Probabilistic fuzzy logic controller for uncertain nonlinear systems

Abstract This paper proposes a probabilistic fuzzy proportional - integral (PFPI) controller for controlling uncertain nonlinear systems. Firstly, the probabilistic fuzzy logic system (PFLS) improves the capability of the ordinary fuzzy logic system (FLS) to overcome various uncertainties in the controlled dynamical systems by integrating the probability method into the fuzzy logic system. Moreover, the input/output relationship for the proposed PFPI controller is derived. The resulting structure is equivalent to nonlinear PI controller and the equivalent gains for the proposed PFPI controller are a nonlinear function of input variables. These gains are changed as the input variables changed. The sufficient conditions for the proposed PFPI controller, which achieve the bounded-input bounded-output (BIBO) stability are obtained based on the small gain theorem. Finally, the obtained results indicate that the PFPI controller is able to reduce the effect of the system uncertainties compared with the fuzzy PI (FPI) controller.

[1]  Shaocheng Tong,et al.  Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics , 2017, IEEE Transactions on Cybernetics.

[2]  Min Gan,et al.  Design a Wind Speed Prediction Model Using Probabilistic Fuzzy System , 2012, IEEE Transactions on Industrial Informatics.

[3]  Han-Xiong Li,et al.  A novel probabilistic fuzzy set for uncertainties-based integration inference , 2012, 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings.

[4]  Ahmad M. El-Nagar,et al.  Simplified interval type-2 fuzzy logic system based on new type-reduction , 2014, J. Intell. Fuzzy Syst..

[5]  Ahmad M. El-Nagar,et al.  Interval Type-2 Fuzzy PID Controller: Analytical Structures and Stability Analysis , 2014 .

[6]  Chunyan Miao,et al.  A probabilistic fuzzy approach to modeling nonlinear systems , 2011, Neurocomputing.

[7]  李永明,et al.  Adaptive fuzzy robust output feedback control of nonlinear systems with unknown dead zones based on small-gain approach , 2014 .

[8]  Yongming Li,et al.  Adaptive output-feedback control design with prescribed performance for switched nonlinear systems , 2017, Autom..

[9]  Kuang-Chin Lu,et al.  Probabilistic Wavelet Fuzzy Neural Network based reactive power control for grid-connected three-phase PV system during grid faults , 2016 .

[10]  Chunlin Chen,et al.  Probabilistic Fuzzy Control of Mobile Robots for Range Sensor Based Reactive Navigation , 2011 .

[11]  K Salahshoor,et al.  Design and implementation of a new fuzzy PID controller for networked control systems. , 2008, ISA transactions.

[12]  Chang Chieh Hang,et al.  Parallel structure and tuning of a fuzzy PID controller , 2000, Autom..

[13]  Vineet Kumar,et al.  Parallel fuzzy P+fuzzy I+fuzzy D controller: Design and performance evaluation , 2010, Int. J. Autom. Comput..

[14]  John W. Seaman,et al.  Unity and diversity of fuzziness-from a probability viewpoint , 1994, IEEE Trans. Fuzzy Syst..

[15]  H. Ying,et al.  Deriving Analytical Input–Output Relationship for Fuzzy Controllers Using Arbitrary Input Fuzzy Sets and Zadeh Fuzzy AND Operator , 2006, IEEE Transactions on Fuzzy Systems.

[16]  Ahmad M. El-Nagar,et al.  Hardware-in-the-loop simulation of interval type-2 fuzzy PD controller for uncertain nonlinear system using low cost microcontroller , 2016 .

[17]  Zhi Liu,et al.  Probabilistic fuzzy logic system: A tool to process stochastic and imprecise information , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[18]  Fouad Mrad,et al.  Experimental comparative analysis of adaptive fuzzy logic controllers , 2002, IEEE Trans. Control. Syst. Technol..

[19]  Saudi Arabia,et al.  Comparison between Conventional and Fuzzy Logic PID Controllers for Controlling DC Motors , 2010 .

[20]  Han-Xiong Li,et al.  An Efficient Configuration for Probabilistic Fuzzy Logic System , 2012, IEEE Transactions on Fuzzy Systems.

[21]  Zhi Liu,et al.  A probabilistic fuzzy logic system for modeling and control , 2005, IEEE Transactions on Fuzzy Systems.

[22]  L. Zadeh Discussion: probability theory and fuzzy logic are complementary rather than competitive , 1995 .

[23]  C. A. Desoer,et al.  Nonlinear Systems Analysis , 1978 .

[24]  Guanrong Chen,et al.  Fuzzy PID controller: Design, performance evaluation, and stability analysis , 2000, Inf. Sci..

[25]  Wei Wu,et al.  Adaptive feedforward and feedback control of non-linear time-varying uncertain systems , 1999 .

[26]  Zhi Liu,et al.  A Probabilistic Neural-Fuzzy Learning System for Stochastic Modeling , 2008, IEEE Transactions on Fuzzy Systems.

[27]  T P Blanchett,et al.  PID gain scheduling using fuzzy logic. , 2000, ISA transactions.

[28]  Rong-Jong Wai,et al.  A fuzzy neural network controller for parallel-resonant ultrasonic motor drive , 1998, IEEE Trans. Ind. Electron..

[29]  Hao Ying,et al.  A general technique for deriving analytical structure of fuzzy controllers using arbitrary trapezoidal input fuzzy sets and Zadeh AND operator , 2003, Autom..

[30]  Faa-Jeng Lin,et al.  Squirrel-cage induction generator system using probabilistic fuzzy neural network for wind power applications , 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[31]  Mohammad R. Akbarzadeh-Totonchi,et al.  Probabilistic fuzzy logic and probabilistic fuzzy systems , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).