PID controller tuning by particle swarm optimization on electrical discharge machining servo control system
12 Jun 2012-Vol. 1, pp 51-55
TL;DR: The main goal of this work is to get PID parameters through Particle Swarm Optimization (PSO) algorithm to ensure a stable, robust and controlled system.
Abstract: Electrical Discharge Machining (EDM) is included in a stochastic process. So maintaining gap between electrode and workpiece is not easy. In order to control the gap, a proportional integral derivative (PID) controller is designed for EDM servo actuator system. The main goal of this work is to get PID parameters through Particle Swarm Optimization (PSO) algorithm to ensure a stable, robust and controlled system. The controller and the model for EDM die sinking are verified by simulation of the control system using MATLAB and simulink program. Simulation results verify the effectiveness of the PID controller in which its parameter determined by PSO to control the electrode position towards workpiece.
Citations
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TL;DR: An adaptive fuzzy PID control for a class of multi-input multi-output disturbed nonlinear systems with unknown dynamics and unknown dead-zone inputs’ nonlinearities is proposed.
Abstract: This paper proposes an adaptive fuzzy PID control for a class of multi-input multi-output disturbed nonlinear systems with unknown dynamics and unknown dead-zone inputs’ nonlinearities. Within this scheme, a fuzzy system is used in order to approximate the PID gains instantaneously according to whole control system objective. Specifically, a gradient descent algorithm is used to update online the fuzzy system parameters such that the error between the fuzzy PID control and the unknown ideal control converges to an adjustable region. The control design and the stability analysis of the closed-loop system are given by the Lyapunov approach. The effectiveness of the proposed controller is highlighted by simulated examples .
15 citations
01 Apr 2020
TL;DR: In this article, a new integrated control system using Programmable System-on-Chip (PSoC) for die-sinking EDM in order to enhance Material Removal Rate (MRR).
Abstract: Electrical discharge machining (EDM) is one of the earliest non-conventional machining in order to manufacture very accurate 3-D complex components on any electrically conductive materials. In die-sinking EDM, a pulse discharge occurs in a small spark gap between electrically conductive workpiece and electrode in dielectric medium. This paper proposed a new integrated control system using Programmable System-on-Chip (PSoC) for Die-sinking EDM in order to enhance Material Removal Rate (MRR). The MRR result of EDM-PSoC system is higher than EDM-Ben Fleming system due to the effect off high speed processing data analysis using PID algorithm in PSoC microcontroller and leads to improving system efficiency 41%.
4 citations
01 Oct 2017
TL;DR: This work proposes an adaptive PID control law to deal with a class of single input single output (SISO) uncertain nonlinear systems and uses a fuzzy system for approximating the PID control gains.
Abstract: In this work we propose an adaptive PID control law to deal with a class of single input single output (SISO) uncertain nonlinear systems. In fact, a fuzzy system is used for approximating the PID control gains. The fuzzy system parameters are adjusted online using a robust adaptation law based on the Gradient method and augmented by the so-called “e-modification” term, in order to minimize the error between the fuzzy PID controller and the unknown ideal controller. The stability of the closed-loop system is proven analytically using the Lyapunov approach. A simulation example is presented to illustrate the performance of the proposed scheme.
3 citations
Cites methods from "PID controller tuning by particle s..."
...For this end, many methods has been proposed in literatures [2-12]; the empirical methods as Ziegler Nichols [2], analytical methods as root locus [2], and population-based optimization methods such as genetic algorithms and particle swarm optimization [36]....
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TL;DR: In this article, two discharge models are simulated and compared, and the simulation results show that there are behavior differences between these models, which is very important on the outcome of research.
Abstract: In recent years, simulation-based research is highly appreciated. So many researchers use this way to investigate and solve some problems. Empirical model or behavioral model is used to bring the problems into computer or other computing devices. Selection and determination of the model is very important on the outcome of research. In Electrical Discharge Machining system, servo control system uses voltage drop between electrode and workpiece as a feedback signal. Discharge phenomenon convert gap or distance between electrode and workpiece to voltage. In this paper, two discharge models are simulated and compared. Simulation results show that there are behavior’s differences between these models.
2 citations
Dissertation•
01 May 2015
TL;DR: The proposed Integrated Control Mechanism (ICM) to improve the MRR of the EDM system is implemented and the result shows that MRR is higher when compared to the normal machining process.
