Current Issue : April - June Volume : 2012 Issue Number : 2 Articles : 6 Articles
This paper presents the state identification study of 3D partial differential equations (PDEs) using the differential neural networks\n(DNNs) approximation. There are so many physical situations in applied mathematics and engineering that can be described by\nPDEs; these models possess the disadvantage of having many sources of uncertainties around their mathematical representation.\nMoreover, to find the exact solutions of those uncertain PDEs is not a trivial task especially if the PDE is described in two or more\ndimensions. Given the continuous nature and the temporal evolution of these systems, differential neural networks are an attractive\noption as nonparametric identifiers capable of estimating a 3D distributed model. The adaptive laws for weights ensure the\nââ?¬Å?practical stabilityââ?¬Â of the DNN trajectories to the parabolic three-dimensional (3D) PDE states. To verify the qualitative behavior\nof the suggested methodology, here a nonparametric modeling problem for a distributed parameter plant is analyzed....
We present a design method for iterative learning control system by using an output recurrent neural network (ORNN).\r\nTwo ORNNs are employed to design the learning control structure. The first ORNN, which is called the output recurrent\r\nneural controller (ORNC), is used as an iterative learning controller to achieve the learning control objective. To guarantee the\r\nconvergence of learning error, some information of plant sensitivity is required to design a suitable adaptive law for the ORNC.\r\nHence, a second ORNN, which is called the output recurrent neural identifier (ORNI), is used as an identifier to provide the\r\nrequired information. All the weights of ORNC and ORNI will be tuned during the control iteration and identification process,\r\nrespectively, in order to achieve a desired learning performance. The adaptive laws for the weights of ORNC and ORNI and\r\nthe analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will\r\nasymptotically converge to zero and repetitive output tracking error will asymptotically converge to zero except the initial resetting\r\nerror....
Model of ultrasonic motor is the foundation of the design of ultrasonic motor�s speed and position controller. A two-input and\r\none-output dynamic Takagi-Sugeno model of ultrasonic motor driving system is worked out using fuzzy reasoning modeling\r\nmethod in this paper. Many fuzzy reasoning modeling methods are sensitive to the initial values and easy to fall into local\r\nminimum, and have a large amount of calculation. In order to overcome these defects, equalized universe method is used in\r\nthis paper to get clusters centers and obtain fuzzy clustering membership functions, and then, the unknown parameters of the\r\nconclusions of fuzzy rules are identified using least-square method. Different experimental data that are tested with different\r\noperational conditions are used to examine the validity of the fuzzy model. Comparison between experimental data and calculated\r\ndata of the model indicates that the model can well describe the nonlinear characteristics among the frequency, amplitude of\r\ndriving voltage and rotating speed. The proposed fuzzy model can be used to analyze the performance of ultrasonic motor driving\r\nsystem, and also can be used to design the speed and position controller of ultrasonic motor....
Model predictive control (MPC) has been used successfully in industry. The basic characteristic of these algorithms is the\nformulation of an optimization problem in order to compute the sequence of controlmoves that minimize a performance function\non the time horizon with the best information available at each instant, taking into account operation and plant model constraints.\nThe classical algorithms InfiniteHorizonModel Predictive Control (IHMPC) andModel Predictive Control with Reference System\n(RSMPC) were used for the experimental application in themultivariable control of the pilot plant (level and pH). The simulations\nand experimental results indicate the applicability and limitation of the control technique....
This paper addresses the problem of global asymptotic stability of a class of discrete uncertain state-delayed systems described by\nthe Fornasini-Marchesini second local state-space (FMSLSS) model using generalized overflow nonlinearities. The uncertainties\nare assumed to be norm bounded. A computationally tractable, that is, linear-matrix-inequality-(LMI-) based new criterion for\nthe global asymptotic stability of such system is proposed. It is demonstrated that several previously reported stability criteria\nfor two-dimensional (2D) systems are recovered from the presented approach as special cases. Numerical examples are given to\nillustrate the usefulness of the presented approach....
One of the main advantages of predictive control approaches is the capability of dealing explicitly with constraints on the\r\nmanipulated and output variables. However, if the predictive control formulation does not consider model uncertainties, then the\r\nconstraint satisfaction may be compromised. A solution for this inconvenience is to use robust model predictive control (RMPC)\r\nstrategies based on linear matrix inequalities (LMIs). However, LMI-based RMPC formulations typically consider only symmetric\r\nconstraints. This paper proposes a method based on pseudoreferences to treat asymmetric output constraints in integrating SISO\r\nsystems. Such technique guarantees robust constraint satisfaction and convergence of the state to the desired equilibrium point. A\r\ncase study using numerical simulation indicates that satisfactory results can be achieved....
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