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In this paper, we apply outer synchronization to identify unknown parameters existing in the node dynamics between two interacted networks. According to the coupling and interacted matrices, we design the corresponding adaptive control schemes and updating laws to achieve the outer synchronization. By the Lyapunov functional theory, we derive two theorems of the appearance of outer synchronization...
This paper investigates the attitude control problem of re-entry Reuseable Launch Vehicles (RLV) with unknown nonlinear dynamics. An adaptive RBF neural network is employed to approximate the unknown functions, and neural network structure with a neural controller and a saturation compensator are designed to reduce the effects of the approximation error. In addition, to optimize the initial parameters...
This paper researches identification and learning from adaptive neural network(NN) control of uncertain rigid-link electrically-driven (RLED) robot manipulators. By using input-to-state stability(ISS) adaptive NN controller and the small gain theorem, the closed-loop system is divided into two connecting subsystems, which proved to be exponential stability under the persistent excitation(PE) condition...
Combined with the backstepping technique, a neural network (NN)-based adaptive tracking control scheme is proposed for the Accommodation Vessel (AV). The control objective is to steer AV following the trajectory of Floating Production Storage and Offloading (FPSO) and keeping the desired distance with FPSO so that the smooth gangway operation can be achieved. In order to fulfill the control task,...
In this paper, an adaptive dynamic surface control (DSC) method is proposed for the flexible robotic system with unmodeled dynamics and time-varying output constraints. The dynamic disturbances are effectively dealt with by introducing a dynamic signal. The unknown continuous functions are approximated by using radial basis function neural networks (RBFNNs). An asymmetric time-varying barrier Lyapunov...
In this paper, a novel adaptive neural control technique for a class of switched multi-input multi-output (MIMO) uncertain nonlinear systems, capable of guaranteeing prescribed performance, is established by exploiting the classical average dwell time (ADT) method. Neural networks are used to approximate the unknown nonlinear functions. A common output error transformation for different subsystems...
Diabetes is one of the most common metabolic diseases and the statistics show that one in eleven adults has diabetes, but one in two adults with diabetes is undiagnosed, and in 2040 one in 10 adults will have diabetes. In this paper is proposed a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classifying patients with diabetes based on data sets with diabetic patients (Pima Indians...
The use of fuzzy and adaptive neuro-fuzzy technique for fault detection, classification and location is presented in this study. Ten different types of electrical faults in a transmission line were investigated. The results obtained show that a high degree of accuracy was recorded for detection, classification and location of electrical faults in the extra-high voltage line. Ongoing studies on the...
This paper investigates the adaptive state-feedback control problem of a two-stage chemical reactor system. Firstly, by using a direct radial basis function neural network (RBF NN) approximation approach, the nonlinear terms are handled under much weaker conditions. Then, with the help of a novel dynamic gain-based backstepping technique and appropriate Lyapunov-Krasovskii functionals, a smooth controller...
This paper addresses the state constrained control problem of a class of nonlinear pure-feedback systems in the presence of unknown dynamics. Minimal learning parameter technique based neural networks are used to estimate the model uncertainties, thus the amount of online updated parameters is largely reduced. Filtered signals are introduced to avoid algebraic loop problems encountered in the implementation...
In this paper, a distributed consensus-based formation control of networked nonholonomic mobile robots using neural networks (NN) in the presence of uncertain robot dynamics with event-based communication is presented. The robots communicate their location and velocity information with their neighbors, at event-based sampling instants, to drive themselves to a pre-defined desired formation by using...
The questions of increase power equipment productivity based on neural network modeling and adaptive cluster analysis are considered. As an research object a boiler units of the heat network of a typical pulp-and-paper industry enterprise, which is a large consumer of steam for technological needs, are considered. The data mining algorithm in the field of power equipment operation, on the basis of...
This paper addresses the assist-as-needed (AAN) control problem for robotic orthoses. The objective is to design a stable AAN controller with an adjustable assistance level. The controller aims to follow a desired trajectory while allowing an adjustable tracking error with low control effort to provide a freedom zone for the user. By ensuring the stability of the system and providing the freedom zone,...
In this paper, artificial neural network assisted MRAS based speed estimator is presented for the poly-phase induction motor drive. The adaptive model of conventional RF-MRAS is replaced by a neural network in order to immune the system from rotor resistance variation and for the reduction of complex mathematical computational efforts. To train the network, Levenberg-Marquardt back-propagation algorithm...
This paper presents a method to improve power factor of electrical system by automatically controlling a synchronous motor. This proposed method of controller is based on an artificial neural network (ANN) together with adaptive PI. Therefore the ANN adaptive PI controller performs adequately both rapid and slow changing load condition. Generally the performance of this proposed method was highly...
This paper focuses on adaptive neural control for nonlinear system in nonstrict feedback form in the presence of output constraint. Since the backstepping control can not be directly employed to nonstrict feedback structure during controller design. Using the variable separation method, the above obstacle has been overcome. Then, by utilizing barrier Lyapunov function, the issue of output constraint...
A network of vehicular cyber-physical systems (VCPSs) can use wireless communications to interact with each other and the surrounding environment to improve transportation safety, mobility, and sustainability. However, cloud-oriented architectures are vulnerable to cyber attacks, which may endanger passenger and pedestrian safety and privacy, and cause severe property damage. For instance, a hacker...
Granular neural networks (GNNs) process granulated data with neural networks. Class based (CB) granulation of input data considers the belongingness of each feature to the classes present in the data. Advancement in the sensor technology has produced large amount of data which is also called as stream of data. To process the stream of CB granulated data, the article proposes an adaptive neural network...
Neural networks are universal approximators that can estimate and control plant dynamics under severe process nonlinearities. In this paper, we propose a direct adaptive inverse control (DAIC) for nonlinear plants based on multilayered feed forward neural network (MFNN) using back propagation through model (BPTM) algorithm. Neural Network (NN) controller is designed directly in the feed forward loop...
In this paper an adaptive control law is designed for formation control of underactuated autonomous ground vehicles based on the leader-follower approach and kinematics equations. The follower vehicles have limited knowledge about the leader's states. The unknown term containing the velocity information of the leader is estimated using neural networks with online adaptive weight tuning laws. The decentralized...
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