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Acrobot is a kind of under-actuated two-link rods robot. The control methods of Acrobot are complicated, but doing research on it is valuable to the applications and the study of nonlinear systems. Balance control and swing-up control are two main control areas of Acrobot. This paper centers on its balance control and combines the advantages of sliding mode control and fuzzy neural network to control...
The grinding industrial production system is a typical complex nonlinear multivariable process with strongly coupling and large time delays. The up-to-the-minute research results of integrated modeling and intelligent control of grinding process are summarized. It mainly includes the fuzzy control, artificial neural network control, soft-sensor modeling and hybrid intelligent control strategy. In...
the annealing furnace temperature control system in this paper is designed to enhance anti-disturbances and improve the performance and accuracy of temperature controlling for the system. Considering the controlled object's characteristics of nonlinear, large hysteresis, time-varying namely uncertainty, the authors have used the method of wavelet function being adopted in neural network prediction...
The underwater environment poses a difficult challenge for autonomous underwater navigation. A standard problem of underwater vehicles is to maintain its position at a certain depth in order for it to perform desired operations. An effective controller is required for this purpose and hence the design of a depth controller for an Unmanned Underwater Vehicle is described in this paper. The control...
This paper presents a single input fuzzy controller (SFLC) for adjusting the duty cycle of a photovoltaic (PV) charger to extract the maximum power out of a PV panel. The algorithm is based on maximum power point tracking (MPPT) and is compared with two conventional MPPT techniques, namely the Hill climbing method and the two input fuzzy controller method. The PV system design and modeling is described...
The classical controllers algorithm is both simple and reliable, and has been applied to thousands of control loops in various industrial applications over the past 60 years (89%-90% of applications). This paper presents the neuro-fuzzy controller incorporates fuzzy logic algorithm with a five-layer artificial neural network (ANN) structure. The conventional controller is replaced by Adaptive Neuro-Fuzzy...
The Photovoltaic (PV) energy is one of the renewable energies that attracts attention of researchers in the recent decades. Since the conversion efficiency of PV arrays is very low, it requires maximum power point tracking (MPPT) control techniques to extract the maximum available power from PV arrays. In this paper, two categories of MPPT algorithms, namely indirect and direct methods are discussed...
Electro-hydraulic servo valve is a kind of equipment that always leads to some faults in the automatic gauge control system of the strip rolling mill. In order to diagnose faults of electro-hydraulic servo valves better, a three layers BP neural network was designed based on collection and analysis on the characteristic information of some their faults before. Simulation results proved that the BP...
Uncertainty is an inevitable problem in real-time industrial control systems and, to handle this problem and the additional one of possible variations in the parameters of the system, the use of sliding mode control theory-based approaches is frequently suggested. In this paper, instead of using a conventional sliding mode controller, a sliding mode control theory-based learning algorithm is proposed...
In this paper, a three-dimensional (3D) self-adaptive region fuzzy guidance law based on radial basis function (RBF) neural networks for some attacking UAV was proposed. Firstly, 3D motion equations for pursuit-evasion of UAV and maneuvering target are given. Secondly, the proposed method was applied to decreasing the miss distance distance, which is mostly arisen from the fixed navigation rates of...
The AGC of reheat interconnected two area power systems are characterized by non-linearity and uncertainty. A hybrid neural network and fuzzy control is proposed for automatic generation control in power systems. Recurrent neural network is employed to forecast controller and system's future output, based on the current Area Control Error (ACE) and the predicted change-of-ACE. The Control Performance...
In this paper we describe the application of genetic algorithms for optimal type-2 fuzzy system design. We illustrate the approach with two cases, one of designing optimal neural networks and the other of fuzzy control. Simulation results show the feasibility of the proposed approach of using hierarchical genetic algorithms for designing type-2 fuzzy systems.
Knowledge manufacturing system has the ability of modifying dynamically manufacturing mode rapidly when production environment factors change. It is essential to evaluate the matching degree of established manufacturing mode and changed production environment factors. In this paper, a matching decision model for self-adaptability of knowledge manufacturing system based on the fuzzy neural network...
This paper presents the performances of different type fuzzy logic controllers which are adaptive neural-network based fuzzy logic (ANNFL) controller, hierarchical adaptive neural-network based fuzzy logic (HANNFL) controller and adaptive neural-network based interval type2 fuzzy logic (ANNIT2FL) controller. ANNFL, HANNFL and ANNIT2FL controllers are applied on flexible manipulator for both position...
In this work is presented a design and implementation of an intelligent controller applied to dynamic systems. The main objective is to design a management intelligent system that acts in the controllers of Direct current machine (DC) and thermo-eletric control systems drives. The purpose is to evaluate the performance of algorithms for intelligent control that integrate DC and thermal actuators to...
The global asymptotic stability of fuzzy cellular neural networks with unbounded time-varying delays and Lipschitz continuous activation functions is investigated in this brief. Based on the concept of comparison, some novel sufficient conditions for the globally asymptotic stability of equilibria are given.
Wind energy systems have been emerging as a highly significant solution to the problem of limited traditional energy sources. In this paper, control methodologies adapted to wind energy systems are topically reviewed. oHard computing or control techniques such as proportional-integral-derivative (PID), optimal, nonlinear, adaptive and robust and soft computing or control techniques such as neural...
A novel approach is promoted for fuzzy neural ship controllers. A RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors. Utilizing the designed network to substitute the conventional fuzzy inference, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network...
The paper present a new learning algorithm for fuzzy neural network (FNN) systems to approximate unknown nonlinear continuous functions. The concept of exponential fast terminal sliding mode is introduced into the learning algorithm to improve approximation ability. The training algorithm guarantees that the approximation is stable and converges to the optimal approximation function with improved...
In the view of the worsening congestion situation of freeway and related urban expressway. Based on fuzzy metering and neural network theories. A fuzzy control method which chooses d-value of mainline traffic state and expected state and ramp traffic state as input variables, ramp metering rate as output variable was raised. accordingly a ramp metering algorithm was put forward to establish a five-layer...
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