Supervisory Control Performance Research Articles

Various uncertainties self-correction method for the supervisory control of a hybrid cooling system in data centers

The uncertainties of the crucial parameters have a considerable influence on the supervisory control performance of the water side free cooling system in data centers, especially for the model-based optimal control. To reduce the uncertainty of the key parameters in an operational data center hybrid cooling system, this study proposed an uncertainties self-correction method based on Bayesian Inference and Markov Chain Monte Carlo (BI-MCMC) theory. Four novel enhanced self-correction strategies, including the benchmarks extended, adding sensitivity coefficient, local correction and prior distribution updated, were developed to handle the negative impacts caused by the various uncertain working conditions and complex relationships between parameters. The performance of the proposed method was fully investigated under the cases with single/multiple uncertain parameters with different degrees of uncertainty and deviation. For the uncertainties problem with single parameter error scenario, the basic BI-MCMC method reduced the error by at least 92.5%. For the multiple parameter uncertain scenarios, the designed enhanced strategies can significantly reduce the degree of the uncertainty, i.e. the correction accuracy is up to 98% and the absolute correction deviations are not more than 0.02. Therefore, the proposed method could provide a solution to the current challenges in handling the uncertainties and establishing a reliable model for the supervisory control.

A Discrete-Event Supervisory Control for the AC Microgrid

Based on Supervisory Control Theory, this paper introduces a systematic and bottom-up approach to the development and synthesis of a microgrid supervisory control (SC) to formalize a discrete power management strategy. The proposed approach is general, transparent, and rigorous. The envisioned strategy is applied to the islanded mode of operation of an AC microgrid, which includes a battery energy storage system, a solar photovoltaic unit, and a conventional rotating generator as distributed energy resources. The SC is nonblocking and maximally permissive, i.e., it enables transitions among all possible discrete operational scenarios of the microgrid and identifies those that must be avoided. The proposed supervisory control is implemented in an industrial-grade PLCnext hardware platform; the supervisory control performance is evaluated through case studies conducted in a RTDS-based real-time hardware-in-the-loop environment.

Impact of uncertainties on the supervisory control performance of a hybrid cooling system in data center

Model-based optimal control (MBOC) is a promising method to reap the potential of the waterside free cooling system in data center. While the theoretical energy savings are impressive, uncertainties in the model, sensors, and actuators could hinder this control strategy's practical application. This study quantified the impact of various uncertainties on the supervisory control method in two steps. First, sensitivity analysis was conducted using the Morris method and the one-at-a-time method to identify the influential impact of uncertain elements. Next, Monte-Carlo simulation was designed to perform an uncertainty study and evaluate the robustness and energy efficiency of the control method. A virtual emulator for simulating the proposed novel hybrid cooling system in data centers in combination with different supervisory control methods was developed. Full-year simulations indicate that the impact of uncertainty on the control performance of the MBOC strategy is greater than that of conventional control strategy; under various uncertainties, the energy consumption and operation mode prediction error rate of the MBOC method were increased by 43.6% and 99.2%, respectively. The research suggests that, if MBOC is adopted for the hybrid cooling system control, more efforts should be placed on reducing the uncertainties from various sources.

Availability Principle Applied to a Fuzzy Supervisory Control of a Pilot Flotation Column

This work presents a study of fuzzy expert control system, based on the availability principle, of a pilot flotation column at the Hybrid Process Control Laboratory in Santa Maria University. Sensors and actuators are communicated to a programmable Logic Controller (PLC) where calibrations and local control are implemented, and the data is sent to a PC network, where a Distributed Control System (DCS) is running. Process model predictions are obtained by running a simulator fed with actual operating variables and virtual variables associated to feed characteristics. All data is displayed at PC stations and historical data files are also built for further analysis. Operating variables (real and virtual) can also be analyzed off-line by advanced software tools. Supervisory control running on PC network writes DCS set points on PLC.The supervisory control performance was experimentally evaluated by keeping the mineralogical objectives of concentrate grade and recovery in narrow bands. The operation of the supervisor was compared to a base case where there is no supervisory control, and each experiment was subjected to stochastic auto-correlated disturbances in the feed conditions. The fuzzy supervisory control, unlike conventional control, is more capable of maintaining the metallurgical results within control limits during almost 90% of the operating time. An availability principle for manipulated variables was successfully tested acting on top of the fuzzy logic decision system.

Interactive effects of the COMT gene and training on individual differences in supervisory control of unmanned vehicles.

We examined whether a gene known to influence dopamine availability in the prefrontal cortex is associated with individual differences in learning a supervisory control task. Methods are needed for selection and training of human operators who can effectively supervise multiple unmanned vehicles (UVs). Compared to the valine (Val) allele, the methionine (Met) allele of the COMT gene has been linked to superior executive function, but it is not known whether it is associated with training-related effects in multi-UV supervisory control performance. Ninety-nine healthy adults were genotyped for the COMT Val158Met single nucleotide polymorphism (rs4680) and divided into Met/Met, Val/Met, and Val/Val groups. Participants supervised six UVs in an air defense mission requiring them to attack incoming enemy aircraft and protect a no-fly zone from intruders in conditions of low and high task load (numbers of enemy aircraft). Training effects were examined across four blocks of trials in each task load condition. Compared to the Val/Met and Val/Val groups, Met/Met individuals exhibited a greater increase in enemy targets destroyed and greater reduction in enemy red zone incursions across training blocks. Individuals with the COMT Met/Met genotype can acquire skill in executive function tasks, such as multi-UV supervisory control, to a higher level and/or faster than other genotype groups. Potential applications of this research include the development of individualized training methods for operators of multi-UV systems and selecting personnel for complex supervisory control tasks.

