Based on fully tuned RBF neural networks and backstepping control techniques, a novel nonlinear adaptive control scheme is proposed for missile control systems with a general set of uncertainties. The effect of the uncertainties is synthesized one term in the design procedure. Then RBF neural networks are used to eliminate its effect. The nonlinear adaptive controller is designed using backstepping control techniques. The control problem is resolved while the control coefficient matrix is unknown. The adaptive tuning rules for updating all of the parameters of the fully tuned RBF neural networks are firstly derived by the Lyapunov stability theorem. Finally, nonlinear 6-DOF numerical simulation results for a BTT missile model are presented to demonstrate the effectiveness of the proposed method.
KEYWORDS: Signal generators, Data processing, Signal processing, Digital signal processing, Digital electronics, Modulation, Data storage, Analog electronics, Instrumentation control, Computer architecture
A Histogram-based On-Board ADC BIST method is presented in this paper. Compared with the classical Histogram test, this scheme reduces the BIST hardware cost greatly, because the difficulty of nonlinearity computation is reduced in our scheme. Instead, we set a threshold to justify the nonlinearity of the ADC under test.
KEYWORDS: Switching, Control systems design, Control systems, Matrices, VHF band, Radon, Instrumentation control, Information operations, Missiles, Silicon
On the base of double-sliding modes variable structure control theories, new design methods of switching surfaces and changing rules of inputs are analyzed. In a system with unmatched uncertainties, two switching surfaces can be designed separately to guarantee the stability; it is proved that the inputs can trap the state trajectory on the switching surfaces; a more simple changing rules of inputs are used, then the state trajectory will hit one of the switching surfaces alternately, so the switching frequency is reduced. Simulation of a linear model with unmatched uncertainties are given, simulation results show the effectiveness and feasibility of the proposed methods.
Based on neural network (NN) and robust control, an adaptive controller is developed for a class of MIMO nonaffine nonlinear systems. First, take the Taylor series expansion of the original system in the neighborhood of the operating trajectory. Then estimate the unknown functions of the system using NN. Finally, use robust control to overcome the affects of NN estimated error terms and high order terms of the Taylor series expansion. All the signals in the closed-loop system are proven to be uniformly ultimately bounded (UUB) and the mean square and L_infinite tracking error bounds are given in this paper. The simulation results show the effectiveness of the proposed approach.
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