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Automation in Manufacturing
Published in Edward Y. Uechi, Business Automation and Its Effect on the Labor Force, 2023
As early as the 1930s, industrial control systems were used to manage operations of power plants, oil refineries, chemical processing plants, and electricity transmission stations. Various names went by SCADA (Supervisory Control and Data Acquisition), DCS (Distributed Control System), and PCS (Process Control System). The industrial control system would have either a closed-loop feedback control or an open-loop feedback control. In an open-loop feedback control, the system would indicate a change in operation, and the human operator would react appropriately to the change as indicated. For example, a human operator would see an error light turn on and would then press a switch or turn a valve. In a closed-loop feedback control, a sensor detects a change and sends the change to a controller. The controller in turn makes an adjustment according to a computer software program written to handle the detected change. A human operator is not involved in the closed-loop feedback control. Earlier analog systems used relays, valves, and pneumatic gauges. Later digital systems used a computer and electronics.
Introduction to MATLAB and Simulink
Published in Cheng Siong Chin, Computer-Aided Control Systems Design, 2017
After completing the open-loop simulation using MATLAB and Simulink, a closed-loop control system can be designed. In this section, PID parameters tuning using the Ziegler–Nichols method is used. A closed-loop control system is one in which the output signal has a direct effect upon the control action, that is, closed-loop control systems are feedback control systems. The system error signal, which is the difference between the input signal and the feedback signal, is fed back to the controller so as to reduce the output error and thus bring the output of the system to the desired value. In other words, the term closed-loop implies the use of feedback action in order to reduce the system error. Figure 2.42 shows the block diagram of the closed-loop control system.
Short-Term Electricity Price Forecasting
Published in João P. S. Catalão, Electric Power Systems, 2017
In engineering applications, closed-loop control systems, based on the feedback from the output, can change their inputs to modify their outputs, a characteristic that is not seen in the open-loop control systems. Similarly, the closed-loop prediction strategy takes the feedback from the output. If the prediction generated for price spike occurrence and the forecast produced for the value of price spike/normal price are inconsistent, the inputs of the price spike occurrence predictor are changed accordingly on the basis of the price value prediction by the activated estimator. In this way, the price spike occurrence predictor can modify its output on the basis of the closed-loop operation. This cycle is continued until consistently more accurate forecasts for price spike occurrence, price spike value, and normal price value are obtained. More details about the closed-loop price spike prediction strategy can be found in Reference [54].
An inverse dynamics based fuzzy adaptive state-feedback controller for a nonlinear 3DOF manipulator
Published in International Journal of Modelling and Simulation, 2023
M. J. Mahmoodabadi, N. Nejadkourki
Tuning a closed loop system is known as the adjustment of its control parameters to the best values for the desired control response. One of the most common methods to improve the performance of a designed controller is the use of adaptation laws for effectively tuning the system gains. In the following, some of the researches related to this idea are listed as follows: adaptive fuzzy proportional-derivative control with stable tracking guarantee [10], adaptive proportional-derivative power-level control for pressurized water reactors [11], nonlinear adaptive depth-control of a tethered autonomous underwater vehicle [12], composite anti-disturbance model reference adaptive control for switched systems [13] and manipulation robots’ trajectory motion adaptive control [14].
Machine learning based predictive modeling and control of surface roughness generation while machining micro boron carbide and carbon nanotube particle reinforced Al-Mg matrix composites
Published in Particulate Science and Technology, 2022
Ravi Sekhar, T. P. Singh, Pritesh Shah
In the past, researchers have extensively investigated various analytical and statistical approaches to model various aspects of micro composite machining (Pramanik, Zhang, and Arsecularatne 2006, 2008; Sikder and Kishawy 2012). However, most of these models cannot be implemented in a closed loop industrial control system for automating the composite machining process. Industrial process automation typically requires parametric models that can be implemented in a suitable controller architecture for closed loop control. Closed loop control is a real time sensor feedback driven system wherein the controller automatically manipulates input parameters to maintain the process output at the desired set point. This real time process control is critically dependent upon the accuracy of the parametric model defining the relationship between the input and the output parameters. Accurate parametric models can be derived effectively by the implementation of system identification methodology. System identification employs actual input/output data sets to ‘identify’ models that describe the real system behavior based on existing parametric structures. System identification based parametric modeling has been successfully applied across various domains such as 3 D printing, structural damage prediction, robotics, fuel cells, HIV drug resistance, bio-diesel engines, bolted joint systems and many more (Silva, Machado, and Barbosa 2006; Giurgiutiu 2010; Shahiri et al. 2015; Pinto and Carvalho 2015; Pandit, Sekhar, and Shah 2019; Shah, Sekhar, and Singh 2021; Shah and Sekhar 2021).
A Framework Utilizing Augmented Reality to Enhance the Teaching–Learning Experience of Linear Control Systems
Published in IETE Journal of Research, 2021
Deepti Prit Kaur, Archana Mantri, Ben Horan
Control System is defined as the interconnection of interacting components which form a system configuration to provide a desired system response. Such systems are used to achieve increased productivity and improved performance of a device or system. The basis for analysis of a control system is governed by linear system theory, which assumes the cause and effect relationship for the components of a system [31]. A control system that satisfies the requirement of linearity (if system output is linear with respect to input and follows the rule of superposition) and time invariance (if the relation between system input and output is independent of the passage of time) is termed as LTI Control System. An LTI system can be open loop or closed loop based on its classification. While an open loop system is a simple yet less accurate system, a closed loop is complex but self-correcting and more accurate control system. Despite having many advantages over open loop system, a closed loop system tends to become unstable due to the presence of feedback which was used for the purpose of reducing the error between reference input and desired output of the system.