A self-tuning fuzzy PID controller design using gamma aggregation operator

In this paper, a novel tuning approach for aggregation operator of a fuzzy PID controller has been proposed. The gamma operator which has a free parameter (gamma) is used for the aggregation purpose. The change of the gamma parameter between 0 and 1 generates the aggregation operator hybrid output of the AND and OR operators. In this manner, firstly, the effect of the gamma parameter change on the control surface of the fuzzy PID controller and the closed loop system response are studied. Secondly, a gamma tuning mechanism is built by a fuzzy logic decision mechanism using the observations from the first various simulation studies. The aim of the gamma tuning mechanism is to change the gamma parameter in an online manner to obtain a fast response with no or less overshoot. The benefit of the proposed approach over the conventional aggregation operator is shown on a non-linear system via simulations. Simulations are performed in Matlab, and the performance of the proposed approach is studied for a sequence of different set-points in order to observe the set-point following performances. Moreover, the disturbance rejection performance is studied. The results of the simulations show the advantage of the proposed new self-tuning structure over the conventional structures.

[1]  T. Kumbasar,et al.  Inverse fuzzy Model Control with online adaptation via Big Bang-Big Crunch optimization , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[2]  Engin Yesil,et al.  Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration , 2013, Eng. Appl. Artif. Intell..

[3]  Ngai Ming Kwok,et al.  Automatic Fuzzy Membership Function Tuning Using the Particle Swarm Optimization , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[4]  Rajani K. Mudi,et al.  A robust self-tuning scheme for PI- and PD-type fuzzy controllers , 1999, IEEE Trans. Fuzzy Syst..

[5]  Fernando Tadeo,et al.  Fuzzy control of a neutralization process , 2006, Eng. Appl. Artif. Intell..

[6]  Byung Soo Moon Equivalence between fuzzy logic controllers and PI controllers for single input systems , 1995 .

[7]  Shiuh-Jer Huang,et al.  Metal chamber temperature control by using fuzzy PID gain auto-tuning strategy , 2009 .

[8]  George K. I. Mann,et al.  New methodology for analytical and optimal design of fuzzy PID controllers , 1999, IEEE Trans. Fuzzy Syst..

[9]  A. K. Pal,et al.  A PD-type self-tuning FLC for second-order systems with dead-time , 2012, 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).

[10]  Y. E. Hawas A supervisory control system for ATIS/ATMS integration. Part 2: neuro-fuzzy calibration and analyses , 2002 .

[11]  Devendra K. Chaturvedi,et al.  Soft Computing - Techniques and its Applications in Electrical Engineering , 2008, Studies in Computational Intelligence.

[12]  Josef Benedikt,et al.  A GIS APPLICATION TO ENHANCE CELL-BASED INFORMATION MODELING , 2000 .

[13]  H. Zimmermann,et al.  Latent connectives in human decision making , 1980 .

[14]  W. Marsden I and J , 2012 .

[15]  M. Mizumoto Pictorial representations of fuzzy connectives, Part II: cases of compensatory operators and self-dual operators , 1989 .

[16]  Peter E.D. Love,et al.  A computational intelligent fuzzy model approach for excavator cycle time simulation , 2003 .

[17]  Karim Salahshoor,et al.  A novel real-time fuzzy adaptive auto-tuning scheme for cascade PID controllers , 2011 .

[18]  Nordin Saad,et al.  A PLC-based modified-fuzzy controller for PWM-driven induction motor drive with constant V/Hz ratio control , 2012 .

[19]  Saro Lee Application and verification of fuzzy algebraic operators to landslide susceptibility mapping , 2007 .

[20]  Xiao-Gang Duan,et al.  Effective Tuning Method for Fuzzy PID with Internal Model Control , 2008 .

[21]  Alain Segundo Potts,et al.  Fuzzy auto-tuning for a PID controller , 2010, 2010 9th IEEE/IAS International Conference on Industry Applications - INDUSCON 2010.

[22]  Han-Xiong Li,et al.  Conventional fuzzy control and its enhancement , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[23]  Engin Yesil,et al.  Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm , 2011, Expert Syst. Appl..

[24]  Ali Asghar Alesheikh,et al.  Land assessment for flood spreading site selection using geospatial information system , 2008 .

[25]  Masaharu Mizumoto,et al.  PID type fuzzy controller and parameters adaptive method , 1996, Fuzzy Sets Syst..

[26]  Huiwen Deng,et al.  An adaptive fuzzy logic controller with self-tuning scaling factors based on neural networks , 2005, Proceedings Autonomous Decentralized Systems, 2005. ISADS 2005..

[27]  Andreas Meier,et al.  Master's Thesis: Application of Fuzzy Classification to a Data Warehouse in E-Health , 2006 .

[28]  Xiuzhen Ma,et al.  The Software of Auto-Tuning Parameters of PID Controller Based on Fuzzy Genetic Algorithm , 2010, 2010 International Conference on Computational Intelligence and Software Engineering.

[29]  H. Zimmermann,et al.  Advanced fuzzy logic control of a model car in extreme situations , 1992 .

[30]  Onur Karasakal,et al.  Implementation of a New Self-Tuning Fuzzy PID Controller on PLC , 2005 .

[31]  Devendra K. Chaturvedi,et al.  Applications of Fuzzy Rule Based System , 2008 .

[32]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[33]  Sylvie Galichet,et al.  Fuzzy controllers: synthesis and equivalences , 1995, IEEE Trans. Fuzzy Syst..

[34]  Y. E. Hawas A SUPERVISORY CONTROL SYSTEM FOR ATIS/ATMS INTEGRATION.. , 2002 .

[35]  Kuan-Yu Chen,et al.  A self-tuning fuzzy PID-type controller design for unbalance compensation in an active magnetic bearing , 2009, Expert Syst. Appl..

[36]  Y. E. Hawas A supervisory control system for ATIS/ATMS integration. Part 2: neuro-fuzzy calibration and analyses , 2002 .

[37]  Werner von Seelen,et al.  Evaluating flexible fuzzy controllers via evolution strategies , 1999, Fuzzy Sets Syst..

[38]  T. Fukuda,et al.  Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm , 1995 .

[39]  Jin-Jye Lin,et al.  A fuzzy PID controller being like parameter varying PID , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[40]  Francisco Herrera,et al.  Genetic adaption of rule connectives and conjunction operators in fuzzy rule based systems: an experimental comparative study , 2003, EUSFLAT Conf..

[41]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[42]  Neil Genzlinger A. and Q , 2006 .

[43]  Seema Chopra,et al.  Auto Tuning of Fuzzy PI Type Controller Using Fuzzy Logic , 2008 .

[44]  Müjde Güzelkaya,et al.  EVALUATION OF THE PERFORMANCE OF VARIOUS FUZZY PID CONTROLLER STRUCTURES ON BENCHMARK SYSTEMS , 2005 .

[45]  M. Mizumoto Pictorial representations of fuzzy connectives, part I: cases of t-norms, t-conorms and averaging operators , 1989 .

[46]  Hung-Yuan Chung,et al.  A PID type fuzzy controller with self-tuning scaling factors , 2000, Fuzzy Sets Syst..