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In this paper, a hybrid control strategy, variable universe adaptive fuzzy sliding mode control, is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as...
Nowadays congestion control problem of the intermediate nodes in the Internet has received extensively attention in networking and control community. In this paper, a novel adaptive PID (Proportional-Integral-Differential) controller based on neural networks for the problem of AQM with ECN marks is presented. Considering a previously developed nonlinear dynamic model of TCP/AQM system and the queue...
A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, which are coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global...
PID controllers are popular in industrial applications, as they are easy to install and reasonably robust. However, for highly nonlinear systems, the performance of PID controllers can deteriorate quite fast. It is necessary to develop nonlinear PID controllers for controlling nonlinear processes. An approach to design these controllers is to switch between several linear PID controllers using fuzzy...
This paper investigates the failure prediction problem for multivariable and multi-failure-mode complex systems based on performance degradation. In our treatment, neural network is employed to simulate system performance degradation and to predict the health states of functional modules. BP network and Learning Vector Quantization (LVQ) network are used simultaneously to simulate and to predict future...
This paper presents a computational model of neural network for both spatial and temporal weights, and a unified adaptation scheme based on two biologically plausible learning rules-Hebbian rule and lateral inhibition is proposed. This model is applied to color video environment to develop a set of complete spatiotemporal weights simulating receptive field of simple cell in primary visual cortex,...
A direct neural interface system (NIS) promises to provide communication and independence to persons with paralysis by harnessing intact motor cortical signals to enable controlling prosthetic devices. An intracortical NIS aims to achieve this by sensing extracellular neuronal signals through chronically implanted microelectrodes and by decoding the spiking activity of neurons into prosthetic control...
A nonlinear multivariable adaptive decoupling PID control strategy based on multiple models and neural network is proposed for a class of uncertain discrete time nonlinear dynamical systems. The adaptive decoupling PID controller is composed of a linear adaptive PID decoupling controller, a neural network nonlinear adaptive PID decoupling controller and a switch mechanism. The PID parameters of such...
This paper presents Model predictive control (MPC) of nonlinear hybrid system based on neural network (NN) optimization. Multiple model method is used to modeling of nonlinear hybrid system and these models are combined using Bayes theorem. NN optimization combined gradient NN with recurrent NN is proposed to solve optimization problem of each sample time in MPC. An example of benchmark three spherical...
This paper presents the design of a neural network-based feedback linearisation (NARMA-L2) slip controller for an anti-lock braking system (ABS). The dynamics of the electro-mechanical based braking system are incorporated in the ABS model and thus a slip controller is developed to minimise the braking distance. The proposed controller is compared with an optimally-tuned PID controller. Simulation...
In order to deal with the control problems of nonlinearity and difficulty of establishing an exact model of a micro turbine engine, a control algorithm based on neural network is proposed. The RBF neural network can identify the output value online, which is used by the single neuron controller to adjust its parameters based on a gradient algorithm. The simulation and rig test experiments show that,...
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