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Feedback linearization technique is a relative mature design technique in nonlinear control system theory, the backstepping technique is a controller design technique which recently gets much favor. The feedback linearization based on backstepping technique is the combination of the two techniques mentioned, it uses backstepping design process, designs a sequence of ??virtual?? systems of relative...
This paper presents a method to compute sub-optimal control strategies of discrete time large scale nonlinear systems by neural networks. The method is based on the principle of decomposition of the global system into interconnected subsystems for which we consider that non-linearities are located in the interconnection terms. Then, a mixed method is considered to coordinate between different subsystems...
In advance of network communication society by the Internet, the way how to send data fast with a little loss has become an important transportation problem. A generalized maximum flow algorithm provides the best solution to the transportation problem of determining which route is appropriated to exchange data. Therefore, the importance of the maximum flow algorithm continues to grow. In this paper,...
In this paper, adaptive neural network (NN) control is investigated for a class of block triangular multi-inputmulti-output (MIMO) nonlinear discrete-time systems with each subsystem in pure-feedback form with unknown control directions. Each subsystem is transformed into a predictor form such that the noncausal problem can be avoided in the control design. By exploring the properties of block triangular...
This paper presents a neural network based direct adaptive control scheme for a class of affine nonlinear systems which are exactly input-output linearizable by nonlinear state feedback. When the system dynamics are completely unknown, the control input comprises two terms. One is an adaptive feedback linearization term and the other one is a sliding mode term. The neural networks weight update laws...
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