Upswing and stabilization control of inverted pendulum system based on the SIRMs dynamically connected fuzzy inference model

Abstract A new fuzzy controller is presented based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model for upswing and stabilization control of inverted pendulum system. The fuzzy controller takes the angle and angular velocity of the pendulum and the position and velocity of the cart as its input items, and the driving force as its output item. Each input item is assigned with a SIRM and a dynamic importance degree. When the pendulum locates at the pending domain, the fuzzy controller becomes an upswing controller by using the saturation feature of the membership functions of the pendulum angle. When the pendulum locates at the upright domain, the fuzzy controller then becomes a stabilization controller and realizes smoothly the pendulum angular control and the cart position control in parallel by using the SIRMs and the dynamic importance degrees. The fuzzy controller has a simple structure and is easily understandable compared with the other approaches. Simulation results show that the fuzzy controller can swing up the pendulum from the pending position and then stabilize the whole system in about 3.0 s.

[1]  James M. Tien,et al.  On a fuzzy-neural hierarchical controller with a self-generating knowledge base , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[2]  Hikaru Inooka,et al.  Application of tree search to the swinging control of a pendulum , 1992, IEEE Trans. Syst. Man Cybern..

[3]  Katsumasa Matsuura.  A Fuzzy Control of Upswing and Stabilization of Inverted Pole Including Control of Cart Position , 1993 .

[4]  Byoungho Lee,et al.  Fuzzy rule base derivation using neural network-based fuzzy logic controller by self-learning , 1993, Proceedings of IECON '93 - 19th Annual Conference of IEEE Industrial Electronics.

[5]  Jianqiang Yi,et al.  A proposal of SIRMs dynamically connected fuzzy inference model for plural input fuzzy control , 2002, Fuzzy Sets Syst..

[6]  K. Furuta,et al.  VSS controller design for discrete-time systems , 1993, Proceedings of IECON '93 - 19th Annual Conference of IEEE Industrial Electronics.

[7]  J. Yi,et al.  Trajectory tracking control of unconstrained objects based on the SIRMs dynamically connected fuzzy inference model , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[8]  Katsuhisa Furuta,et al.  Control of unstable mechanical system Control of pendulum , 1976 .

[9]  Norio Baba,et al.  A new approach for finding the global minimum of error function of neural networks , 1989, Neural Networks.

[10]  N. Yubazaki.  SIRMs dynamically connected fuzzy inference model and its applications , 1997 .

[11]  S. Yasunobu,et al.  Swing up fuzzy controller for inverted pendulum based on a human control strategy , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[12]  Masaharu Mizumoto.  Fuzzy Singleton-type Reasoning Method , 1994 .

[13]  S. Omatu,et al.  Stability of inverted pendulum by neuro-PID control with genetic algorithm , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[14]  Akira Ohsumi,et al.  Nonlinear control of swing-up and stabilization of an inverted pendulum , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[15]  Katsuhisa Furuta,et al.  Swing up control of inverted pendulum , 1991, Proceedings IECON '91: 1991 International Conference on Industrial Electronics, Control and Instrumentation.

[16]  William S. Levine,et al.  Nonlinear controller for an inverted pendulum having restricted travel , 1995, Autom..