Control of many individual controllers or control loops
Supervisory control is a general term for control of many individual controllers or control loops, such as within a distributed control system. It refers to a high level of overall monitoring of individual process controllers, which is not necessary for the operation of each controller, but gives the operator an overall plant process view, and allows integration of operation between controllers.
A more specific use of the term is for a Supervisory Control and Data Acquisition system or SCADA, which refers to a specific class of system for use in process control, often on fairly small and remote applications such as a pipeline transport, water distribution, or wastewater utility system station.
Forms
Functional levels of a typical Distributed Control System, showing the supervisory level.
Supervisory control often takes one of two forms. In one, the controlled machine or process continues autonomously. It is observed from time to time by a human who, when deeming it necessary, intervenes to modify the control algorithm in some way. In the other, the process accepts an instruction, carries it out autonomously, reports the results and awaits further commands. With manual control, the operator interacts directly with a controlled process or task using switches, levers, screws, valves etc., to control actuators. This concept was incorporated in the earliest machines which sought to extend the physical capabilities of man. In contrast, with automatic control, the machine adapts to changing circumstances and makes decisions in pursuit of some goal which can be as simple as switching a heating system on and off to maintain a room temperature within a specified range. Sheridan [1] defines supervisory control as follows: "in the strictest sense, supervisory control means that one or more human operators are intermittently programming and continually receiving information from a computer that itself closes an autonomous control loop through artificial effectors to the controlled process or task environment."
Other points
Robotics applications have traditionally aimed for automatic control. Automatic control requires sensing and responding appropriately to all combinations of circumstances which can present problems of overwhelming complexity. A supervisory control scheme offers the prospect of solving the automation problem incrementally and leaving those problems unsolved to be handled by the human supervisor.
Communications delay does not have the same impact on this control scheme. All time critical feedback occurs at the slave where the delays are negligible. Instability is thus avoided without modifying the feedback loop. Communications delay, in this case, slows the rate at which an operator can assign tasks to the slave and determine whether those tasks have been successfully carried out.
Thomas B. Sheridan, a researcher of supervisory control and other subjects and professor of mechanical engineering at MIT
Supervisory control theory– Method for automatically synthesizing supervisors that restrict the behavior of a plant
References: Human Supervisory Control
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Sarter, N. and Amalberti, R. (Eds.) (2000). Cognitive engineering in the aviation domain. Lawrence Erlbaum Associates.
Sheridan, T. B. (1992). Telerobotics, automation, and human supervisory control. MIT Press.
Sheridan, T. B. (2002). Humans and automation: System design and research issues. Wiley.
Sheridan, T. B. (Ed.) (1976). Monitoring behavior and supervisory control. Springer.
Woods, D. D. and Roth, E. M. (1988). Cognitive engineering: Human problem solving with tools. Human Factors, 30, 4, 415-430.
Instrumentation is a collective term for measuring instruments, used for indicating, measuring, and recording physical quantities. It is also a field of study about the art and science about making measurement instruments, involving the related areas of metrology, automation, and control theory. The term has its origins in the art and science of scientific instrument-making.
Automation describes a wide range of technologies that reduce human intervention in processes, mainly by predetermining decision criteria, subprocess relationships, and related actions, as well as embodying those predeterminations in machines. Automation has been achieved by various means including mechanical, hydraulic, pneumatic, electrical, electronic devices, and computers, usually in combination. Complicated systems, such as modern factories, airplanes, and ships typically use combinations of all of these techniques. The benefit of automation includes labor savings, reducing waste, savings in electricity costs, savings in material costs, and improvements to quality, accuracy, and precision.
A distributed control system (DCS) is a computerized control system for a process or plant usually with many control loops, in which autonomous controllers are distributed throughout the system, but there is no central operator supervisory control. This is in contrast to systems that use centralized controllers; either discrete controllers located at a central control room or within a central computer. The DCS concept increases reliability and reduces installation costs by localizing control functions near the process plant, with remote monitoring and supervision.
James Sacra Albus was an American engineer, Senior NIST Fellow and founder and former chief of the Intelligent Systems Division of the Manufacturing Engineering Laboratory at the National Institute of Standards and Technology (NIST).
An industrial process control or simply process control in continuous production processes is a discipline that uses industrial control systems and control theory to achieve a production level of consistency, economy and safety which could not be achieved purely by human manual control. It is implemented widely in industries such as automotive, mining, dredging, oil refining, pulp and paper manufacturing, chemical processing and power generating plants.
