Design and implementation of fuzzy-PD controller based on relation models: A cross-entropy optimization approach

Abstract In this paper, an intelligent system use fuzzy-PD controller based on relation models is developed for a two-wheeled self-balancing robot. Scaling factors of the fuzzy-PD controller are optimized by a Cross-Entropy optimization method. A linear Quadratic Regulator is designed to bring a comparison with the fuzzy-PD controller by control quality parameters. The controllers are ported and run on STM32F4 Discovery Kit based on the real-time operating system. The experimental results indicate that the proposed fuzzy-PD controller runs exactly on embedded system and has desired performance in term of fast response, good balance and stabilize.

[1]  Shin'ichi Yuta,et al.  Baggage Transportation and Navigation by a Wheeled Inverted Pendulum Mobile Robot , 2009, IEEE Transactions on Industrial Electronics.

[2]  Chih-Hui Chiu,et al.  The Design and Implementation of a Wheeled Inverted Pendulum Using an Adaptive Output Recurrent Cerebellar Model Articulation Controller , 2010, IEEE Transactions on Industrial Electronics.

[3]  Jian Fang.  The research on the Application of Fuzzy Immune PD Algorithm in the Two-Wheeled and Self-Balancing Robot System , 2014 .

[4]  David J. Edwards,et al.  Cross-entropy optimisation of multiple-input multiple-output capacity by transmit antenna selection , 2007 .

[5]  Alfred C. Rufer,et al.  JOE: a mobile, inverted pendulum , 2002, IEEE Trans. Ind. Electron..

[6]  Miguel A. Olivares-Méndez,et al.  Cross-Entropy Optimization for Scaling Factors of a Fuzzy Controller: A See-and-Avoid Approach for Unmanned Aerial Systems , 2013, J. Intell. Robotic Syst..

[7]  Rodolfo E. Haber,et al.  Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process , 2010, Inf. Sci..

[8]  Chih-Hui Chiu,et al.  Real-time control of a wheeled inverted pendulum based on an intelligent model free controller , 2011 .

[9]  Reuven Y. Rubinstein,et al.  Optimization of computer simulation models with rare events , 1997 .

[10]  P. Albertos,et al.  Linear interpolation based controller design for trajectory tracking under uncertainties: Application to mobile robots , 2015 .

[11]  Seul Jung,et al.  Fuzzy Logic Application to a Two-wheel Mobile Robot for Balancing Control Performance , 2012, Int. J. Fuzzy Log. Intell. Syst..

[12]  Kaustubh Pathak,et al.  Velocity and position control of a wheeled inverted pendulum by partial feedback linearization , 2005, IEEE Transactions on Robotics.

[13]  Witold Pedrycz,et al.  Fuzzy control and fuzzy systems , 1989 .

[14]  Jorge Angeles,et al.  A New Family of Two-Wheeled Mobile Robots: Modeling and Controllability , 2007, IEEE Transactions on Robotics.