A Review of Intelligent Control Algorithms Applied to Robot Motion Control

Intelligent control algorithm has been widely applied to robot control. Many single control strategies, such as iterative learning control, fuzzy control, neural network and adaptive control have been used to overcome obstacles in motion control and path tracing of robot. Some examples have been taken to explain different strategies one by one in this paper. What is more, hybrid control methods which combine single control strategy with others also have an essential role to play in robot control. This paper will illustrate various application of hybrid methods in robot control. In general, this study highlights intelligent control algorithms review of robot control.

[1]  Gordon Cheng,et al.  Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots , 2014, Biological Cybernetics.

[2]  Ching-Chih Tsai Direct Adaptive Fuzzy-Wavelet-Neural-Network Control for Electric Two-Wheeled Robotic Vehicles , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[3]  W. Thomas Miller,et al.  Sensor-based control of robotic manipulators using a general learning algorithm , 1987, IEEE J. Robotics Autom..

[4]  Jan Swevers,et al.  Learning Feasible and Time-optimal Trajectories for Robot Manipulators , 2017 .

[5]  M. Maeda,et al.  Fuzzy drive control of an autonomous mobile robot , 1991 .

[6]  Dongkyoung Chwa,et al.  Obstacle Avoidance Method for Wheeled Mobile Robots Using Interval Type-2 Fuzzy Neural Network , 2015, IEEE Transactions on Fuzzy Systems.

[7]  Xiaoguang Wu,et al.  System identification of biped robot based on dynamic fuzzy neural network and improved RBF neural network , 2016, 2016 IEEE International Conference on Information and Automation (ICIA).

[8]  Martin D. Buhmann,et al.  A Note on the Local Stability of Translates of Radial Basis Functions , 1993 .

[9]  Junfei Qiao,et al.  Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network , 2012 .

[10]  Francis J. Doyle,et al.  Survey on iterative learning control, repetitive control, and run-to-run control , 2009 .

[11]  Kang Li Advanced methods for nonlinear system modelling and identification , 2015 .

[12]  Yiannis S. Boutalis,et al.  Fuzzy Discrete Event Systems for Multiobjective Control: Framework and Application to Mobile Robot Navigation , 2012, IEEE Transactions on Fuzzy Systems.

[13]  Yunhui Liu,et al.  Adaptive Visual Servoing Using Point and Line Features With an Uncalibrated Eye-in-Hand Camera , 2008, IEEE Transactions on Robotics.

[14]  Goele Pipeleers,et al.  Iterative learning control for optimal path following problems , 2013, 52nd IEEE Conference on Decision and Control.

[15]  Sauro Longhi,et al.  Lyapunov-based switching control using neural networks for a remotely operated vehicle , 2007, Int. J. Control.

[16]  Mário Sarcinelli Filho,et al.  A multi-layer control scheme for multi-robot formations with obstacle avoidance , 2009, 2009 International Conference on Advanced Robotics.

[17]  Josef Böhm Robust adaptive control: Petros A. Ioannou, Jing Sun, Prentice Hall, Englewood, Cliffs, NJ, ISBN: 0-13-439100-4 , 2001, Autom..

[18]  Veer Alakshendra,et al.  Design of robust adaptive controller for a four wheel omnidirectional mobile robot , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[19]  Daniel Eggert,et al.  Neural Network Control , 2003 .

[20]  Gang Feng A robust approach to adaptive control algorithms , 1994, IEEE Trans. Autom. Control..

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

[22]  David Naso,et al.  Fuzzy control of a mobile robot , 2006, IEEE Robotics & Automation Magazine.

[23]  Qing Song,et al.  Robust Adaptive Dead Zone Technology for Fault-Tolerant Control of Robot Manipulators Using Neural Networks , 2002, J. Intell. Robotic Syst..

[24]  Hani Hagras,et al.  Learning and adaptation of an intelligent mobile robot navigator operating in unstructured environment based on a novel online Fuzzy-Genetic system , 2004, Fuzzy Sets Syst..

[25]  Brian J. Driessen Overlapping-multi-layer deadzone for alleviating over conservativeness in robot adaptive tracking , 2006, Syst. Control. Lett..

[26]  Marcelo Godoy Simões,et al.  Fuzzy optimisation based control of a solar array system , 1999 .

[27]  Yasuo Kuniyoshi,et al.  Three dimensional bipedal stepping motion using neural oscillators-towards humanoid motion in the real world , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[28]  Zheng Liu,et al.  Hybrid control of biped robots in the double-support phase via H/sub /spl infin// approach and fuzzy neural networks , 2003 .

[29]  Liu Hsu,et al.  Design of a high performance variable structure position control of ROVs , 1995 .

[30]  Letitia Mirea,et al.  Dynamic multivariate B-spline neural network design using orthogonal least squares algorithm for non-linear system identification , 2014, 2014 18th International Conference on System Theory, Control and Computing (ICSTCC).

[31]  B. Heimann,et al.  Application study on Iterative Learning Control of high speed motions for parallel robotic manipulator , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[32]  Abdelkrim Boukabou,et al.  Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems , 2017, Neurocomputing.

[33]  Chian-Song Chiu,et al.  Hybrid Fuzzy Model-Based Control of Nonholonomic Systems: A Unified Viewpoint , 2008, IEEE Transactions on Fuzzy Systems.

[34]  Xia Zhang,et al.  Using neural network realize the identification of nonlinear system , 2011, 2011 International Conference on Electric Information and Control Engineering.

[35]  Suk-Kyo Hong,et al.  Unifying strategies of obstacle avoidance and shooting for soccer robot systems , 2007, 2007 International Conference on Control, Automation and Systems.

[36]  Hiroshi Shimizu,et al.  Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment , 1991, Biological Cybernetics.

[37]  S. S. Thakur,et al.  Design of Analog Fuzzy Controller for Autonomous Mobile Robot , 2017, Int. J. Uncertain. Fuzziness Knowl. Based Syst..