Fuzzy Controllers With Maximum Sensitivity for Servosystems

In this paper, new Takagi-Sugeno proportional-integral-fuzzy controllers (PI-FCs) to control a class of servosystems are proposed. The controlled plants in these control systems (CSs) are of integral type. In the first phase, there are designed linear PI controllers tuned in terms of the extended symmetrical optimum method to ensure the imposed overshoot and settling time with respect to the set point and to three possible types of load disturbance inputs. The connections between the two design parameters of the linear controllers and the desired maximum sensitivity and complementary sensitivity considering one of the disturbance inputs are derived. Then, accepting the approximate equivalence between the fuzzy controllers and the linear ones in certain conditions and using the modal equivalence principle, an attractive design method for the PI-FCs is proposed. With this respect, the design method guarantees maximum imposed sensitivity and complementary sensitivity for the CSs and, therefore, good responses with respect to modifications of the set point and of the disturbance inputs, and robustness with respect to model uncertainties. An application in speed control of a nonlinear servosystem with variable load, accompanied by experimental results, is provided to validate the new results, the fuzzy controllers, and a design method

[1]  J. Aracil,et al.  Stability Issues in Fuzzy Control , 2000 .

[2]  Tore Hägglund,et al.  Performance comparison between PID and dead-time compensating controllers , 2002 .

[3]  Bor-Sen Chen,et al.  Mixed H2/H∞ fuzzy output feedback control design for nonlinear dynamic systems: an LMI approach , 2000, IEEE Trans. Fuzzy Syst..

[4]  Yoichi Hori,et al.  An Algorithm for Extracting Fuzzy Rules Based on RBF Neural Network , 2006, IEEE Transactions on Industrial Electronics.

[5]  K.J. ÅSTRÖM,et al.  Design of PI Controllers based on Non-Convex Optimization , 1998, Autom..

[6]  Stefan Preitl,et al.  Optimisation criteria in development of fuzzy controllers with dynamics , 2004, Eng. Appl. Artif. Intell..

[7]  Jun Oh Jang,et al.  Deadzone compensation of an XY-positioning table using fuzzy logic , 2005, IEEE Trans. Ind. Electron..

[8]  Marco Liserre,et al.  Implementation issues of a fuzzy-logic-based three-phase active rectifier employing only Voltage sensors , 2005, IEEE Transactions on Industrial Electronics.

[9]  Marian P. Kazmierkowski,et al.  Active filtering function of three-phase PWM boost rectifier under different line voltage conditions , 2005, IEEE Transactions on Industrial Electronics.

[10]  T. Orlowska-Kowalska,et al.  Optimization of fuzzy-logic speed controller for DC drive system with elastic joints , 2004, IEEE Transactions on Industry Applications.

[11]  Tore Hägglund,et al.  Benchmark systems for PID control , 2000 .

[12]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[13]  M. Araki,et al.  Two-Degree-of-Freedom PID Controllers , 2003 .

[14]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[15]  A.G. Perry,et al.  A new design method for PI-like fuzzy logic controllers for DC-to-DC converters , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

[16]  Michio Sugeno,et al.  On stability of fuzzy systems expressed by fuzzy rules with singleton consequents , 1999, IEEE Trans. Fuzzy Syst..

[17]  E. Rosenwasser,et al.  Sensitivity of Automatic Control Systems , 1999 .

[18]  Meng Joo Er,et al.  Obstacle avoidance of a mobile robot using hybrid learning approach , 2005, IEEE Transactions on Industrial Electronics.

[19]  Farrokh Janabi-Sharifi,et al.  Design of a self-adaptive fuzzy tension controller for tandem rolling , 2005, IEEE Transactions on Industrial Electronics.

[20]  George Calcev,et al.  Some remarks on the stability of Mamdani fuzzy control systems , 1998, IEEE Trans. Fuzzy Syst..

[21]  Robert Babuška,et al.  An overview of fuzzy modeling for control , 1996 .

[22]  J. Doyle,et al.  Robust and optimal control , 1995, Proceedings of 35th IEEE Conference on Decision and Control.

[23]  Stefan Preitl,et al.  LOW COST FUZZY CONTROLLERS FOR CLASSES OF SECOND-ORDER SYSTEMS , 2002 .

[24]  Robert Babuska,et al.  Perspectives of fuzzy systems and control , 2005, Fuzzy Sets Syst..

[25]  László T. Kóczy,et al.  Fuzzy if... then rule models and their transformation into one another , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[26]  Faa-Jeng Lin,et al.  Robust Fuzzy Neural Network Sliding-Mode Control for Two-Axis Motion Control System , 2006, IEEE Transactions on Industrial Electronics.

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

[28]  Nobuyuki Matsui,et al.  GA-based evolutionary identification algorithm for unknown structured mechatronic systems , 2005, IEEE Transactions on Industrial Electronics.

[29]  Rong-Jong Wai,et al.  Robust Neural-Fuzzy-Network Control for Robot Manipulator Including Actuator Dynamics , 2006, IEEE Transactions on Industrial Electronics.

[30]  Antoni Arias,et al.  Novel Fuzzy Adaptive Sensorless Induction Motor Drive , 2006, IEEE Transactions on Industrial Electronics.

[31]  Qiang Sun,et al.  New self-tuning fuzzy PI control of a novel doubly salient permanent-magnet motor drive , 2006, IEEE Transactions on Industrial Electronics.

[32]  Stefan Preitl,et al.  An extension of tuning relations after symmetrical optimum method for PI and PID controllers , 1999, Autom..