Neural network based adaptive control of nonlinear plants using random search optimization algorithmsThis paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.
Document ID
19930071676
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Boussalis, Dhemetrios (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)