A Combination of Fuzzy Logic and Neural Network Algorithms for Active Vibration Control

The construction of a dynamic absorber incorporating active vibration control is described. The absorber is a 2 degree of freedom spring-lumped mass system sliding on a guide pillar, with two internal vibration disturbance sources. Both the main mass and the secondary absorber mass were acted on by direct current (d.c.) servo motors, respectively, to suppress the vibration amplitude. In this paper, a new control approach is proposed by combining fuzzy logic and neural network algorithms to control the multi-input/multi-output (MIMO) system. Firstly, the fuzzy logic controller was designed for controlling the main influence part of the MIMO system. Secondly, the coupling neural network controller was employed to take care of the coupling effect and refine the control performance of the MIMO system. The experimental results show that the control system effectively suppresses the vibration amplitude and with good position tracking accuracy.

[1]  Takeshi Kimura,et al.  ADVANCED CONTROL METHODS OF ACTIVE SUSPENSION. , 1992 .

[2]  R. Hecht-Nielsen.  Counterpropagation networks. , 1987, Applied optics.

[3]  Fuzzy Logic in Control Systems : Fuzzy Logic , 2022 .

[4]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[5]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[6]  T. Warren Liao,et al.  An evaluation of ART1 neural models for GT part family and machine cell forming , 1993 .

[7]  Fe Frans Veldpaus,et al.  An Optimal Continuous Time Control Strategy for Active Suspensions with Preview , 1993 .

[8]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[10]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[11]  M.B.A. Abdel-Hady.  ACTIVE SUSPENSION WITH PREVIEW CONTROL. , 1994 .

[12]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[13]  Yosef S. Sherif,et al.  Applications of fuzzy set theory , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Hoyong Kim,et al.  An Active Suspension System using Fuzzy Logic Control , 1993, 1993 American Control Conference.

[15]  R. J. Anderson,et al.  IMPLEMENTING PREVIEW CONTROL ON AN OFF-ROAD VEHICLE WITH ACTIVE SUSPENSION , 1992 .

[16]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[17]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[18]  Robin S. Sharp,et al.  Optimization and Performance Enhancement of Active Suspensions for Automobiles under Preview of the Road , 1992 .

[19]  Kenneth Andrew Marko,et al.  Neural network architectures for active suspension control , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[20]  Edge C. Yeh,et al.  A fuzzy preview control scheme of active suspension for rough road , 1994 .

[21]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[22]  Toshio Yoshimura,et al.  An Active Suspension Model For Rail/Vehicle Systems With Preview and Stochastic Optimal Control , 1993 .

[23]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[24]  Aleksander Hac.  Optimal Linear Preview Control of Active Vehicle Suspension , 1992 .