Abstract
Investigates direct adaptive neural network control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neurocontrol area, theoretical issues concerning the existing backpropagation-based control schemes are still being worked out. The major contribution of this paper is proposing a variable-index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neurocontrol schemes. These new schemes possess inherent robustness to system model uncertainty, which must be avoided in order to satisfy any matching condition. To demonstrate the feasibility of the proposed learning algorithms and the direct adaptive neurocontrol schemes, intensive computer simulations were conducted, based on different nonlinear systems and functions.
Published Version
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