A method for improving the robustness of PID control

In this paper, an effective method is proposed for robust proportional-integral-derivative (PID) control that is easily implementable on commonly used equipment such as programmable logic controller (PLC) and programmable automation controller (PAC). The method is based on a two-loop model following control (MFC) system containing a nominal model of the controlled plant and two PID controllers. Basic features exhibited by the MFC structure are presented, and a technique to tune both component controllers is given. The proposed structures have been implemented in a programmable logic controller and tested on control plants with perturbed parameters. Also, the proposed control system has been checked for its performance in cases when the operation of PID controllers is based on fuzzy logic. Tuning rules for the fuzzy controllers in the presented MFC system have been proposed. Results of tests lend support to the view that the proposed control structures may find wide application to robust control of plants with time-varying parameters.

[1]  Weng Khuen Ho,et al.  Tuning of PID Controllers based on Gain and Phase Margin Specifications , 1993 .

[2]  Weng Khuen Ho,et al.  Optimal Gain and Phase Margin Tuning for PID Controllers , 1998, Autom..

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

[4]  Chang Chieh Hang,et al.  Single-loop controller design via IMC principles , 2001, Autom..

[5]  André Titli,et al.  Fuzzy controller: design, evaluation, parallel and hierarchical combination with a PID controller , 1995 .

[6]  Guomin Li,et al.  Robust nonlinear nominal-model following control to overcome deadzone nonlinearities , 2001, IEEE Trans. Ind. Electron..

[7]  Stefan Domek,et al.  PID Robust Model Following Control , 2000 .

[8]  Chyi Hwang,et al.  Solution of a non-convex optimization arising in PI/PID control design , 2002, Autom..

[9]  Stanislaw Skoczowski,et al.  Robust model following control with use of a plant model , 2001, Int. J. Syst. Sci..

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

[11]  Tore Hägglund,et al.  Advances in Pid Control , 1999 .

[12]  Cheng-Ching Yu.  Autotuning of PID Controllers , 1999 .

[13]  S. Daley,et al.  Optimal-Tuning PID Control for Industrial Systems , 2000 .

[14]  Shankar P. Bhattacharyya,et al.  New results on the synthesis of PID controllers , 2002, IEEE Trans. Autom. Control..

[15]  Friedrich M. Wahl,et al.  Forward-model-based control system for robot manipulators , 2004, Robotica.

[16]  M. Mizumoto.  Realization of PID controls by fuzzy control methods , 1995 .

[17]  Zoran Gajic,et al.  A Reference Guide to PID Controllers in the Nineties , 2000 .

[18]  Kyihwan Park,et al.  Adaptive control for time optimal tracking systems , 1996 .

[19]  Jae-Bok Song,et al.  Control of linear motor-based arm motion generator using active impedance implementation , 1999 .

[20]  Richard Marquez,et al.  An extension of predictive control, PID regulators and Smith predictors to some linear delay systems , 2002 .

[21]  Krzysztof Pietrusewicz,et al.  Anti-Windup Implementation in a Fuzzy-Logic Model-Following Robust PID Controller , 2004 .

[22]  Guomin Li,et al.  A novel model-following scheme with simple structure for electrical position servo systems , 1998, Int. J. Syst. Sci..

[23]  Aidan O'Dwyer,et al.  Handbook of PI and PID controller tuning rules , 2003 .

[24]  Tore Hägglund,et al.  The future of PID control , 2000 .

[25]  Tzuen-Lih Chern,et al.  Automatic Voltage Regulator Design by Modified Discrete Integral Variable Structure Model Following Control , 1998, Autom..

[26]  Toshiharu Sugie,et al.  Robust model following control with prescribed accuracy for uncertain nonlinear systems , 1993 .

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