A trapezoidal fuzzy support vector regression system for humanoid robots

Time-varying external disturbances cause instability of humanoid robots, or even tip robots over. In this work, a trapezoidal fuzzy support vector regression (TFSVR)-based control system is proposed to learn the external disturbances and increase the zero-moment-point (ZMP) stability margin of humanoid robots. First, the humanoid states and the corresponding control torques of the joints for training the controller is collected by implementing simulation experiments. Secondly, a TFSVR with a time-related trapezoidal fuzzy membership function (TFMF) is proposed to train the controller using the simulated data. Thirdly, the parameters of the proposed TFSVR are updated using a cubature Kalman filter (CKF). Simulation results are provided. The proposed method is shown to be effective to learn and adapt occasional external disturbances and ensure the stability margin of the robot.

[1]  Simon Haykin,et al.  Control theoretic approach to tracking radar: First step towards cognition , 2011, Digit. Signal Process..

[2]  Simon Haykin,et al.  Cubature Kalman smoothers , 2011, Autom..

[3]  Sharad Singhal,et al.  Training Multilayer Perceptrons with the Extende Kalman Algorithm , 1988, NIPS.

[4]  Lee A. Feldkamp,et al.  Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.

[5]  Christophe Sabourin,et al.  Robustness of the dynamic walk of a biped robot subjected to disturbing external forces by using CMAC neural networks , 2005, Robotics Auton. Syst..

[6]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[7]  Simon Haykin,et al.  Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations , 2010, IEEE Transactions on Signal Processing.

[8]  Dan Simon,et al.  Training fuzzy systems with the extended Kalman filter , 2002, Fuzzy Sets Syst..

[9]  Y. Wang,et al.  A Type-2 Fuzzy Switching Control System for Biped Robots , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Tzuu-Hseng S. Li,et al.  A biped gait learning algorithm for humanoid robots based on environmental impact assessed artificial bee colony , 2015, IEEE Access.

[11]  Emilio Arnieri,et al.  Support Vector Regression Machines to Evaluate Resonant Frequency of Elliptic Substrate Integrate Waveguide Resonators , 2008 .

[12]  Yingmin Jia,et al.  Location of Mobile Station With Maneuvers Using an IMM-Based Cubature Kalman Filter , 2012, IEEE Transactions on Industrial Electronics.

[13]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

[14]  Jun Ho Oh,et al.  Biped Walking Pattern Generation Using an Analytic Method for a Unit Step With a Stationary Time Interval Between Steps , 2015, IEEE Transactions on Industrial Electronics.

[15]  Tao Jiang,et al.  Generalized defuzzification strategies and their parameter learning procedures , 1996, IEEE Trans. Fuzzy Syst..