PROJECT TITLE :

Universal Neural Network Control of MIMO Uncertain Nonlinear Systems

ABSTRACT:

During this brief, an eternal tracking management law is proposed for a class of high-order multi-input–multi-output uncertain nonlinear dynamic systems with external disturbance and unknown varying control direction matrix. The proposed controller consists of high-gain feedback, Nussbaum gain matrix selector, on-line approximator (OLA) model and a robust term. The OLA model is represented by a 2-layer neural network. The continuousness of the management signal is absolute to relax the necessity for the actuator bandwidth and avoid the incurred chattering effect. Asymptotic tracking performance is achieved theoretically by customary Lyapunov analysis. The control feasibility is also verified in simulation environment.


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