Fixed Set Point Weighting 2DOF PID Controller for Control Processes

Abdelaty, Bahaaeldin Gamal

doi:10.11648/j.engmath.20180201.13

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…The commonly used PID controllers are relatively simple and have stable control, the parameters are relatively easy to be adjusted, and there is no static error [ 22 ]. If the control object is a system that changes continuously over time, the parameters of the PID controller are not easy to be automatically adjusted to meet the control requirements of the system, and the corresponding control effect is not very ideal [ 23 ]. Therefore, the traditional PID controller needs to be modified to improve its control performance.…”

Section: Methodsmentioning

confidence: 99%

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Sun

2020

PLoS ONE

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The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. The related principles and structure of hybrid electromagnetic and elastic foil gas bearings is introduced firstly. Then, the proportional, integral, and derivative (PID) bearing controller is introduced and improved into two controllers: IPD and CPID. The controllers and hybrid bearing system are controlled based on the RBF neural network based on deep learning. The characteristics of the hybrid bearing system are explored at the end of this study, and the control simulation research is developed based on the Simulink simulation platform. The effects of the PID, IPD, and CIPD controllers based on the RBF neural network are compared, and they are also compared based on the traditional particle swarm optimization (PSO). The results show that the thickness, spread angle, and rotation speed of the elastic foil have great impacts on the bearing system. The proposed CIPD bearing control method based on RBF neural network has the shortest response time and the best control effect. The controller parameter tuning optimization starts to converge after one generation, which is the fastest iteration. It proves that RBF neural network control based on deep learning has high feasibility in hybrid bearing system. Therefore, the results provide an important reference for the application of deep learning in rotating machinery.

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Section: Methodsmentioning

confidence: 99%

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Sun

2020

PLoS ONE

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“…The parallel critic 2DOF PID structure defined above has two inherent closed loop transfer functions available in its structure using respective proportional and derivative set-point limiters or weights b and c ∈ [0, 1] ∈ R + [1,3,10,[42][43][44]. The structure reduces to the common 1DOF error-feedback PID structure when the set-point weights are both unity.…”

Section: Critic Pid Controllermentioning

confidence: 99%

Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

Somefun¹,

Akingbade²,

Dahunsi³

2020

ujca

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One of the lowest level control tasks, especially in robotics and other manufacturing industries, upon which other high-level controls are dependent, is the speed control of a dc motor. Usually, tuning the parameters of the proportional integral derivative (PID) controller for this task employs established conventional methods that expose the knowledge of nominal process model parameters to the control algorithm. These methods have found widespread use. Notwithstanding, a promising line of inquiry is: to search alternate possibilities of a PID being designed to automatically achieve comparable good control performance without using a formal mathematical process model approximation of the actual physical system, such as dc motor plant, in the frequency or time domain. In this paper, we propose an intelligent PID design method, "optimal closed PID-loop model predictive control", that answers this question using the characteristic settling-time (including delay-time) property of dynamic processes. The performance of this proposed method is benchmarked with popular process-model based methods. Simulation results illustrate the promise and effectiveness of the proposed tuning method, in ensuring good closed-loop performance quality for the dc motor, without using formal process models.

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“…In terms of representation, a straightforward and more general computational model of the PID control algorithm has a 2DOF control structure [24]- [27]. This is the integer-valued number of closed-loop transfer functions that are present in the controller's structure and can be adjusted independently [28].…”

Section: Related Workmentioning

confidence: 99%

"Closed Proportional-Integral-Derivative-Loop Model" Following Control

Somefun,

Akingbade,

Dahunsi

2020

Preprint

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The proportional-integral-derivative (PID) control law is often overlooked as a computational imitation of the critic control in human decision. This paper provides a formulation to remedy this problem. Further, based on the characteristic settling-behaviour of dynamical systems, the "closed PID-loop model" following control (CPLMFC) method is introduced for automatic PID design. Also, a method for closed-loop settlingtime identification is provided. The CPLMFC algorithm and some recommended guidelines are given for setting the critic weights of the PID. Finally, two representative case-studies are simulated. Both the theoretical results and simulation results (via performance indices) illustrate that the CPLMFC can guarantee both accurate and stable closed-loop adaptive PID control performance in real-time.

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Fixed Set Point Weighting 2DOF PID Controller for Control Processes

Cited by 8 publications

References 19 publications

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning

Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

"Closed Proportional-Integral-Derivative-Loop Model" Following Control

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