Self-Organizing Fuzzy Controller for Gas-Assisted Injection Molding Combination Systems

Lin, Jeen; Lian, Ruey-Jing

doi:10.1109/tcst.2009.2037973

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Specially, the development of SOFCs involves no system model, which significantly simplifies the controller design process; therefore, SOFCs have been employed extensively in various applications with complicated dynamics. [16][17][18][19][20] As the authors are concerned, the SOFC has advantageous learning and reasoning abilities in real time for manipulating complicated and nonlinear systems, which is a desirable property for under-actuated system…”

Section: Introductionmentioning

confidence: 99%

Self-organizing fuzzy optimal control for under-actuated systems

Zhang

Wang

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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This study combines self-organizing fuzzy logic control technology with optimal control theory and presents a selforganizing fuzzy optimal controller for under-actuated systems. Instead of calibrating the control input directly, the self-organizing fuzzy optimal controller employs the self-organizing fuzzy system as a superior regulator to adjust the weighting matrix of the cost function for the optimal controller. Through this operation and the hierarchical control architecture design, self-organizing fuzzy optimal controller adaptively regulates the control strategy and manipulates the optimal control procedure according to the system dynamic behavior in real time. The optimal control law corresponding to the proposed regulator is then derived from the maximum principle for optimal control theory. The stability of the selforganizing fuzzy optimal controller is analyzed and proved through the Lyapunov stability theorem. Simulations on an under-actuated gantry crane system and a ball-and-beam system have verified that the self-organizing fuzzy optimal controller provides superior control performances as compared with the traditional linear quadratic regulating controller.

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Section: Introductionmentioning

confidence: 99%

Self-organizing fuzzy optimal control for under-actuated systems

Zhang

Wang

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…As it can be seen in technical literature, researchers try to use mathematical approximations such as the neural network model [12,17,42,43], Taguchi method [20,44-^6], genetic algorithm [19, 43,47], support vector regression [48], statistical analysis [13], and the Fuzzy algorithm [49,50] with their experimental or numerical studies in order to reduce the processing time and computational expenses. However, some of these mathematical approximations are still time-consuming and some of them are classified as one-step (no iteration) optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Powder Injection Molding Process Parameters Using the Sequential Simplex Algorithm and Sensitivity Analysis

Panahi¹,

Mianajiy²,

Miandoabchi³

et al. 2013

Journal of Manufacturing Science and Engineering

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In this work, the minimization of warpage was investigated using the "MOLDFLOW" software and sequential simplex algorithm based on feedstock properties. Also, the sensitivity analysis was implemented to determine the degree of impact of each parameter on the warpage. This study is divided into two portions: experimental analysis and numerical analysis. First, for the experimental study, four kinds of feedstock with different alumina powder loadings were prepared to investigate the rheological properties. This investigation showed that the feedstock with 60 vol. % alumina powder was the optimum feedstock for the injection molding. Also, the results indicated that the viscosity of feedstock decreases by increasing both the shear rate and temperature. Next, the thermal conductivity of this feedstock was measured at different temperatures and it was found that the change of temperature can greatly influence the thermal conductivity of feedstock. In the numerical study, the injection molding parameters were divided into three categories. Based on the feedstock properties obtained form the first portion, and in order to minimize the warpage, the values of these parameters were sequentially acquired by MOLD-FLOW and used in the sequential simplex algorithm for gradual convergence to the optimum level. To show the accuracy of numerical results, several samples were injection molded using the injection molding conditions for each vertex; results showed a close correlation between the values obtained by the numerical simulation and by the actual case. After determining the optimum parameter values, the sensitivity analysis was performed to identify the level of influence of each parameter on warpage. The obtained results showed that the most effective parameters on warpage are the mold temperature, packing pressure, and the holding time. Generally, it is demonstrated that the experimental and numerical analysis, performed via the MOLDFLOW software and sequential simplex algorithm, together with the sensitivity analysis can be useful in achieving success in the powder injection molding (PIM) technique.

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A modified self-tuning fuzzy logic temperature controller for metal induction heating

Chang

Chiang

Tai

2020

Review of Scientific Instruments

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This paper presents a method to build a dynamic target curve producer corresponding to the rising time setting and the ultimate target temperature as a reference for a fuzzy logic controller that is used in the metal heating process application. To achieve this goal, there are some quantization factors in a fuzzy controller that must be set according to the system situation, as well as the experience of experts that will cause the controller to have a lack of adaptivity. To solve this problem, in this paper, all the quantization factors are analyzed thoroughly, and a self-tuning module is designed to make it possible for the controller to perform real-time adjustments based on the system situation and, eventually, make it more adaptive. During the design process, a simulation comparing the control capabilities of the conventional fuzzy logic controller and the self-tuning fuzzy logic controller (STFLC) is made using a finite element analysis. Finally, experiments are carried out on the induction heating system to verify the effect of the proposed STFLC. The results show that, with the proposed self-tuning module, the control capability and adaptivity of the controller were drastically improved.

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Self-Organizing Fuzzy Controller for Gas-Assisted Injection Molding Combination Systems

Cited by 7 publications

References 20 publications

Self-organizing fuzzy optimal control for under-actuated systems

Self-organizing fuzzy optimal control for under-actuated systems

Optimization of the Powder Injection Molding Process Parameters Using the Sequential Simplex Algorithm and Sensitivity Analysis

A modified self-tuning fuzzy logic temperature controller for metal induction heating

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