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An accurate on-line measurement of important quality variables is essential for successful monitoring and controlling of chemical processes. However, these variables are usually difficult to measure on-line due to the practical limitations such as the time-delay, high cost and reliability considerations. To overcome this problem, two online soft sensors are proposed based upon a combined adaptive...
An adaptive neural network control strategy for a class of nonlinear system is proposed, which combines the technique in generalized predictive control theory and the gradient descent rule to accelerate learning and improve convergence with neural networkpsilas capability of approximating to nonlinear function, Taking the neural network as a model of the system, control signals are directly obtained...
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...
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...
Because of the inherent nonlinearity of high power amplifier, there are in-band distortion and adjacent-channel interference, which may have a negative influence on communication systems. We have to make a linearization processing to overcome them. First, this paper attempts to analyze nonlinearity distortion of HPA in a mathematic approach, and then briefly introduces the basic principle about digital...
This paper has effectively combined self-study advantage of adaptive neural network and fuzzy reasoning method of fuzzy math, resolved the problem that the fuzzy rule is difficult to be determined in the fault diagnosis for transformer insulation, confirmed the fuzzy rule and the fuzzy subject degree by using self-study function of adaptive neural network and established ANFIS model of transformer...
Linear system theory has made significant contribute to the developments of the classical control's area in the past three decades. The motivation of this paper emerges from the need to develop novel control strategies that can be applied to nonlinear dynamic systems. Furthermore, the need for an adaptive scheme emerges for dealing with time varying systems. Paper presents model reference based neural...
Aiming at a class of nonaffine nonlinear system with uncertainties, an adaptive backstepping neural controller design is presented. By applying backstepping design strategy and online approaching nonlinearity with fully tuned radial basis function (RBF) neural networks, the adaptive tuning rules are derived from the Lyapunov stability theory. A nonlinear tracking differentiator is introduced to deal...
In this article, the adaptive learning method of the radial basic function neural network is used on the variable structure sliding mode control of underwater robot to estimate and approach the upper bound of the uncertainty and disturbance induced by hydrodynamics. With this study method, the difficulties of establishment and resolving of precision dynamic model of underwater robot can be avoided...
This paper proposes a new narrowband active noise control (ANC) system where an ANFIS (adaptive network-based fuzzy inference system) is utilized as an adaptive controller. The ANFIS is a combination of a neural network and a fuzzy inference system. For the purpose of computational cost reduction, the nonlinear premise parameters in the ANFIS are fixed and only its linear consequent parameters are...
This paper presents a novel control design of shunt active power filter to compensate reactive power and reduce the unwanted harmonics. A shunt active filter is realized employing three-phase voltage source inverter (VSI) and a control circuit. The shunt active filter acts as a current source, which is connected in parallel with a nonlinear load and controlled to generate the required compensation...
The electric plant construction projects evaluation is a complex, multi-factor, multi-index decision-making problem. In order to evaluate the construction projects scientifically and accurately, this paper proposes the evaluating model based on the adaptive neuro-fuzzy inference system (ANFIS). The ANFIS avoids the fuzziness characteristic of the projects information itself and the circumstances of...
EFI engine's ECU through a fixed pre-set supply procedures to control fuel injection time, however, the pre-set data can only be reached through a large number of tests. How to attain to pre-set data efficiently and how to dynamically adjust the fuel injection time according to the traffic circumstance are currently the hot issues in Engine Electronic Control Simulation field. In this paper, aiming...
In this paper, a novel model reference adaptive control (MRAC) scheme based on neural network (NN) is proposed for servo system tracking control to achieve high-precision position control. This scheme consists of an MRAC controller and an online NN controller in velocity-loop and a traditional PID controller in position-loop. For reducing influence which arose from modeling error, unknown model dynamics,...
Adaptive noise cancellation technology is a very good signal processing technology, which can eliminate background noise effectively. In order to restrain the traditional adaptive canceller from trapping in local optimum, the improved PSO algorithm is leading into the mutation operator according to standard derivation of swarm fit value to inhibit local optimum, and design an adaptive noise canceller...
In this paper, a control strategy is proposed for gait generation for a two-legged humanoid robot. The two-legged robot is assumed as a 3-dimensional robot with 5-links. Gait generation is performed by assuming motions in the saggital and lateral planes. Dynamic model of the robot is obtained by using MATLABreg/SimMechanics Toolbox. A fuzzy logic controller (FLC) is established for gait generation...
In this study, hierarchical adaptive network based fuzzy logic controller for a single flexible link robot manipulator is designed. This study is aimed to get the end of the flexible robot manipulator to desired position and terminate vibrations on arm while it moves under the action of an external driving torque. The proposed controller has two subsystems. The fast-subsystem reduces the vibration...
In the paper, the synchronization of a class of chaotic neural networks with delay and impulse is investigated. Based on the stability analysis of impulsive differential equations with delay, the adaptive synchronization conditions of two coupled neural networks are derived. A numerical example is given to show the theoretical results.
The paper considers the problem of global adaptive tracking for a class of uncertain nonlinear systems in which the uncertainty is impossible to be parameterized. With the help of the technique of unit partition in differential topology, a result on global approximation of function using neural networks is proved. Based the result, a method of global adaptive neural network control for the uncertain...
A new adaptive nonlinear state predictor (ANSP) is presented for a class of nonlinear systems with input time-delay. High-order neural network (HONN) incorporating with a special identification model is first employed to identify the unknown nonlinear time-delay system. The predicted weight updating law of neural network is calculated based on the identification, which can be used to predict the future...
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