Adaptive control using neural networks
Neural networks for control
Self-scheduled H∞ control of linear parameter-varying systems: a design example
Automatica (Journal of IFAC)
On the Kalman-Yakubovich-Popov lemma
Systems & Control Letters
Induced l2 and generalized H2 filtering for systems with repeated scalar nonlinearities
IEEE Transactions on Signal Processing
Brief New results for analysis of systems with repeated nonlinearities
Automatica (Journal of IFAC)
A new synthesis approach for feedback neural networks based on the perceptron training algorithm
IEEE Transactions on Neural Networks
Neural network-based control design: an LMI approach
IEEE Transactions on Neural Networks
Robust backstepping control of induction motors using neural networks
IEEE Transactions on Neural Networks
Robust quasi-LPV control based on neural state-space models
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Robust and adaptive backstepping control for nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
Smooth function approximation using neural networks
IEEE Transactions on Neural Networks
An Improved Dynamic Neurocontroller Based on Christoffel Symbols
IEEE Transactions on Neural Networks
Novel Neural Network Adaptive Control Architecture With Guaranteed Transient Performance
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Output Feedback Stabilization for Time-Delay Nonlinear Interconnected Systems Using Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neurodynamic Programming and Zero-Sum Games for Constrained Control Systems
IEEE Transactions on Neural Networks
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The advantages brought about by using classical linear control theory in conjunction with neural approximators have long been recognized in the literature. In particular, using linear controllers to obtain the starting neural control design has been shown to be a key step for the successful development and implementation of adaptive-critic neural controllers. Despite their adaptive capabilities, neural controllers are often criticized for not providing the same performance and stability guarantees as classical linear designs. Therefore, this paper develops an algebraic synthesis procedure for designing dynamic output-feedback neural controllers that are closed-loop stable and meet the same performance objectives as any classical linear design. The performance synthesis problem is addressed by deriving implicit model-following algebraic relationships between model matrices, obtained from the classical design, and the neural control parameters. Additional linear matrix inequalities (LMIs) conditions for closed-loop exponential stability of the neural controller are derived using existing integral quadratic constraints (IQCs) for operators with repeated slope-restricted nonlinearities. The approach is demonstrated by designing a recurrent neural network controller for a highly maneuverable tailfin-controlled missile that meets multiple design objectives, including pole placement for transient tuning, H∞and H2 performance in the presence of parameter uncertainty, and command-input tracking.