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By introducing the approximators, an indirect adaptive control method is presented for the control of a class of nonlinear systems. The proposed scheme can overcome the drawbacks of existing adaptive nonlinear control techniques which can only control minimum phase nonlinear systems and may easily result in a large control effort. The stability of closed-loop system was proved, and the convergence...
An adaptive control scheme is proposed for a kind of time-varying nonlinear systems. In the scheme the nonlinear systems are divided into a finite number of subsystems, which are in strict feedback and interpolated by functions of an exogenous scheduling variable. The adaptive method yields stability of all signals in the closed-loop. The results of simulation show that the controller converges very...
An adaptive neural network control strategy for a class of nonlinear system is proposed, which combines the technique in generalized predictive control theory and the gradient descent rule to accelerate learning and improve convergence with neural networkpsilas capability of approximating to nonlinear function, Taking the neural network as a model of the system, control signals are directly obtained...
An application of TNN on the damage detection of steel bridge structures is presented. The issues relating to the design of network and learning algorithm are addressed and network architectures have been developed with reference to trussed bridge structures. The training patterns are generated for multiple damaged zones in a structure. The results of simulation show that the algorithm is suitable...
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