Output feedback control of nonlinear systems using RBF neural networks

@article{Seshagiri2000OutputFC,
  title={Output feedback control of nonlinear systems using RBF neural networks},
  author={S. Seshagiri and H. Khalil},
  journal={IEEE transactions on neural networks},
  year={2000},
  volume={11 1},
  pages={
          69-79
        }
}
  • S. Seshagiri, H. Khalil
  • Published 2000
  • Mathematics, Computer Science, Medicine
  • IEEE transactions on neural networks
An adaptive output feedback control scheme for the output tracking of a class of continuous-time nonlinear plants is presented. An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design. The method uses parameter projection, control saturation, and a high-gain observer to achieve semi-global uniform ultimate boundedness. The effectiveness of the proposed method is demonstrated through simulations. The… Expand
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  • F. Da, S. Fei, X. Dai
  • Computer Science
  • Proceedings of the 2005, American Control Conference, 2005.
  • 2005
TLDR
An adaptive output feedback control scheme is proposed for the output tracking of a class of nonlinear systems represented by input-output models and using Lyapunov's stability theory, the global stability of the system is proven. Expand
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An adaptive output feedback controller for a class of uncertain stochastic nonlinear systems that combines together parameter projection, control saturation, and high-gain observers is presented. Expand
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  • Y. Niu, Xingyu Wang, Chengtao Hu
  • Mathematics
  • Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527)
  • 2002
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References

SHOWING 1-10 OF 19 REFERENCES
Stable adaptive neural control scheme for nonlinear systems
TLDR
A design methodology is developed that expands the class of nonlinear systems that adaptive neural control schemes can be applied to and relaxes some of the restrictive assumptions that are usually made. Expand
Stable control of nonlinear systems using neural networks
A neural-network-based direct control architecture is presented that achieves output tracking for a class of continuous-time nonlinear plants, for which the nonlinearities are unknown. The controllerExpand
Adaptive control of a class of nonlinear discrete-time systems using neural networks
TLDR
The result says that, for any bounded initial conditions of the plant, if the neural network model contains enough number of nonlinear hidden neurons and if the initial guess of the network weights is sufficiently close to the correct weights, then the tracking error between the plant output and the reference command will converge to a bounded ball, whose size is determined by a dead-zone nonlinearity. Expand
Stable Direct Adaptive Control of a Class of Discrete Time Nonlinear Systems
Abstract A direct adaptive control scheme is presented for a class of discrete-time nonlinear systems. We show that if the plant has exponentially stable zero-dynamics and appropriate basis functionsExpand
Gaussian Networks for Direct Adaptive Control
A direct adaptive tracking control architecture is proposed and evaluated for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty inExpand
A robust neural adaptive control scheme
A direct nonlinear adaptive controller, to solve the regulation problem for unknown dynamical systems that are modeled by recurrent neural networks is discussed. The behaviour of the closed loopExpand
Adaptive output feedback control of nonlinear feedback linearizable systems
TLDR
It is shown that in the absence of persistence of excitation the tracking error can be made as small as desired by increasing the observer and parameter adaptation gains, and an exponentially stable adaptive observer is created for feedback linearizable non-linear systems. Expand
Robust adaptive output feedback control of nonlinear systems without persistence of excitation
TLDR
This paper proves tracking error convergence without persistence of excitation, and shows that the adaptive controller is robust with respect to sufficiently small bounded disturbance, and adds a robustifying control component to show that the controllers is robust for a wide class of, not-necessarily-small, bounded disturbance. Expand
Modelling, Identification and Stable Adaptive Control of Continuous-Time Nonlinear Dynamical Systems Using Neural Networks
TLDR
A crucial characteristic of the methods and formulations developed in this paper is the generality of the results which allows their application to various neural network models as well as other approximators. Expand
Dynamic structure neural networks for stable adaptive control of nonlinear systems
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 theExpand
...
1
2
...