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In this paper, we apply an adaptive control algorithm to a nonlinear multivariable process. Such controller is based on the multiple models approach. As the design of the control law requires the knowledge of the dynamical model of the system, we deal firstly with the identification of the system parameters using the recursive least squares and the retro propagation of the gradient algorithms. Then,...
In this paper dynamic surface control (DSC) technique is combined with neural network based adaptive control design framework to design the longitudinal dynamics controller for a nonlinear generic hypersonic air vehicle (HSAV). Detailed stability analysis is carried out to prove the uniform ultimate boundedness of all the signals in the closed loop system. The effectiveness of the proposed strategy...
In this paper, an adaptive neural controller design procedure for a class of nonlinear systems with incompletely known and time varying nonlinearities is presented. The unknown process dynamics is on-line identified using feedforward neural networks based estimators. Both the form of the controller and the adaptation laws of neural networks weights are derived from a Lyapunov stability property of...
Nowadays Congestion control problem of the intermediate nodes in the Internet has received extensively attention in control community. In this paper, a novel intelligent PID (Proportional-Integral-Differential) controller based on neural networks (PIDNN) for the problem of AQM is presented. Considering a previously developed nonlinear dynamic model of TCP/AQM system and the queue management mechanism...
This paper presents output feedback neural control for helicopters in single-channel modes of operation with dynamics in single-input single-output (SISO) nonlinear nonaffine form. A constructive approach for adaptive NN control design with guaranteed stability is proposed based on the use of the Implicit Function Theorem, Mean Value Theorem, and high gain observer. It is shown that the output tracking...
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