Supervisory control of multiple robots in dynamic tasking environments

A military targeting environment was simulated to examine the effects of an intelligent route-planning agent RoboLeader, which could support dynamic robot re-tasking based on battlefield developments, on the performance of robotics operators. We manipulated the level of assistance (LOAs) provided by RoboLeader as well as the presence of a visualisation tool that provided feedback to the participants on their primary task (target encapsulation) performance. Results showed that the participants’ primary task benefited from RoboLeader on all LOAs conditions compared to manual performance; however, visualisation had little effect. Frequent video gamers demonstrated significantly better situation awareness of the mission environment than did infrequent gamers. Those participants with higher spatial ability performed better on a secondary target detection task than did those with lower spatial ability. Finally, participants’ workload assessments were significantly lower when they were assisted by RoboLeader than when they performed the target entrapment task manually. Practitioner Summary: This study demonstrated the utility of an intelligent agent for enhancing robotics operators’ supervisory control performance as well as reducing their workload during a complex urban scenario involving moving targets. The results furthered the understanding of the interplay among level-of-autonomy, multitasking performance and individual differences in military tasking environments.

Team Performance and Communication within Networked Supervisory Control Human-Machine Systems

The effects of task load, automation reliability and team communication on supervisory control performance were examined using a multi-UAV simulation with two operators. Performance was degraded by high task load and improved with an automated decision aid. In addition, team working memory, defined as the average of individual working memory capacity scores, was associated with superior team performance. Higher levels of task load increased the amount of information communicated by teams whereas the presence of an automated decision aid decreased the amount of information communicated by teams. The results are discussed in relation to models of team cognition for teams performing similar tasks in a shared, networked human-machine system.

Adaptive fuzzy controller design for a class of uncertain nonlinear MIMO systems

A combined adaptive fuzzy output feedback control scheme is presented for a class of multiple-input–multiple-output (MIMO) nonlinear systems with the unmeasured states. A state observer is used to estimate the unmeasured states of the systems. The resulting closed-loop system consists of the state observer and four control components, where the control components are the combined adaptive fuzzy controller, supervisory controller, H∞ robust control item and auxiliary compensation item. Compared with the existing results in the observer design, the main advantages of this scheme are that (1) the combined adaptive fuzzy control item, which can trade off the fuzzy descriptions for controlled object rules and control action rules, is designed in the controller; (2) the proposed controller has the supervisory control performance. The stability of the closed-loop system is analyzed by using Lyapunov analysis method. Simulation results validate the effectiveness of the proposed control scheme.

A KNOWLEDGE-BASED SYSTEM TO MODEL HUMAN SUPERVISORY CONTROL IN DYNAMIC PLANNING

With the increases in the levels of automation and computerization, supervisory control systems are becoming increasingly common in commercial and military applications. A supervisory control system consists of one or more human operators interacting with highly automated components such as those seen in satellite ground control, flexible manufacturing systems, or nuclear power plants. Humans typically perform cognitively intense tasks such as monitoring, planning, real-time control, and troubleshooting, and are ultimately responsible for the safe and efficient operation of the overall system. Although developments on supervisory control have led to useful applications in interface design and automation, there is a scarcity of research that empirically evaluates human decision making in supervisory control through emulation of task performance using knowledge-based systems. In the context of dynamic planning involving simulated search and rescue missions using ground based autonomous robots and uninhabited aerial vehicles, we developed a knowledge-based system that mimics supervisory control performance. This paper describes the application domain, the details of the simulation model, and the implementation and assessment of a knowledge-based system that mimics human supervisory control performance.

The effects of personality on supervisory control tasks

Personality is a collection of holistic human traits: character, perception of the world, covert and overt behaviors, uniqueness in functioning and coping within cultural setting, and response to different environmental stimuli. While many studies have focused on perception and cognitive aspects of supervisory control (SC), not much consideration is given to the effect of personality on supervisory control performance. In this paper, results showing the effects of Type A and Type B personality types on SC and workload perception are discussed. One major finding of the study shows that Type A individuals respond better than Type B to monitoring single tasks for automation failure, and they also experience the least amount of workload in both single and multiple SC tasks. However, Type A individuals made significantly more errors, than Type B individuals, during system monitoring and resource management tasks.

The use of measures of entropy in evaluating human supervisory control of a manufacturing system

The use of measures of entropy for evaluating human supervisory control performance in a flexible manufacturing system is described. The measures are especially useful in explaining performance when graphic displays intended for enhancing human pattern-recognition capabilities are available and under task conditions with contrasting types of workload.