Intelligent Controller for Robotic Motion Control

A self-organizing fuzzy controller (SOFC) has been developed to control complicated and nonlinear systems. However, it is arduous to choose an appropriate learning rate and a suitable weighting distribution of the SOFC to achieve satisfactory performance for system control. Furthermore, the SOFC is mainly used to control single-input single-output systems. When the SOFC is applied to manipulating a robotic system, which is an example of multiple-input multiple-output systems, it is difficult to eliminate the dynamic coupling effects between the degrees of freedom (DOFs) of the robotic system. To address the problems, this study developed a self-organizing fuzzy radial basis-function neural-network (RBFN) controller (SFRBNC) for robotic systems. The SFRBNC uses an RBFN to regulate in real time these parameters of the SOFC to optimal values, thereby solving the problem faced when the SOFC is applied. The RBFN has coupling weighting regulation ability, so it can eliminate the dynamic coupling effects between the DOFs for robotic system control. From the experimental results of the 6-DOF robot tests, the SFRBNC demonstrated better control performance than the SOFC.

[1]  Frank L. Lewis,et al.  Control of Robot Manipulators , 1993 .

[2]  Osamu Hasegawa,et al.  Associative Memory for Online Learning in Noisy Environments Using Self-Organizing Incremental Neural Network , 2009, IEEE Transactions on Neural Networks.

[3]  Ji Lin,et al.  A smoothing Levenberg–Marquardt method for the extended linear complementarity problem , 2009 .

[4]  Ruey-Jing Lian,et al.  Intelligent Control of Active Suspension Systems , 2011, IEEE Transactions on Industrial Electronics.

[5]  A. K. Rigler,et al.  Accelerating the convergence of the back-propagation method , 1988, Biological Cybernetics.

[6]  Yoichi Hori,et al.  Optimal Control Design for Robust Fuzzy Friction Compensation in a Robot Joint , 2009, IEEE Transactions on Industrial Electronics.

[7]  Shiuh-Jer Huang,et al.  A Combination of Fuzzy Logic and Neural Network Algorithms for Active Vibration Control , 1996 .

[8]  Ruey-Jing Lian,et al.  Hybrid fuzzy-logic and neural-network controller for MIMO systems , 2009 .

[9]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[10]  Vijander Singh,et al.  ANN-based estimator for distillation using Levenberg-Marquardt approach , 2007, Eng. Appl. Artif. Intell..

[11]  Han-Pang Huang,et al.  Development and Fuzzy Control of a Pipe Inspection Robot , 2010, IEEE Transactions on Industrial Electronics.

[12]  Ruey-Jing Lian,et al.  New modifying self-organizing fuzzy controller for robotic motion control , 2006 .

[13]  Chia-Feng Juang,et al.  Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control , 2009, IEEE Transactions on Industrial Electronics.

[14]  Shiuh-Jer Huang,et al.  A hybrid fuzzy logic and neural network algorithm for robot motion control , 1997, IEEE Trans. Ind. Electron..

[15]  C. S. G. Lee,et al.  Robotics: Control, Sensing, Vision, and Intelligence , 1987 .

[16]  Ruey-Jing Lian,et al.  Self-Organizing Fuzzy Controller for Gas-Assisted Injection Molding Combination Systems , 2010, IEEE Transactions on Control Systems Technology.

[17]  L. B. Jack,et al.  Investigation of Self-Organizing Oscillator Networks for Use in Clustering Microarray Data , 2008, IEEE Transactions on NanoBioscience.

[18]  Atsushi Kawamoto,et al.  Stabilization of geometrically nonlinear topology optimization by the Levenberg–Marquardt method , 2009 .

[19]  Ruey-Jing Lian,et al.  Self-organizing fuzzy controller for injection molding machines , 2010 .

[20]  S. Shao.  Fuzzy self-organizing controller and its application for dynamic processes , 1988 .

[21]  Ebrahim H. Mamdani,et al.  A linguistic self-organizing process controller , 1979, Autom..

[22]  Wei Sun,et al.  RBF Networks-Based Adaptive Inverse Model Control System for Electronic Throttle , 2010, IEEE Transactions on Control Systems Technology.

[23]  Zijiang Yang,et al.  Lag Synchronization of Unknown Chaotic Delayed Yang–Yang-Type Fuzzy Neural Networks With Noise Perturbation Based on Adaptive Control and Parameter Identification , 2009, IEEE Transactions on Neural Networks.

[24]  Hujun Yin,et al.  A self-organising mixture autoregressive network for FX time series modelling and prediction , 2009, Neurocomputing.

[25]  Chieh-Li Chen,et al.  Self-organizing fuzzy logic controller design , 1993 .

[26]  T. Martin McGinnity,et al.  Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a Brain–Computer Interface , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[27]  Kuo-Ching Chiou,et al.  Development and application of a novel radial basis function sliding mode controller , 2003 .

[28]  Maoan Han,et al.  Synchronization schemes for coupled identical Yang–Yang type fuzzy cellular neural networks , 2009 .

[29]  Shiuh-Jer Huang,et al.  A stable self-organizing fuzzy controller for robotic motion control , 2000, IEEE Trans. Ind. Electron..

[30]  Cheng-I Chen,et al.  Radial-Basis-Function-Based Neural Network for Harmonic Detection , 2010, IEEE Transactions on Industrial Electronics.