Self-organizing fuzzy controller for injection molding machines

Lin, Jeen; Lian, Ruey-Jing

doi:10.1016/j.jprocont.2010.02.010

Cited by 24 publications

(11 citation statements)

References 24 publications

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“…[22][23][24][25][26][27][28][29] Significantly, a new type of learning algorithm was developed and widely applied in recent investigations, which used the output error and error change to adjust the SOFC's linguistic fuzzy rule table directly, so that it can be generated without any initial fuzzy rules and significantly eliminate the difficulty of designing the fuzzy rule base. [30][31][32][33][34][35][36][37] The general form of this proposed self-organizing fuzzy algorithm can be formulated as…”

Section: Self-organizing Fuzzy Algorithmmentioning

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: Self-organizing Fuzzy Algorithmmentioning

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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“…3, and consists of three steps: performance measure, model estimation, and rule modification [7]- [9], [22]- [24]. A generalized form for the self-organizing fuzzy algorithm has been proposed [7]- [9], [22]- [24] as…”

Section: A Self-organizing Fuzzy Algorithmmentioning

confidence: 99%

“…M is the direct forward system gain in the control system. It is generally set as 1 to eliminate the identification procedure of the system and to reduce the computational time required during implementation [7]- [9], [22]- [24]. γ is a learning rate and ς is a weighting distribution.…”

Section: A Self-organizing Fuzzy Algorithmmentioning

confidence: 99%

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Intelligent Controller for Robotic Motion Control

Lian

2011

IEEE Trans. Ind. Electron.

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A self-organizing fuzzy controller (SOFC) has been developed to control complicated and nonlinear systems. However, it is arduous to choose an appropriate learning rate and a suitable weighting distribution of the SOFC to achieve satisfactory performance for system control. Furthermore, the SOFC is mainly used to control single-input single-output systems. When the SOFC is applied to manipulating a robotic system, which is an example of multiple-input multiple-output systems, it is difficult to eliminate the dynamic coupling effects between the degrees of freedom (DOFs) of the robotic system. To address the problems, this study developed a self-organizing fuzzy radial basis-function neural-network (RBFN) controller (SFRBNC) for robotic systems. The SFRBNC uses an RBFN to regulate in real time these parameters of the SOFC to optimal values, thereby solving the problem faced when the SOFC is applied. The RBFN has coupling weighting regulation ability, so it can eliminate the dynamic coupling effects between the DOFs for robotic system control. From the experimental results of the 6-DOF robot tests, the SFRBNC demonstrated better control performance than the SOFC.Index Terms-Radial basis-function neural-network (RBFN), robotic systems, self-organizing fuzzy controller (SOFC).

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“…3 The development of further modeling and control strategies for the injection molding process can have a positive effect on the production rate and final product quality of injected molded parts. 4 The plastic injection molding process consists of preparing a plastic shot called melt that will be injected into a mold cavity. The melt has several properties affecting final part quality such as temperature and viscosity.…”

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confidence: 99%

Controlling Process Parameters during Plastication in Plastic Injection Molding Using Model Predictive Control

Dubay

Hernandez

et al. 2014

Adv Polym Technol

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This investigation presents a method for controlling two key parameters during plastication that can significantly influence melt viscosity on an injection molding machine (IMM). These parameters are screw rotational speed and hydraulic back pressure. Having the functionality to control these parameters at specific targets can potentially provide the mechanism to change the melt viscosity. Mathematical models were derived that described the interaction between these two parameters. These models were used to formulate a multivariable model predictive controller developed to control them. The controller was implemented on an industrial-scale IMM with good closed-loop performance. C

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Self-organizing fuzzy controller for injection molding machines

Cited by 24 publications

References 24 publications

Self-organizing fuzzy optimal control for under-actuated systems

Self-organizing fuzzy optimal control for under-actuated systems

Intelligent Controller for Robotic Motion Control

Controlling Process Parameters during Plastication in Plastic Injection Molding Using Model Predictive Control

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