Historical perspective and state of the art in connectionist learning control

Connectionist learning control is surveyed, starting with work by learning control engineers in the sixties and early seventies. The controllers are reviewed in a roughly chronological order, stressing the concepts and interaction of components in each learning control architecture. Some comparisons with adaptive control techniques are made, some necessarily so because of the integration of adaptive control techniques into some of the systems.<<ETX>>

[1]  K. Fu,et al.  A heuristic approach to reinforcement learning control systems , 1965 .

[2]  J. Sklansky,et al.  Learning systems for automatic control , 1966 .

[3]  Arthur E. Bryson,et al.  Applied Optimal Control , 1969 .

[4]  King-Sun Fu,et al.  Learning control systems--Review and outlook , 1970 .

[5]  Kumpati S. Narendra,et al.  Stochastic Automata Models with Applications to Learning Systems , 1973, IEEE Trans. Syst. Man Cybern..

[6]  Bernard Widrow,et al.  Punish/Reward: Learning with a Critic in Adaptive Threshold Systems , 1973, IEEE Trans. Syst. Man Cybern..

[7]  Kumpati S. Narendra,et al.  Learning Automata - A Survey , 1974, IEEE Trans. Syst. Man Cybern..

[8]  James S. Albus,et al.  Data Storage in the Cerebellar Model Articulation Controller (CMAC) , 1975 .

[9]  M. Raibert Analytical equations vs. table look-up for manipulation: A unifying concept , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.

[10]  J. Albus Mechanisms of planning and problem solving in the brain , 1979 .

[11]  Hendrik Van Brussel,et al.  Further developments of the active adaptable compliant wrist (AACW) for robot assembly , 1981 .

[12]  Hendrik Van Brussel,et al.  A self-learning automaton with variable resolution for high precision assembly by industrial robots , 1982 .

[13]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  H. Elliott,et al.  Nonlinear Adaptive Control of Mechanical Linkage Systems with Application to Robotics , 1983, 1983 American Control Conference.

[15]  P. Anandan,et al.  Pattern-recognizing stochastic learning automata , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  E. G. Harokopos Optimal learning control of mechanical manipulators in repetitive motions , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[17]  Tien C. Hsia,et al.  Adaptive control of robot manipulators - A review , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[18]  D. E. Whitney,et al.  Historical Perspective and State of the Art in Robot Force Control , 1987 .

[19]  Paul J. Werbos,et al.  Building and Understanding Adaptive Systems: A Statistical/Numerical Approach to Factory Automation and Brain Research , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  Charles W. Anderson,et al.  Strategy Learning with Multilayer Connectionist Representations , 1987 .

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

[22]  Filson H. Glanz,et al.  Application of a General Learning Algorithm to the Control of Robotic Manipulators , 1987 .

[23]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[24]  J. A. Franklin,et al.  Refinement of robot motor skills through reinforcement learning , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[25]  M. Kawato,et al.  Hierarchical neural network model for voluntary movement with application to robotics , 1988, IEEE Control Systems Magazine.

[26]  Moshe Kam,et al.  Neuromorphic architectures for fast adaptive robot control , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[27]  Farzad Pourboghrat Neuromorphic controllers , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[28]  Mitsuo Kawato,et al.  Repetitively structured cascade neural network model which generates an optimal arm trajectory , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[29]  Anuradha M. Annaswamy,et al.  Stable Adaptive Systems , 1989 .

[30]  A. Guez,et al.  Neuromorphic adaptive control , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[31]  J. Franklin Input space representation for refinement learning control , 1989, Proceedings. IEEE International Symposium on Intelligent Control 1989.

[32]  Geoffrey E. Hinton Connectionist Learning Procedures , 1989, Artif. Intell..

[33]  M. Niranjan,et al.  Generalising the nodes of the error propagation network , 1989, International 1989 Joint Conference on Neural Networks.

[34]  Kumpati S. Narendra,et al.  Adaptive identification and control of dynamical systems using neural networks , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[35]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory, Third Edition , 1989, Springer Series in Information Sciences.

[36]  L. G. Kraft,et al.  Comparison of convergence properties of CMAC neural networks and traditional adaptive controllers , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[37]  Geoffrey E. Hinton 20 – CONNECTIONIST LEARNING PROCEDURES1 , 1990 .

[38]  Kumpati S. Narendra,et al.  Adaptive control using neural networks , 1990 .

[39]  Oliver G. Selfridge,et al.  Some new directions for adaptive control theory in robotics , 1990 .