Self-tuning PID controllers in pursuit of plug and play capacity

Mendes, Jérôme; Osório, Luís; Araújo, Rui

doi:10.1016/j.conengprac.2017.09.006

Cited by 20 publications

(10 citation statements)

References 16 publications

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“…Kim [17] designed a PID controller with an analyser of gain/phase margin and immune algorithm. Mendes et al [18] discussed several self-tuning controllers (STCs) with PID form based on explicit and implicit identification algorithms. Wai and Lee [19] designed an adaptive fuzzy-neural-network control (AFNNC) scheme to overcome the highly nonlinear and unstable characteristics of a control object.…”

Section: Literature Reviewmentioning

confidence: 99%

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

Chu

Dong

et al. 2020

Mathematical Problems in Engineering

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Automatic train operation (ATO) system is one of the important components in advanced train operation control systems. Ideal controllers are expected for the automatic driving function of ATO systems. Aiming at the intelligence requirements of the systems, an NSGA-II-based parameter tuning method for the fuzzy immune PID (FI-PID) controller and a grey model GM(1,1)-based fuzzy grey immune PID (FGI-PID) controller were proposed. Taking a maglev train’s model as the control object and a velocity-time curve as the input, the feasibility of the parameter tuning method for the FI-PID controller and the applicability of the FI-PID controller and the FGI-PID controller for the ATO system were tested. The results showed that the optimized parameters were ideal, the two controllers all showed good performance on the indicators of traceability and comfort level, and the FGI-PID controller performed better than the FI-PID controller. The results exhibited the effectiveness of the proposed methods.

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Section: Literature Reviewmentioning

confidence: 99%

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

Chu

Dong

et al. 2020

Mathematical Problems in Engineering

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“…Jerome Mendes et al [26] have addressed the controlling issues that are unknown to industrial utilizations and time varying plants. To control unknown and time-varying plants the plug-and-play method was followed with used one algorithm named as control algorithm that automatically modify the control parameters.…”

Section: Recent Related Work: An Overviewmentioning

confidence: 99%

“…The elite must infect at each level the ant lion (random movement). So that, each ant thought to be related along ant lion through elite and roulette wheel, provided through the equation (26)…”

Section: Figure3 Flow Diagram Of the Proposed Techniquementioning

confidence: 99%

An Effective Technique for Tuning the Time Delay System with PID Controller-Ant Lion Optimizer Algorithm with ANN Technique

George

Ganesan

2020

IJEAT

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Nowadays, the PID controller is very common controller as well as very important controller in industrial utilizations. In the paper, proposed an ALO algorithm and ANN controller is utilized to enhance PID controller performance and control the tuning of TDS. TDS stands for Time delay system. ALO stands for Ant lion optimizer and ANN stands for Artificial neural network. In terms of parameters controlling, the time delay system is controlled and for different delay events low overshoot and fast time settling is reached. The novelty of the presented method is enhancing the PID controller performance by optimizing the PID gain parameters and controlling the highorder TDS. The performance of time delay system can be enhanced through decreasing error, tracking, time delay & error, rapid and exactly for their corresponding reference values. For parameter controlling of time delay system along optimal values, can be significantly enhanced the performance. To analyze the characteristics of the presented method, the various time delay systems are analyzed. The input and gain parameters were utilized to evaluate the objective function from tuning system. Based on proposed method, the optimal result is achieved and evaluated the increae time, settling time, overshoot as well as steady state error in TDS. The suggested controller is executed in MATLAB/Simulink work site and the presented technique performance examined through performance indexes and time domain specifications are evaluated using presented method compared to previous methods like ABC (Artificial Bee colony) algorithm, GSA (Gravitational Search Algorithm) ,FA (Firefly Algorithm).

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“…The controller structures or parameters can be determined based on the discrete-time transfer function of the plant in reference to (2) and the block diagram above. When taking P = Q = R = 1, the unique solution of the remaining controller parameters can be obtained [28].…”

Section: Fig 2 -Direct Gmvc Block Diagrammentioning

confidence: 99%

Direct and indirect self-tuning generalized minimum variance control

Jasim¹,

Jasim²,

Abase³

et al. 2019

Serb J Electr Eng

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Theoretically, several self-tuning control (STC) algorithms have been developed and many simulation results have proved their feasibility in the past years, but applications of STC are hardly seen. This paper proposes direct and indirect STC plans that can be applied to a chemical process system with a time delay auto-regressive and exogenous model of a varied time constant. The plan controller is a combination of a generalized minimum variance control (GMVC) strategy and an identification algorithm (simplified parameter) called recursive least squares to estimate controller parameters for the direct method and plan parameters for the indirect method. The experimental results show that GMVC is able to track the desired input or set point.

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Self-tuning PID controllers in pursuit of plug and play capacity

Cited by 20 publications

References 16 publications

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

An Effective Technique for Tuning the Time Delay System with PID Controller-Ant Lion Optimizer Algorithm with ANN Technique

Direct and indirect self-tuning generalized minimum variance control

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