Abstract
Adaptive control techniques can achieve impressive control results for systems which can be accurately modeled or estimated. This paper addresses the case of a nonlinear system for which no model exists, and for which the only available data are a set of inputs and measured state outputs. A back-propagation neural network is used to take the place of the system model in a standard adaptive control strategy. Computer simulation is presented which shows the success of this strategy for a discrete-time single-input, single-output ( SISO) nonlinear system. Results can be extended to the multiple-input, multiple-output ( MIMO) case.
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