Title :
New developments in neurocontrol
Author :
Lewis, F.L. ; Parisini, Thomas
Author_Institution :
Autom. & Robotics Res. Inst., Texas Univ., Arlington, TX, USA
Abstract :
The complexity of modern day systems is increasing and performance requirements for industrial and military systems are becoming more stringent. With new results based on nonlinear stability theory, neural network (NN) based controllers are now able to provide guaranteed closed-loop stability, performance, and robustness for such complex dynamical systems. This paper presents a family of NN controllers developed for robotic systems, force control, backstepping control of industrial motors, friction compensation, deadzone compensation of actuators, etc. Some high-level NN control architectures are discussed, including reinforcement techniques and optimal design
Keywords :
closed loop systems; compensation; feedback; force control; learning (artificial intelligence); machine control; neurocontrollers; robots; stability; actuators; backstepping control; closed loop systems; deadzone compensation; feedback; force control; friction compensation; industrial motors; neural network; neurocontrol; optimal control; reinforcement learning; robotic systems; robustness; Control systems; Defense industry; Electrical equipment industry; Force control; Neural networks; Nonlinear control systems; Robot control; Robust control; Robust stability; Service robots;
Conference_Titel :
Control Applications, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location :
Trieste
Print_ISBN :
0-7803-4104-X
DOI :
10.1109/CCA.1998.728250