In the field of human factors and ergonomics, human reliability is the probability that a human performs a task to a sufficient standard. Reliability of humans can be affected by many factors such as age, physical health, mental state, attitude, emotions, personal propensity for certain mistakes, and cognitive biases.
Thomas B. Sheridan is American professor of mechanical engineering and Applied Psychology Emeritus at the Massachusetts Institute of Technology. He is a pioneer of robotics and remote control technology.
Robot software is the set of coded commands or instructions that tell a mechanical device and electronic system, known together as a robot, what tasks to perform. Robot software is used to perform autonomous tasks. Many software systems and frameworks have been proposed to make programming robots easier.
Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots. Robotics is related to the sciences of electronics, engineering, mechanics, and software. The word "robot" was introduced to the public by Czech writer Karel Čapek in his play R.U.R., published in 1920. The term "robotics" was coined by Isaac Asimov in his 1941 science fiction short-story "Liar!"
An industrial control system (ICS) is an electronic control system and associated instrumentation used for industrial process control. Control systems can range in size from a few modular panel-mounted controllers to large interconnected and interactive distributed control systems (DCSs) with many thousands of field connections. Control systems receive data from remote sensors measuring process variables (PVs), compare the collected data with desired setpoints (SPs), and derive command functions that are used to control a process through the final control elements (FCEs), such as control valves.
The following outline is provided as an overview of and topical guide to automation:
A hierarchical control system (HCS) is a form of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control system.
In recent years, the use of biologically inspired methods such as the evolutionary algorithm have been increasingly employed to solve and analyze complex computational problems. BELBIC is one such controller which is proposed by Caro Lucas, Danial Shahmirzadi and Nima Sheikholeslami and adopts the network model developed by Moren and Balkenius to mimic those parts of the brain which are known to produce emotion.
Real-time Control System (RCS) is a reference model architecture, suitable for many software-intensive, real-time computing control problem domains. It defines the types of functions needed in a real-time intelligent control system, and how these functions relate to each other.
The following outline is provided as an overview of and topical guide to robotics:
Adaptive collaborative control is the decision-making approach used in hybrid models consisting of finite-state machines with functional models as subcomponents to simulate behavior of systems formed through the partnerships of multiple agents for the execution of tasks and the development of work products. The term “collaborative control” originated from work developed in the late 1990s and early 2000 by Fong, Thorpe, and Baur (1999). It is important to note that according to Fong et al. in order for robots to function in collaborative control, they must be self-reliant, aware, and adaptive. In literature, the adjective “adaptive” is not always shown but is noted in the official sense as it is an important element of collaborative control. The adaptation of traditional applications of control theory in teleoperations sought initially to reduce the sovereignty of “humans as controllers/robots as tools” and had humans and robots working as peers, collaborating to perform tasks and to achieve common goals. Early implementations of adaptive collaborative control centered on vehicle teleoperation. Recent uses of adaptive collaborative control cover training, analysis, and engineering applications in teleoperations between humans and multiple robots, multiple robots collaborating among themselves, unmanned vehicle control, and fault tolerant controller design.
The following outline is provided as an overview of and topical guide to control engineering:
Automation bias is the propensity for humans to favor suggestions from automated decision-making systems and to ignore contradictory information made without automation, even if it is correct. Automation bias stems from the social psychology literature that found a bias in human-human interaction that showed that people assign more positive evaluations to decisions made by humans than to a neutral object. The same type of positivity bias has been found for human-automation interaction, where the automated decisions are rated more positively than neutral. This has become a growing problem for decision making as intensive care units, nuclear power plants, and aircraft cockpits have increasingly integrated computerized system monitors and decision aids to mostly factor out possible human error. Errors of automation bias tend to occur when decision-making is dependent on computers or other automated aids and the human is in an observatory role but able to make decisions. Examples of automation bias range from urgent matters like flying a plane on automatic pilot to such mundane matters as the use of spell-checking programs.
Semi-automation is a process or procedure that is performed by the combined activities of man and machine with both human and machine steps typically orchestrated by a centralized computer controller.
Cognitive systems engineering (CSE) is a field of study that examines the intersection of people, work, and technology, with a focus on safety-critical systems. The central tenet of cognitive systems engineering is that it views a collection of people and technology as a single unit that is capable of cognitive work, which is called a joint cognitive system.
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