Constrained Control of a Class of Uncertain Nonlinear MIMO Systems Using Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A Sequential Learning Algorithm for RBF Networks with Application to Ship Inverse Control
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Output feedback control for discrete-time nonlinear systems and its applications
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Nonlinear internal model control based on transformed fuzzy hyperbolic model
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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A novel neural approximate inverse control is proposed for general unknown single-input-single-output (SISO) and multi-input-multi-output (MIMO) nonlinear discrete dynamical systems. Based on an innovative input/output (I/O) approximation of neural network nonlinear models, the neural inverse control law can be derived directly and its implementation for an unknown process is straightforward. Only a general identification technique is involved in both model development and control design without extra training (online or offline) for the neural nonlinear inverse controller. With less approximation made on controller development, the control will be more robust to large variations in the operating region. The robustness of the stability and the performance of a closed-loop system can be rigorously established even if the nonlinear plant is of not well defined relative degree. Extensive simulations demonstrate the performance of the proposed neural inverse control.