H∞ Control Design Using Dynamic Neural Networks
Neural Processing Letters
A Simplified Neural Network for Linear Matrix Inequality Problems
Neural Processing Letters
Adaptive SDRE based nonlinear sensorless speed control for PMSM drives
ACC'09 Proceedings of the 2009 conference on American Control Conference
Switching fuzzy dynamic output feedback H∞ control for nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reliable robust controller design for nonlinear state-delayed systems based on neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A hierarchical optimization neural network for large-scale dynamic systems
Automatica (Journal of IFAC)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Gradient-type Hopfield networks have been widely used in optimization problems solving. The paper presents a novel application by developing a matrix oriented gradient approach to solve a class of linear matrix inequalities (LMIs), which are commonly encountered in the robust control system analysis and design. The solution process is parallel and distributed in neural computation. The proposed networks are proven to be stable in the large. Representative LMIs such as generalized Lyapunov matrix inequalities, simultaneous Lyapunov matrix inequalities, and algebraic Riccati matrix inequalities are considered. Several examples are provided to demonstrate the proposed results. To verify the proposed control scheme in real-time applications, a high-speed digital signal processor is used to emulate the neural-net-based control scheme