Catálogo de publicaciones - libros

In this paper, an adaptive fuzzy-neural network control problem is discussed for some uncertain MIMO nonlinear systemswith the block-triangular structure. The fuzzy-neural networks are utilized to approximate the virtual controllers, and by using backstepping technique, the direct adaptive FNN control scheme is developed. The proposed control method guarantees the closed-loop signals to be semiglobally uniformly ultimately bounded.

A newly developed non-symmetric sinusoidal membership function (NSSMF) is constructed. An improved fuzzy neural network controller using NSSMF is constructed to control the speed of ultrasonic motors. A dynamic algorithm with adaptive learning rate is used to train FNNC online. The global convergence of the FNNC systems could be guaranteed by adjusting the adaptive learning rate. The validity of the proposed scheme is examined by simulated experiments.

The special scenario of the topology in the cellular Ad Hoc networks was analyzed and a novel cross layer power control game algorithm based on Neural Fuzzy Connection Admission Controller (NFCAC) was proposed in this paper. NFCAC has been successfully applied in the control-related problems of neural networks. However, there is no discussion about the power control game algorithm and location recognition based on NFCAC in cellular Ad Hoc networks. The proposed algorithm integrated the attributes both of NFCAC and the topology space in special scenario. The topology and the power consumption of each node can all be optimized due to the minimum link occupation with the help of the algorithm. Simulation results show that the novel algorithm can give more power control guarantee to cellular Ad Hoc networks in the variable node loads and transmitting powers, and make the node more stable to support multi-hops at the same time.

This paper proposes a model predictive control scheme with recurrent fuzzy neural network (RFNN) by using the temperature of the drying process for grain dryers. In this scheme, there are two RFNNs and two PI controllers. One RFNN with feedforeward and feedback connections of grain layer history position states predicts outlet moisture content (MPRFNN), and the other predicts the discharge rate of the dryer (RPRFNN). One PI controller adjusts the objective of the discharge rate by using MPRFNN, and the other adjusts the given frequency of the discharge motor to control the discharge rate of the grain dryer to reach its objective by using RPRFNN. The experiment is carried out by applying the proposed scheme on the control of a gain dryer with four stages to confirm its effectiveness.

A GA-based adaptive fuzzy-neural controller for a class of multi-input multi-output nonlinear systems, such as robotic systems, is developed for using observers to estimate time derivatives of the system outputs. The weighting parameters of the fuzzy-neural controller are tuned on-line via a genetic algorithm (GA). For the purpose of on-line tuning the weighting parameters of the fuzzy-neural controller, a Lyapunov-based fitness function of the GA is obtained. Besides, stability of the closed-loop system is proven by using strictly-positive-real (SPR) Lyapunov theory. The proposed overall scheme guarantees that all signals involved are bounded and the outputs of the closed-loop system track the desired output trajectories. Finally, simulation results are provided to demonstrate robustness and applicability of the proposed method.

A new method for active noise control is proposed and experimentally demonstrated. The method is based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which is introduced to overcome nonlinearity inherent in active noise control. A new algorithm referred to as Filtered-X ANFIS algorithm suitable for active noise control is proposed. Real-time experiment of Filtered-X ANFIS is performed using floating point Texas Instruments C6701 DSP. In contrast to previous work on ANC using computational intelligence approaches which concentrate on single channel and off-line adaptation, this research addresses multichannel and employs online adaptation, which is feasible due to the computing power of the DSP.

In this paper, a robust neural networks adaptive control scheme is proposed for the stabilization of uncertain linear systems with time-varying delay and bounded perturbations. The uncertainty is assumed to be unknown continuous function without norm-bounded restriction. The perturbation is sector-bounded. Combined with liner matrix inequality method, neural networks and adaptive control, the control scheme ensures the stability of the close-loop system for any admissible uncertainty.

Multiple controllers based on multiple radial based function neural network(RBFNN) models are used to control a nonlinear system to trace a set-point. Considering the nonlinearity of the system, when the set-point value is time variant, a controller based on a fixed structure RBFNN can not give a good control performance. A switching controller which switches among different controller based on different RBFNN is used to adapt the varing set-point value and improve the output reponse and control performance of the nonlinear system.

In this paper, a novel adaptive tracking control strategy is established for general non-Gaussian stochastic systems based on two-step neural network models. The objective is to control the conditional PDF of the system output to follow a given target function by using dynamic neural network models. B-spline neural networks are used to model the dynamic output probability density functions (PDFs), then the concerned problem is transferred into the tracking of given weights corresponding to the desired PDF. The dynamic neural networks with undetermined parameters are employed to identify the nonlinear relationships between the control input and the weights. To achieve control objective, an adaptive state feedback controller is given to estimate the unknown parameters and control the nonlinear dynamics.

An scheme of composite sliding control is proposed for a class of uncertainty nonlinear system, which is based on fuzzy neural networks (FNN) and simple neural networks (SNN). The SNN is uniquely determined by the design of the global integral sliding mode surface, the output of which replaces the corrective control, and FNN is applied to mimic the equivalent control. In this scheme, the bounds of the uncertainties and the extern disturbance are not required to be known in advance, and the stability of systems is analyzed based on Lyapunov function. Simulation results are given to demonstrate the effectiveness of this scheme.