Fuzzy Controller for Laboratory Levitation System: Real-time Experiments Using Programmable Logic Controller

Development of a Fuzzy Proportional Integral Derivative (FPID) controller for a laboratory magnetic levitation process is described. The process is unstable and nonlinear, it is impossible to use a classical PID controller which works correctly. The process is very fast: the sampling period is 1 ms. The FPID controller is implemented using the R04 (the iQ-R family) Programmable Logic Controller (PLC) produced by Mitsubishi Electric.

[1]  B. Kaplan,et al.  Dynamic stabilization of tuned-circuit levitators , 1976 .

[2]  M. Eissa,et al.  Active vibration control of a nonlinear magnetic levitation system via Nonlinear Saturation Controller (NSC) , 2014 .

[3]  Keum-Shik Hong,et al.  Fuzzy sliding mode control of container cranes , 2015 .

[4]  Edwin Lughofer,et al.  Modeling and control with neural networks for a magnetic levitation system , 2017, Neurocomputing.

[5]  Erik D. Goodman,et al.  Greenhouse climate fuzzy adaptive control considering energy saving , 2017 .

[6]  Andrzej Turnau,et al.  Time-Optimal Control Supported by PD in Real-Time* * , 2012 .

[7]  Knut Graichen,et al.  Nonlinear model predictive control of a magnetic levitation system , 2013 .

[8]  Stefan Preitl,et al.  Points of View on Magnetic Levitation System Laboratory-Based Control Education , 2012 .

[9]  Ho Jae Lee,et al.  Robust sampled-data fuzzy control of nonlinear systems with parametric uncertainties: Its application to depth control of autonomous underwater vehicles , 2012, International Journal of Control, Automation and Systems.

[10]  Jun Wu,et al.  A modeling and control approach to magnetic levitation system based on state-dependent ARX model , 2014 .

[11]  Bidyadhar Subudhi,et al.  Nonlinear Control of a Magnetic Levitation System Using a New Input-Output Feedback Linearization , 2016 .

[12]  Xiao Wu,et al.  Hierarchical optimization of boiler–turbine unit using fuzzy stable model predictive control , 2014 .

[13]  Devinder Thapa,et al.  Auto-generation of IEC standard PLC code using t-MPSG , 2009 .

[14]  Andrzej Turnau,et al.  Neural adapted controller learned on-line in real-time , 2009 .

[15]  Yongchun Fang,et al.  Disturbance Rejection for a Magnetic Levitation System , 2006, IEEE/ASME Transactions on Mechatronics.

[16]  Maciej Lawrynczuk.  Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach , 2014 .

[17]  Mohammad Shahrokhi,et al.  Adaptive nonlinear control of pH neutralization processes using fuzzy approximators , 2009 .

[18]  Pietro Valdastri,et al.  A magnetic levitation robotic camera for minimally invasive surgery: Useful for NOTES? , 2017, Surgical Endoscopy.

[19]  Zi-Jiang Yang,et al.  Robust position control of a magnetic levitation system via dynamic surface control technique , 2004, IEEE Transactions on Industrial Electronics.

[20]  M. Ono,et al.  Japan's superconducting Maglev train , 2002 .

[21]  Huzefa Shakir,et al.  Time-domain fixed-structure closed-loop model identification of an unstable multivariable maglev nanopositioning system , 2011 .

[22]  Maciej Ławryńczuk,et al.  Computationally Efficient Model Predictive Control Algorithms , 2014 .

[23]  Xiaoou Li,et al.  Two-stage neural sliding-mode control of magnetic levitation in minimal invasive surgery , 2011, Neural Computing and Applications.

[24]  Wei Zheng,et al.  Stability Analysis and Dynamic Output Feedback Control for Nonlinear T-S Fuzzy System with Multiple Subsystems and Normalized Membership Functions , 2018 .