabrication, roll-to-roll (R2R) dry transfer by mechanical peeling is becoming a promising process for efficient transfer of thin film materials from a donor substrate to a target substrate for device fabrication. As a key process variable, the peeling angle determines the loading condition of the R2R dry transfer process and thus the quality of the transferred material. Controlling the peeling angle is challenging in a R2R process because of the adhesion energy and peeling front velocity in addition to the flexible substrates involved in the peeling process. In this article, a real-time supervisory control strategy is developed for the R2R mechanical peeling process. A strain energy-based peeling process model is employed to estimate the peeling front velocity and the adhesion energy. The real-time estimation is then used to generate reference signals for web tension controllers to track the desired peeling angles. The proposed control strategy is demonstrated with both simulation and experimental results.

K-nearest neighbour and K-fold cross-validation used in wind turbines for false alarm detection

Maintenance management is critical to ensure the efficiency and competitiveness of energy production in the wind energy industry. Current maintenance operations are planned according to the alarm distribution of the wind farm provided by the supervisory control and data acquisition system, and false alarms may lead to unnecessary operation. The detection of false alarms is critical to ensure proper wind turbine maintenance management plans because false alarms increase the complexity and the costs of maintenance operations. This paper proposes a methodology based on K-nearest neighbour algorithms that compare different k-fold cross-validation values for false alarm detection. A real case scenario formed by three real wind turbines is presented to test the reliability of the methodology. The signal and alarm dataset are acquired by the supervisory control and data acquisition system and an alarm log as a response variable. It is demonstrated that the performance of the three wind turbines is analogous and the variations of the k-cross validation value showed that the accuracy does not increase significantly. The proposed methodology presented an accuracy of 98 % and more than 22 % of false alarms were detected in the case study. These outcomes demonstrate the robustness of the proposed approach for detecting false alarms in wind turbines.

A Micro Energy Grid Optimal Design and Economic Operation Using Genetic Algorithms

This paper presents an optimal design procedure and economic operational scheduling of micro energy grids (MEGs). The optimization objectives are to minimize cost, carbon dioxide emissions, and energy deficiency. The energy sources and conversion technologies included in this study are renewable-based sources (wind and photovoltaic), a furnace, an electrical heater, a main power grid, and a local power station. Two proposed control levels are applied to control the operation of the MEG. The supervisor control level selects the energy supplier based on price and/or availability. The inner control level dynamically matches the demand profile with the supply profile. The control loops guarantee dynamic matching between the demand profile and supply profile. Two scenarios are simulated, zero interest rates and 5.25% interest rates. The results showed renewables contribute with a significant share as an energy source, however, higher interest rates would negatively impact this contribution. It also confirms that carbon taxes can reduce the use of fossil fuels as an energy source.

Vulnerability analysis and modeling false data injection attack against voltage source converter in renewable generation power system

With the increasing penetration of renewable resources, more power electronic devices that need communication with control centers may bring a novel risk of cyber attacks. This paper investigates the vulnerability of the hierarchical control and proposes a false data injection attack (FDIA) constructing algorithm against voltage source converters. The attack can be accomplished via a physical attack generator or falsification via attacking supervisory control and data acquisition system. By developing the FDIA model against state estimation, the proposed attack model can circumvent bad data detection in the secondary control loop. The tests are carried out on a single converter infinite bus benchmark and an IEEE 34-bus system. The results show that the proposed attack model can mislead the system to produce threatening oscillation.

Optimization methods to solve the set covering problems

Phasor Measurement Unit Placement aims to decrease the cost this phasor equipment while maintaining the observability of the electrical distribution systems. This placement can be modeled as a set covering problem. Synchronized phasor data provides electrical systems’ information with higher quality and frequency than Remote Terminal Units. These data enable the implementation of faster than real-time state estimators. Thus, supervisory control and data acquisition systems can be replaced by automatically monitored systems that add reliability, efficiency, and resilience to smart grid systems. Due to the flexibility and large-scale in the distribution systems, heuristic methods have frequently been applied to this placement problem. Since the use of Chemical Reaction Optimization (CRO) algorithm is not recurrent, we have decided to implement it together with a topological system observability analysis. Initial population creation, parameterization, elementary reactions of CRO, and local search strategies are presented in this study. Computational results were performed in IEEE power system test cases. The optimization method proposed has obtained optimal results at a lower computation time than the literature. To test in large-scale, we have applied another optimization method to Set Covering Problems from Beasley library, with different parameters. However, the results were not satisfactory indicating that the CRO might not be suitable to some large-scale instances of the Set Covering Problems this library. In this case, further studies are required.

