Interval type-2 fuzzy adaptive tracking control design for PMDC motor with the sector dead-zones

We propose IT2FAC scheme to reduce the sector dead-zone nonlinearities.This scheme integrates variable structure to achieve the H ∞ tracking performance.This scheme uses the Lyapunov criterion to ensure the boundedness of all states.This scheme exhibits faster tracking responses for the PMDC motor systems. This paper deals with the permanent magnet DC motor system with sector dead-zones and external disturbances via interval type-2 fuzzy adaptive tracking control scheme to achieve H ∞ tracking performance. The type-2 fuzzy dynamic model is used to approximate the motor dynamics without constructing sector dead-zone inverse, where the parameters of the fuzzy model are obtained both from the fuzzy inference and online update laws. Based on the Lyapunov criterion and Riccati-inequality, the control scheme is derived to stabilize the closed-loop system such that all states of the system are guaranteed to be bounded and H ∞ tracking performance is achieved due to uncertainties, dead-zone nonlinearities, and external disturbances. The advantage of the proposed control scheme is that it can better handle the vagueness or uncertainties inherent in linguistic words using fuzzy set membership functions with adaptation capability by linear analytical results instead of estimating non-linear system functions as the system parameters are unknown. Finally, the simulations of the PMDC motor system with sector dead-zone nonlinearities are used to illustrate the effectiveness of the proposed control scheme and the performance comparisons with the three-dimensional autonomous R o ? ssler system (Wang and Chua, 2008) are used to validate the H ∞ tracking performance of the PMDC motor system.

[1]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[2]  S.-Y. Chang,et al.  Pre- and post-filtering approach to sensorless VSS speed control of a permanent magnet DC motor subject to Hammerstein and Wiener nonlinearities , 2013 .

[3]  Shuang Zhao,et al.  An Integrated 20-kW Motor Drive and Isolated Battery Charger for Plug-In Vehicles , 2013, IEEE Transactions on Power Electronics.

[4]  A. Astolfi.  Disturbance Attenuation and H,-Control Via Measurement Feedback in , 1992 .

[5]  Bor-Sen Chen,et al.  H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach , 1996, IEEE Trans. Fuzzy Syst..

[6]  Amir Rezaei,et al.  An interval-valued fuzzy controller for complex dynamical systems with application to a 3-PSP parallel robot , 2014, Fuzzy Sets Syst..

[7]  Fang Lin Luo,et al.  Speed nonlinear control of DC motor drive with field weakening , 2001, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248).

[8]  L. Parsa,et al.  Model Reference Adaptive Control of Five-Phase IPM Motors Based on Neural Network , 2011, IEEE Transactions on Industrial Electronics.

[9]  Qing Zhu,et al.  Adaptive Fuzzy Control of Nonlinear Systems in Pure Feedback Form Based on Input-to-State Stability , 2010, IEEE Transactions on Fuzzy Systems.

[10]  Gang Tao,et al.  Adaptive Control of a Class of Nonlinear Systems with Unknown Dead-Zones , 1996 .

[11]  Kai Liu,et al.  Design of Variable Structure Control Law for the Nonlinear System with Parameter Perturbation , 2011 .

[12]  Oscar Castillo,et al.  Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm , 2013, Expert Syst. Appl..

[13]  Bharat Bhushan,et al.  Adaptive control of DC motor using bacterial foraging algorithm , 2011, Appl. Soft Comput..

[14]  Witold Pedrycz,et al.  Granular Computing: Analysis and Design of Intelligent Systems , 2013 .

[15]  Alan J. Laub,et al.  The LMI control toolbox , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[16]  Jason T. Teeter,et al.  A novel fuzzy friction compensation approach to improve the performance of a DC motor control system , 1996, IEEE Trans. Ind. Electron..

[17]  ChenBor-Sen,et al.  H tracking design of uncertain nonlinear SISO systems , 1996 .

[18]  Gerasimos Rigatos,et al.  Adaptive fuzzy control of DC motors using state and output feedback , 2009 .

[19]  Robert Ivor John,et al.  Geometric Type-1 and Type-2 Fuzzy Logic Systems , 2007, IEEE Transactions on Fuzzy Systems.

[20]  Xu Zibin,et al.  Study on Improving the Dead Zone Character of Electro-Hydraulic Servo System with Dither , 2010, 2010 International Conference on Digital Manufacturing & Automation.

[21]  Yongfu Wang,et al.  State observer-based adaptive fuzzy output-feedback control for a class of uncertain nonlinear systems , 2010, Inf. Sci..

[22]  Jerry M. Mendel,et al.  Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters , 2000, IEEE Trans. Fuzzy Syst..

[23]  Bor-Sen Chen,et al.  H∞ decentralized fuzzy model reference tracking control design for nonlinear interconnected systems , 2001, IEEE Trans. Fuzzy Syst..

[24]  Jerry M. Mendel,et al.  Super-Exponential Convergence of the Karnik–Mendel Algorithms for Computing the Centroid of an Interval Type-2 Fuzzy Set , 2007, IEEE Transactions on Fuzzy Systems.

[25]  P. Olver.  Nonlinear Systems , 2013 .

[26]  Xin-Ping Guan,et al.  Adaptive Tracking Controller Design of Nonlinear Systems With Time Delays and Unknown Dead-Zone Input , 2008, IEEE Transactions on Automatic Control.

[27]  Oscar Castillo,et al.  A review on the design and optimization of interval type-2 fuzzy controllers , 2012, Appl. Soft Comput..

