Title :
Neural networks and feedback linearization
Author :
Hassibi, Khosrow M. ; Loparo, Kenneth A.
Author_Institution :
Case Western Reserve Univ., Cleveland, OH, USA
Abstract :
The authors propose a general method for learning of the input-output feedback linearization (IOFL) laws for the class of nonlinear systems described by F.-C. Chen (1990). The direct method previously described by the author (1991) is used with some modifications in the implementation. Three general assumptions required for successful implementation of the method are given. The objective was to learn a controller using a high-order three-layer network such that the resulting closed-loop system behaves similarly to a linear reference model. The IOFL problem is classified into three cases, and the required assumptions to learn the feedback for each case are given. In all cases, the feedback linearizable control law is learned directly from the error between the closed-loop system and the reference model outputs
Keywords :
closed loop systems; feedback; learning systems; linearisation techniques; neural nets; nonlinear systems; closed-loop system; high-order three-layer network; input-output feedback linearization; learning systems; neural networks; nonlinear systems; reference model outputs; Linear feedback control systems; Linear systems; Manipulator dynamics; Neural networks; Neurofeedback; Nonlinear dynamical systems; Nonlinear systems; Output feedback; State feedback; Vectors;
Conference_Titel :
Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on
Conference_Location :
Charlottesville, VA
Print_ISBN :
0-7803-0233-8
DOI :
10.1109/ICSMC.1991.169930
