Optimum Settings for Automatic Controllers

Ziegler, Jens; Nichols, Nancy

doi:10.1115/1.4019264

Cited by 2,004 publications

(100 citation statements)

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“…The parameter values of the PI controller are obtained when the objective function has the smallest value. These values of PI parameters are shown in Table 2 and conventional controller method which is Ziegler‐Nichols method 52 also shown in Table 2.…”

Section: Controller Methods Design and Implementationmentioning

confidence: 99%

PI control based on parameter space approach supported metaheuristic optimization algorithms and ANFIS in a natural gas combined cycle power plant

Bayhan

Tiryaki

2021

Optim Control Appl Methods

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In this article, proportional‐integral (PI) control to ensure stable operation of a steam turbine in a natural gas combined cycle power plant is investigated, since active power control is very important due to the constantly changing power flow differences between supply and demand in power systems. For this purpose, an approach combining stability and optimization in PI control of a steam turbine in a natural gas combined cycle power plant is proposed. First, the regions of the PI controller, which will stabilize this power plant system in closed loop, are obtained by parameter space approach method. In the next step of this article, it is aimed to find the best parameter values of the PI controller, which stabilizes the system in the parameter space, with artificial intelligence‐based control and metaheuristic optimization. Through parameter space approach, the proposed optimization algorithms limit the search space to a stable region. The controller parameters are examined with Particle Swarm Optimization based PI, artificial bee colony based PI, genetic algorithm based PI, gray wolf optimization based PI, equilibrium optimization based PI, atom search optimization based PI, coronavirus herd immunity optimization based PI, and adaptive neuro‐fuzzy inference system based PI (ANFIS‐PI) algorithms. The optimized PI controller parameters are applied to the system model, and the transient responses performances of the system output signals are compared. Comparison results of all these methods based on parameter space approach that guarantee stability for this power plant system are presented. According to the results, ANFIS‐ PI controller is better than other methods.

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Section: Controller Methods Design and Implementationmentioning

confidence: 99%

PI control based on parameter space approach supported metaheuristic optimization algorithms and ANFIS in a natural gas combined cycle power plant

Bayhan

Tiryaki

2021

Optim Control Appl Methods

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“…This study does not include or use yaw control or movements. In tuning the PD Controller, the Ziegler-Nichols method introduced by Nichols et al in [15] were used.…”

Section: Pd Tuningmentioning

confidence: 99%

“…The Proportional controller, Ku, must then be set to a value increasing in value until a certain oscillation, termed as Tu, is obtained from the system. Ku and Tu will then be used to determine the correct values of the PID Controller [15].…”

Section: Pd Tuningmentioning

confidence: 99%

“…The average period of oscillation, Tu, is computed to be 8.4 seconds. According to [15], the Kp and Td value for the PD Controller can be obtained using these testing data. Figure 18 shows the oscillations of the approximated distance of the bounding box for tuning the pitch movement.…”

Section: Figure 17 X Coordinate Oscillationmentioning

confidence: 99%

“…The average period of oscillation, Tu, is computed to be 17.19 seconds. According to [15], the Kp and Td value for the PD Controller can be obtained using these…”

Section: Figure 17 X Coordinate Oscillationmentioning

confidence: 99%

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Chua

et al. 2021

Int. J. Autom. Smart

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This paper presents a novel implementation of an autonomous human following done using local context algorithm. The vision system consists of a drone equipped with a companion computer and camera. Through the vision system, drones can behave according to its desired application. In this study, the Pixhawk 2 Cube flight controller of the drone is connected to Odroid XU4, the companion computer. The OpenMV camera is connected to the Odroid XU4 and streams image bytes whereas the Odroid XU4 performs image processing to detect the human through local context. The Odroid XU4 controls the drone by sending commands to the flight controller based on detected objects. Drone following is implemented by detecting humans from the image stream provided by OpenMV and maintaining the detected human on the center of the image and within a specified distance through drone movements. The results show the single movements right, left, forward, and backward yielded low steady-state errors with settling time of about 3 to 4 seconds.

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Continuous‐time Linear Control

Kratz¹

2009

Control Methods for Electrical Machines

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Optimum Settings for Automatic Controllers

Cited by 2,004 publications

References 0 publications

PI control based on parameter space approach supported metaheuristic optimization algorithms and ANFIS in a natural gas combined cycle power plant

PI control based on parameter space approach supported metaheuristic optimization algorithms and ANFIS in a natural gas combined cycle power plant

A Novel Implementation of an Autonomous Human Following Drone using Local Context

Continuous‐time Linear Control

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