Applications of MATLAB

This may lead you to the solution you are looking for. Issuing the command 

Listed here are applications of MATLAB so you can easily hnd examples in this book  

Equations of state and plotting. Chapter 2, p. 13. Vapor-liquid equilibria. Chapter 3, p. 30. 

Multiple equations, few unknowns. Chapter 4, p. 48. Integrate ordinary differential equation. Chapter 8, p. 114. Use of global command. Chapter 8, p. 116. 

---

This tutorial looks at the application of MATLAB to digital control system design, using the problems in Chapter 7 as design examples. 

Zhang Shanwen, Lei Yingjie, Feng Youqian, et al. 2007. The application of Matlab in time series analysis. Xi an Xidian University. 

T. Michalowski, Application of GATES and MATLAB for Resolution of Equilibrium, Metastable and Non-Equilibrium Electrolytic Systems, Chapter 1 in Applications of MATLAB in Science and Engineering (ed. by T. Michalowski), InTech - Open Access publisher in the fields of Science, Technology and Medicine, 2011. 

Second, to provide a thorough introduction, we need to reproduce many of the graphics windows. To conserve paper (and trees), we have moved these print-intensive and detailed explanations to our Web Support. Furthermore, the HTML-based Help Desk of MATLAB is extremely thorough and should serve as our main guide for further applications. 

Matlab does not include a routine of the kind of fzero for more than one variable. Only the function fsolve, which is part of the Optimisation Toolbox, can deal with systems of equations with several variables. Here we demonstrate the application of fsolve to the system of equations (3.70). 

MATLAB comes as one main body of built-in functions and codes, and there are many additional specialized MATLAB toolboxes for various applications. As this book is primarily directed towards undergraduate and beginning graduate students, we have restricted ourselves deliberately to using the main body of MATLAB only in our codes and none of its many toolboxes. 

This paper presents the application of a model based predictive control strategy for the primary stage of the freeze drying process, which has not been tackled until now. A model predictive control framework is provided to minimize the sublimation time. The problem is directly addressed for the non linear distributed parameters system that describes the dynamic of the process. The mathematical model takes in account the main phenomena, including the heat and mass transfer in both the dried and frozen layers, and the moving sublimation front. The obtained results show the efficiency of the control software developed (MPC CB) under Matlab. The MPC( CB based on a modified levenberg-marquardt algorithm allows to control a continuous process in the open or closed loop and to find the optimal constrained control. 

Li HY, Jiang XF, Yang SH. 2006. Research and application of microseismic monitoring location of strata fracturing based on Matlab [J]. Journal of China Coal Society, 131(2) 154-158. 

Biahmou et al. [42] have developed an approach for deriving behavior models from 3D models in MATLAB/SimMechanics that have been created with CATIA V5. After updating the geometrical model, the behavioral model is to be updated by the user. In order to free the user from this task and achieve a structured and right synchronization of changes in partial models of a system, the application of ontologies has been addressed to identify the update sequence of models [43]. 

Many software packages have incorporated adaptive difference schemes for ordinary differential equations as the core of their designs. NDSolve of Mathematica and ode45 of MATLAB are two applications that are based on an adaptive extension of the fourth-order Runge-Kutta method. 

The students will be given topics/themes related to the subject they have to explore an interesting problem of their choice in the context of the course. Projects can be done individually or in teams of two/three students. The students will be given various themes related to automotive electronics like the realization of control algorithms required for specific automotive applications using MATLAB/Simulink and the implementation of communication protocols automotive sensors emission control systems safety, security, and driver assistance systems etc. The activity involves different phases like the following ... 

They given the analysis of the local truncation error and the stability of all the methods mentioned above. For the local error truncation analysis the authors presented the relation between the error and the energy. The authors examined the accuracy and computational efficiency of all the above methods via the numerical approximation of five problems. Conclusions and open problems are also defined. Computational codes written in Maple are presented for the construction of all methods developed in this paper. Subroutines written in Matlab , for the application of the methods described are also presented. 

This section illustrates application of the PRESS statistic for process model structure selection. Ljung (1987) used data collected from a laboratory-scale Process Trainer to illustrate various identification techniques and examined the sum of squared conventional residuals and Akaike s information theoretic criterion (AIC) for model structure selection. Two sets of input-output data collected from this process are available within MATLAB. We use the entire first set of data M = 1000), called DRYER2 in MATLAB, for this study. Two different model structures are examined here, namely the ARX and FIR model structures, with the objective to find the model within a pcurtic-ular structure that produces the smallest PRESS. 

Prior to the application of MLR-SPA, the data were mean centered to remove constant background effects [13], MLR-SPA calculations were performed using a routine developed by Araujo et al. [14] in MATLAB 7.0 (MathWorks, South Natic, MA, USA) software. The MLR-SPA method is complicated, and a detailed explanation of the projection operations is given in the literature [14,15]. 

---