Papers by Keyword: Nonlinear System

Abstract: The paper studies the effect of networks with transmission delay in the feedback loop of a nonlinear networked control system (NCS). The nonlinear system is modeled by a T-S fuzzy model, and transmission delays are modeled by a finite state Markov process. The fuzzy controller’s membership functions can be different from the plant’s. The membership functions of the plant and the fuzzy controller are incorporated into the controller design. System performance is measured via an norm from disturbance to error and the controller is computed by sum of square approach. Finally we applied these results to an inverted pendulum system. Theoretical analysis and simulation results show that the control strategy studied in this paper is effective and feasible.

Abstract: In this paper, we present Kinematic analysis of a specific class of series–parallel manipulators, known as 2(6-UPS) manipulator, which composed of two modules which consist of elementary manipulators with the parallel structure of the Stewart Platform. After extracting the Kinematics Model of the hybrid manipulator, because of highly nonlinear relations between joint variables and position and orientation (location) of the end effectors in this kind of hybrid mechanism, we applied wavelet neural network with its inherent learning ability, to solve the kinematics problem. Proposed wavelet neural network (WNN) is applied to approximate the paths of mid and upper plate in different paths. Comparison the results of neural network with closed form solution (CFS) show high accurate performance of proposed WNN.

Abstract: In recent years, with the continuous improvement of production capacity, manufacturing industry restructuring and achieved great results, significantly increased the proportion of high-tech industries, some traditional industries has continued to decline. Manufacturing is the material basis of our national economy and the main industry, which largely determines the level of development of comprehensive national strength. This has brought great difficulties to the economic modeling and forecasting system. This paper presents an improved modeling and forecasting methods, recombinant methods by introducing chain data and add data growth economic indicators in an artificial neural network training, the time series data input window to solve practical engineering problems forecasts.

Abstract: This paper presents that single input single output (SISO) switched nonlinear system tracks the variation of the state error to approach the excepted values by using model reference adaptive control (MRAC) method. In order to improve the adaptive control for nonlinear systems by Using Narendra method and dividing the system into two parts: linear and nonlinear parts. The controllers are designed to guarantee that the systems are closed to the model reference system with the arbitrary switching signal. Switching systems can ensure choose the best controller so that can enhance the performance. The adaptive laws are given that are based on a class of feedback single input single output nonlinear uncertain systems which can switch feedback linear standard models. The adaptive laws are different from the classic adaptive laws, because they vary with different switching signals until the best matching one comes. Simulation results show that the proposed method is effective.

Abstract: To describe the complex nonlinear characteristics of a system accurately, a Wavelet Neural Network (WNN) identification model based on Artificial Fish Swarm (AFS) algorithm is proposed. In the identification model, AFS algorithm is introduced to optimize the parameters combination of the network for the satisfactory WNN model. The simulation shows that, the proposed method is a good nonlinear identification capability, and is feasible to identify the nonlinear system.

Abstract: The forced Duffing oscillator is investigated by time-delayed linear and nonlinear acceleration feedbacks. The method of Slowly Varying Parameter (SVP) can efficiently develop frequency response curves with stability of solutions. Appreciable reduction in peak value of response and gradual reduction in the skew-ness in frequency response curve is observed with the introduction of gain and delay. For some values of control parameters the entrainment phenomena are also observed. Appropriate choice of gain and delay parameters are made from the primary stability zone of linear stability analysis graph. The results obtained by this method are compared with numerical integration (NI) method and they are in good agreement.

Abstract: In this paper, the robust control problem of interacting nonlinear multi-input multi-output (MIMO) systems is proposed. The robustness against unknown process parameter variation is considered. Three algorithms based on the Sliding Mode Control (SMC) are proposed: SMC with conventional surface, SMC with PID surface and SMC with integral surface. The effectiveness of these algorithms are simulated and implemented on Two Conical Tank Interacting Level System (TCTILS) which exhibits dynamic non linearity and coupling dynamics. The SMC with conventional sliding surface provides robust control against parameter variation and excellent set point tracking compared to SMC with PID sliding surface and SMC with integral sliding surface.

Abstract: The scope of this paper is to look beyond linear solutions and discuss briefly about the recent developments in nonlinear system control & controller tuning methods. An adaptive cruise control model is taken as case study to illustrate the practicality of implementation in the simulation environment in terms of modelling and control of non-linear process. Effort is made to analyse the nonlinear system control in MATLAB and PROTEUS environment.

Abstract: This paper proposes a robust adaptive robust controller for nonlinear systems represented by input-output models with unmodeled dynamics. Under the circumstances that the output of the system is bounded, the proposed controller can guarantee that all the variables of the system are bounded in the presence of unmodeled dynamics and time-varying disturbances. The scheme does not need to generate an additional dynamic signal to dominate the effects of the unmodeled dynamics. It is shown that the mean-square tracking error can be made arbitrarily small by choosing some design parameters appropriately.

Abstract: The economic system is an extremely complex system, internal systems affected by many factors, highly nonlinear, time delay and other characteristics. This has brought great difficulties to the economic modeling and forecasting system. This paper presents an improved modeling and forecasting methods, recombinant methods by introducing chain data and add data growth economic indicators in an artificial neural network training, the time series data input window to solve practical engineering problems forecasts.