[28]  Abdesselem Boulkroune,et al.  Adaptive fuzzy controller for multivariable nonlinear state time-varying delay systems subject to input nonlinearities , 2011, Fuzzy Sets Syst..

[29]  K. T. Chau,et al.  Anti-control of chaos of a permanent magnet DC motor system for vibratory compactors , 2008 .

[30]  Shaocheng Tong,et al.  Adaptive Fuzzy Output Feedback Tracking Backstepping Control of Strict-Feedback Nonlinear Systems With Unknown Dead Zones , 2012, IEEE Transactions on Fuzzy Systems.

[31]  Bor-Sen Chen,et al.  Robustness design of nonlinear dynamic systems via fuzzy linear control , 1999, IEEE Trans. Fuzzy Syst..

[32]  Shaocheng Tong,et al.  Adaptive fuzzy decentralized output feedback control for stochastic nonlinear large-scale systems , 2012, Neurocomputing.

[33]  Maysam F. Abbod,et al.  Fuzzy Logic-Based Anti-Sway Control Design for Overhead Cranes , 2000, Neural Computing & Applications.

[34]  B. J. Chalmers.  Influence of saturation in brushless permanent-magnet motor drives , 1992 .

[35]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[36]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[37]  Shaocheng Tong,et al.  Observer-based adaptive fuzzy tracking control for a class of uncertain nonlinear MIMO systems , 2011, Fuzzy Sets Syst..

[38]  Roberto Sepúlveda,et al.  Performance of a Simple Tuned Fuzzy Controller and a PID Controller on a DC Motor , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[39]  Oscar Castillo,et al.  Particle Swarm Optimization for Average Approximation of Interval Type-2 Fuzzy Inference Systems Design in FPGAs for Real Applications , 2013, Recent Advances on Hybrid Intelligent Systems.

[40]  Jinzhu Peng,et al.  Identification and adaptive neural network control of a DC motor system with dead-zone characteristics. , 2011, ISA transactions.

[41]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[42]  Yu Yali,et al.  Fuzzy Auto-adjust PID Controller Design of Brushless DC Motor , 2012 .

[43]  V. Kolmanovskii,et al.  Stability of Functional Differential Equations , 1986 .

[44]  Patricia Melin,et al.  Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications , 2013, Appl. Soft Comput..

[45]  Shuzhi Sam Ge,et al.  Adaptive Neural Network Tracking Control of MIMO Nonlinear Systems With Unknown Dead Zones and Control Directions , 2009, IEEE Transactions on Neural Networks.

[46]  Ping-Zong Lin,et al.  Type-2 fuzzy controller design using a sliding-mode approach for application to DC – DC converters , 2000 .

[47]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[48]  Wei-Yen Wang,et al.  Sliding mode control for uncertain nonlinear systems with multiple inputs containing sector nonlinearities and deadzones , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[49]  Jang Myung Lee,et al.  Precise Positioning of Nonsmooth Dynamic Systems Using Fuzzy Wavelet Echo State Networks and Dynamic Surface Sliding Mode Control , 2013, IEEE Transactions on Industrial Electronics.

[50]  A. Isidori,et al.  Disturbance attenuation and H/sub infinity /-control via measurement feedback in nonlinear systems , 1992 .

[51]  Abid Yahya,et al.  Variable Structure System with Sliding Mode Controller , 2013 .

[52]  Jun Oh Jang,et al.  A deadzone compensator of a DC motor system using fuzzy logic control , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[53]  Jack K. Hale,et al.  Introduction to Functional Differential Equations , 1993, Applied Mathematical Sciences.

[54]  Yaonan Wang,et al.  Robust adaptive sliding-mode control of condenser-cleaning mobile manipulator using fuzzy wavelet neural network , 2014, Fuzzy Sets Syst..

[55]  Y. Sozer,et al.  Integrated electric motor drive and power electronics for bidirectional power flow between electric vehicle and DC or AC grid , 2012, 2012 IEEE Energy Conversion Congress and Exposition (ECCE).

[56]  Abdesselem Boulkroune,et al.  Design of a fuzzy adaptive controller for MIMO nonlinear time-delay systems with unknown actuator nonlinearities and unknown control direction , 2010, Inf. Sci..

[57]  Oscar Castillo,et al.  A cognitive map and fuzzy inference engine model for online design and self fine-tuning of fuzzy logic controllers , 2009, HIS 2009.

[58]  Nohé R. Cázarez-Castro,et al.  Designing Type-1 and Type-2 Fuzzy Logic Controllers via Fuzzy Lyapunov Synthesis for nonsmooth mechanical systems , 2012, Eng. Appl. Artif. Intell..

[59]  Jerry M. Mendel,et al.  Interval Type-2 Fuzzy Logic Systems Made Simple , 2006, IEEE Transactions on Fuzzy Systems.

[60]  Oscar Castillo,et al.  Optimization of type-2 fuzzy systems based on bio-inspired methods: A concise review , 2012, Inf. Sci..

[61]  Bing Chen,et al.  Adaptive control for nonlinear MIMO time-delay systems based on fuzzy approximation , 2013, Inf. Sci..

[62]  Shuzhi Sam Ge,et al.  Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities , 2010, IEEE Transactions on Neural Networks.

[63]  Jerry M. Mendel,et al.  Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..

[64]  Xinping Guan,et al.  Brief paper: adaptive backstepping fault-tolerant control for unmatched non-linear systems against actuator dead-zone , 2010 .