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The paper proposes a general approach to the development of neural network algorithms for determining the values of signal parameters in radio-electronic, telecommunication, and information systems from the functional level to the level of nano-elements; the approach is based on the principles of data, image, and signal recognition. Implementation of this general approach is illustrated by a neural...
We propose herein a data-driven dead-zone (DZ) compensation strategy using a model-free Virtual Reference Feedback Tuning (VRFT) approach. The VRFT tuning scheme is accommodated for two controller structures: the first one which explicitly includes a model of the DZ inverse to be identified and the second one which uses a Neural Network (NN) to model the controller to be identified. The main question...
As the penetration of renewable energy increasing in power systems, the contribution of VSC-HVDC for system stability is highly expected. Due to the decoupling modulating of active and reactive power output, the influence of HVDC on the inter-area oscillation is widely studied. This paper proposes a new ANN (Artificial Neural Network) based control design scheme for VSC-HVDC systems to damp inter-area...
The work is devoted to solving the problem of monitoring and diagnostics of the complex technical and technological objects state by data-based models application. As an example of these models use, the process of electrochemical dimensional processing of high-strength materials was considered. The models of electrochemical processing based on neural networks are developed. The option for introduction...
In this paper, a sensorless admittance control scheme is developed for robot manipulators in the presence of input saturation by employing neural networks. To deal with system uncertainties, the radial basis function neural network (RBFNN) is integrated into control design. In order to deal with input saturation, a compensator is applied to handle this problem. To interact with the environment, admittance...
The paper is devoted to consider problems of project management in conditions of internal and external uncertainty in project state assessment. It is shown, that uncertainty of project state assessments influences negatively on the project and on the organized system, in which projects realize. The mechanisms of formalized project state assessment with means of artificial neural networks are proposed...
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...
The robust adaptive control of uncertain system with unknown time-varying control coefficient is discussed. A novel output sampled control scheme based on characteristic model with neural network estimator is proposed. The design of the control scheme includes characteristic modeling, estimation for the characteristic parameters, and characteristic model-based adaptive control. The estimation method...
We apply soft control method on an opinion dynamic model, the weighted DeGroot model, to change the convergent opinion value x. The interaction network plays an important role in the dynamics of system, and the soft control performance (Δx, the difference between the new convergent opinion value x and the original convergent opinion value x). In this paper, we define a new network feature Ω, called...
We present the control system synthesis for the multilink redundant manipulator. Our control system is based on the unique algorithm that includes the novel hybrid method for solving the inverse kinematics problem. This method combines ANFIS-network and iterative refinement. As a result, the control system has high integrative capabilities and is easy to modify for another construction. The manipulator...
A fundamental problem to be resolved to achieve carrier grade functioning of an SDN network is the controller placement problem. The problem has been traditionally limited to optimally assigning switches to static physical controllers and placing those controllers over a network topology. However, virtualized controllers in place of physical ones could largely increase the dynamism in network control...
In this paper, a new RBF based sliding mode controller is proposed for the joint trajectory tracking of robotic manipulators with uncertainties and disturbances. A RBF neural network is employed to approximate the nonlinear uncertainties in the mode, adaptive laws of the parameters are established, and the approximation error is compensated by designing a sliding mode controller, in which a generalized...
The approach to the solution of the technical diagnostics problem of a transport data transmission network (TDTN) is presented, which allows to work with different groups of diagnostic data depending on the requirements by the time the task is completed, which in turn will allow reducing the average time of realization of the network diagnosis process; takes into account the physical and logical structures...
As one of the well-known renewable energy resources, wind energy, which attracts a larger part of today's total investments, is now playing an increasingly important role around the world.
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 presents a real-time speed control for a linear tubular permanent magnet direct current motor (LTPMDCM) by using artificial neural network (ANN). Firstly, a novel form of LTPMDCM prototype has been developed for linear motions. Then, a low cost motor drive has designed for expected rapid response and precise position control. Software developed in MATLAB environment is used for speed control...
Electric cars are eco-friendly as they run on electrically powered engines and they also promote green environment. This paper describes the fuzzy cerebellar based articulation controller to regulate the speed of the switched reluctance motor (SRM) drive. The proposed controller comprises two parts — a compensating controller and a fuzzy cerebellar model articulation controller (CMAC). The fuzzy CMAC...
We present a novel method for training (evolving) fully convolutional neural networks (CNNs) for deformable object manipulation. Instead of using a weight update rule, we evolve an ensemble of compositional pattern generating networks (CPPNs) by means of a genetic algorithm (GA). These ensembles generate the convolutional kernels that comprise the CNN. This allows the GA to search for fit kernels...
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...
The techniques of adaptive passivity-based controller (APBC) and non-adaptive passivity-based controller (PBC), together with a PI controller, are applied to the level regulation of a conical tank. A comparative study considering the controller designs and experimental results is presented. A single APBC and PBC are designed for the whole operational range of the plant; whereas a PI controller with...
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