IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Neural methods for antenna array signal processing: a review
Signal Processing
Improving generalization capabilities of dynamic neural networks
Neural Computation
A real-time neuro-adaptive controller with guaranteed stability
Applied Soft Computing
A direct adaptive neural command controller design for an unstable helicopter
Engineering Applications of Artificial Intelligence
Adaptive Tracking Control of Nonlinear Systems Using Neural Networks
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
Adaptive neural model-based fault tolerant control for multi-variable processes
Engineering Applications of Artificial Intelligence
IEEE Transactions on Neural Networks
Takagi-Sugeno fuzzy model based indirect adaptive fuzzy observer and controller design
Information Sciences: an International Journal
Dynamic structure neural network for stable adaptive control of nonlinear systems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
Automatica (Journal of IFAC)
Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks
Automatica (Journal of IFAC)
Technical Communique: A combined PID/adaptive controller for a class of nonlinear systems
Automatica (Journal of IFAC)
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An adaptive control technique, using dynamic structure Gaussian radial basis function neural networks, that grow in time according to the location of the system's state in space is presented for the affine class of nonlinear systems having unknown or partially known dynamics. The method results in a network that is “economic” in terms of network size, for cases where the state spans only a small subset of state space, by utilizing less basis functions than would have been the case if basis functions were centered on discrete locations covering the whole, relevant region of state space. Additionally, the system is augmented with sliding control so as to ensure global stability if and when the state moves outside the region of state space spanned by the basis functions, and to ensure robustness to disturbances that arise due to the network inherent approximation errors and to the fact that for limiting the network size, a minimal number of basis functions are actually being used. Adaptation laws and sliding control gains that ensure system stability in a Lyapunov sense are presented, together with techniques for determining which basis functions are to form part of the network structure. The effectiveness of the method is demonstrated by experiment simulations