Abstract: A servo control system in Electrical Discharge Machining (EDM) system is a control system with an appropriate control algorithm to position electrode on a particular distance from workpiece during machining process. The gap between the electrode and the workpiece is in the range of 10 – 50 μm. This ideal gap is achieved by applying an appropriate control algorithm to the servo control system of the EDM, and maintaining this gap will improve the Material Removal Rate (MRR) during the machining process. A considerable number of unique methods were proposed in the control algorithm in order to bring the electrode to the optimum position. This research proposes a new method called Integrated Control Mechanism (ICM) to improve the MRR of the EDM system. A rotary encoder is used as an additional mechanical sensor for the feedback control system in order to limit the electrode movement. Modelling of EDM is further investigated to predict the MRR parameter and optimization of electrode control position. A Neural Network system is used to predict MRR where Particle Swarm Optimization (PSO) and Differential Evolution (DE) are studied and simulated to optimize the Proportional Integral Derivative (PID) control parameters for the EDM system. Research conducted shows that the proposed Feed Forward Artificial Neural Network improves the accuracy of prediction in determining MRR by 2.92% and PID parameter optimization is successfully applied either using PSO or DE. The ICM is successfully implemented and the result shows that MRR is higher when compared to the normal machining process.
1 citations
References
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Book•
01 Jan 1995
4,110 citations
Additional excerpts
...In the past decades, various tuning methodologies of PI and PID controllers have been proposed in literatures such as autotuning, self-tuning and computational intelligence[1, 8, 9]....
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TL;DR: The state of the art of PID control is presented and its future is reflected on, including specifications, stability, design, applications, and performance.
1,167 citations
TL;DR: In this article, the authors compared a Dimensional Analysis (DA) model, an Artificial Neural Network (ANN) model and an experimental result for a low gap current of an Electrical Discharge Machining (EDM) process.
Abstract: This paper aims to compare the material removal rate, ν between a Dimensional Analysis (DA) model, an Artificial Neural Network (ANN) model and an experimental result for a low gap current of an Electrical Discharge Machining (EDM) process. The data analysis is based on a copper electrode and steel workpiece materials. The DA and ANN model that have been developed and reported earlier by authors are used to compare the material removal of EDM process. The result indicated that the ANN model provides better accuracy towards the experimental results.
612 citations
Additional excerpts
...Even been proven that Artificial neural networks have advantages compared with the Dimensional Analysis[6]....
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07 Aug 2002
TL;DR: A brief summary of PID theory is given, then some of the most-used PID tuning methods are discussed and some the more recent promising techniques are explored.
Abstract: PID control is a control strategy that has been successfully used over many years. Simplicity, robustness, a wide range of applicability and near-optimal performance are some of the reasons that have made PID control so popular in the academic and industry sectors. Recently, it has been noticed that PID controllers are often poorly tuned and some efforts have been made to systematically resolve this matter. In this paper, a brief summary of PID theory is given, then some of the most-used PID tuning methods are discussed and some of the more recent promising techniques are explored.
515 citations
Additional excerpts
...In the past decades, various tuning methodologies of PI and PID controllers have been proposed in literatures such as autotuning, self-tuning and computational intelligence[1, 8, 9]....
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TL;DR: A new method employing two genetic algorithms (GA) is developed for solving the constraint optimization problem of an optimal disturbance rejection PID controller as a constrained optimization problem.
Abstract: This paper presents a method to design an optimal disturbance rejection PID controller. First, a condition for disturbance rejection of a control system-H/sub /spl infin//-norm-is described. Second, the design is formulated as a constrained optimization problem. It consists of minimizing a performance index, i.e., the integral of the time weighted squared error subject to the disturbance rejection constraint. A new method employing two genetic algorithms (GA) is developed for solving the constraint optimization problem. The method is tested by a design example of a PID controller for a servomotor system. Simulation results are presented to demonstrate the performance and validity of the method.
434 citations
"PID controller tuning by particle s..." refers background in this paper
...Over the years, several authors have proposed the tuning of PID to control variable processes by optimization methods, such as genetic algorithms[10-16], particle swarm optimization[17], tribes algorithm[18], harmony search[19, 20], evolution strategy [21] and ant colony [22]....
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