Design and implementation of a state estimator in a real distribution network

AbstractControlling and operating modern distribution networks are challenging tasks. The implementation of monitoring systems and the development of tools are helping to address this challenge. State estimators are one of the most promising tools for this purpose. These are used to reconstruct the current state of a system based on measurement outputs, which can then be used to design controllers that can adjust the inputs to achieve the desired output. This article deals with the problem of estimating the state of a real‐world distribution network. A combination of conventional supervisory control and data acquisition is used to estimate the state, together with a synchrophasor monitoring system. The proposed method uses the Fortescue transformation to separate the three‐phase coupled equations into three independent modal measurement equations. Single‐phase estimators based on the extended Kalman filter are used to solve three sub‐problems associated with the sequence reference frame, which correctly capture the transient and steady‐state behaviour of the distribution network. The proposed estimation method is easy to implement and robust to measure model noise and uncertainties. Furthermore, an already‐established distribution network tests the estimation scheme under various conditions.

This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving.

This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement. Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect "edge cases" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time. Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions-with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded. DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload-though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation. Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level. These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation.

Design, Development of a Diversified Implemention of a Supervisory Control And Data Acquisition based VLSI System (SCADA) framework Utilizing Microcontroller based Programmable Logic Controllers

In this paper, the designing & implementing of a supervisory control and data acquisition system (SCADA) framework utilizing microcontroller based programmable logic controllers is presented in brief with some of the simulated results. This article shows the automation of a process by interfacing a weighing balance with Supervisory Control And Data Acquisition System (SCADA) through a Programmable Logic Controller (PLC). The research work was taken up as a part of the consultation work at a cigarette manufacturing firm. The detailed study of the entire cigarette manufacturing process revealed that after the processing of tobacco, which is the final stage in the production department, the processed tobacco is weighed in bins on a weighing balance and manually recorded by an operator. The operator has to keep a count of the bins and sum them up at the end of the day. This involves consumption of time and human labor. The aim of our research work is to automate this process so that the information regarding various blends of cigarette and their respective weights can be made available at every PC terminal at the end of the day. For storing this information, we are using the MS-Excel spreadsheet and writing in it with the help of SCADA script. The work discusses in detail the design, architecture, programming and implementation of a PLC. It also covers the various features offered by the SCADA software and also deals with the interfacing of the PLC and SCADA. The interface RS232C / RS485 is used. This work is an online research project which has now been implemented in the firm and yielding desired results. The simulated & experimental results shows the power of the methodology that has been developed in this research paper.

Cyberphysical System Modeled with Complex Networks and Hybrid Automata to Diagnose Multiple and Concurrent Faults in Manufacturing Systems

Cyber–physical systems use digital twins to provide advanced monitoring and control functions, including self-diagnosis. The digital twin is often conceptualized as a 3D model, but mathematical models implemented in numerical simulations are required to reproduce the dynamical and functional characteristics of physical systems. In this work, a cyber–physical system scheme is proposed to monitor and diagnose failures. The virtual system, embedded at the supervisory control level, combines concepts from complex networks and hybrid automata to detect failures in the hardware components and in the execution of the sequential logic control. An automated storage and retrieval system is presented as a case study to show the applicability of the proposed scheme. The functional test and the obtained results validate the implemented system that is shown to be capable of fault diagnosis and location in real time. The online execution of the digital twin present several advantages for diagnosing multiple concurrent failures in sensors, actuators, and the control unit. This approach can be incorporate into diverse manufacturing systems.

Correlation analysis based relevant variable selection for wind turbine condition monitoring and fault diagnosis

Wind turbines’ fault diagnosis under complex environments and disturbances is significant to maintaining high reliability and secure operation over a prolonged period of time. Due to the difficulty of installing additional sensors, the supervisory control and data acquisition system is the only path for condition monitoring and fault diagnosis. However, the complexity of numerous variables bogged down the situation of diagnosis. Hence, this paper proposes a correlation analysis method to filter the variables for maximizing redundant data suppression first. Secondly, a data utility maximization method based on a prior-posterior support vector machine is proposed. Finally, a series of parallel support vector machines are used to realize multi-condition monitoring and fault diagnosis. Experiment results illustrate the effectiveness, robustness, and generality of the method.

Hierarchical Interface-Based Supervisory Control for Verification and Validation of a Cyber-Physical System

Hierarchical Interface-Based Supervisory Control for Verification and Validation of a Cyber-Physical System