Starting from introducing various MUTUAL windows and on-line help facilities, the fundamentals of MAT LAB programming including data types, statements and matrix representation are explained first allowed by matrix manipulations, such as algebraic computation, logical and relationship expressions and data conversion. Then, flow charts in MAT LAB programming is illustrated, including loop structures, conditional structures, switches and trial structures.

MUTUAL function programming and pseudo code processing are covered together with two-dimensional and three- dimensional graphics and visualization techniques. MATT_ABA graphical user interface (GIG) technique sues are explained so that the readers will gain new GUI programming skills to design user-friendly interfaces. Finally, programming kills for delivering high speed, high efficiency codes are introduced with special emphasis on commonly used tips, vectored programming methodology and MIX programming fundamentals for mixed-language programming.

Exercises and chapter references cited are included. Programming, data types, program flow control, functions, matrix operations, GIG, code efficiency, vectored programming techniques, MIX (mixed-language) programming. Chapter-03 MUTUAL Applications in Scientific Computations This chapter covers both numerical computation and analytical problem solutions with MAT LAB. Topical parts included in this long chapter are linear algebra, calculus, ordinary differential equation, optimization, and data and signal processing.

First, a general discussion about analytical solutions and numerical solutions to mathematical problems is presented with a dedicated argument why using numerical methods. We then start by first explain and illustrate how to solve various linear algebra problems, including the input of special matrices, matrix analysis, similarity transformation, decomposition, generally problems, algebraic equation solutions and matrix function evaluations. It is demonstrate that the use of MAT LAB in the solution of linear algebra problems is very straightforward and reliable.

How to solve calculus related problems in MAT LAB is presented next which includes numerical solutions to difference, differentiation, integration and multiple integral problems, as well as analytical (symbolic) ways in solving certain classes of calculus problems. Then, it comes to the fundamentals of dynamical system simulation techniques that are mainly based on numerical solutions to ordinary differential equations in MAT LAB via numerous examples to illustrate how to solve stiff differential equations, implicit differential equations, stochastic differential equations and differential algebraic equations.

Integral transform methods and analytical solutions of differential equations are also dealt with. In particular, the numerical inverse Lovelace transform technique is introduced for solving some complicated differential equations. In the numerical solution methods in optimization problems, a universal nonlinear equation solver is presented, for finding with ease the possible multiple solutions to nonlinear equations, together with other approaches.

Unconstrained optimization problems are explored, followed by inner programming problems and quadratic programming problems as well as ordinary nonlinear programming problems. Dynamic programming techniques and their use in path planning problem applications are discussed as well in a separate section. Finally, this chapter ends with a section introducing data and signal processing methods, including one- and two dimensional interpolation problems and least squares curve fitting problems, data sorting, pseudo random number generating, fast Fourier series transformation and spectrum analysis.

Exercises and chapter references cited are included. Key. Fords: linear algebra, numerical and symbolic calculus, ordinary differential equation, numerical inverse Lovelace transform, optimization, dynamic programming, curve fitting, data interpolation, random numbers, signal processing Chapter-04 Mathematical Modeling and Simulation with Simulating This chapter opens with a historical recall of the evolution of Simulating and a simple explanation of the basic idea of Simulating. This chapter is prepared for those who have little to zero experience with Simulating.

First, a brief introduction to various block libraries of Simulating is given, and some of the commonly used blocks are described. Basic manipulations of Simulating blocks such as rotating, connecting and block parameter modification, together with how to build Simulating models, are then introduced and illustrated at the mouse click level. TO grow the Simulating modeling skills, some essential tools are introduced, including the use of the model browser, model printing and simulation parameter settings. To put Simulating in actual use, modeling and simulation techniques are demonstrated with some detailed illustrative examples.

Equipped with the necessary fundamental knowledge and prepared for more advanced modeling and simulation tasks, the readers are dead to learn linear system modeling and representation methods where L IT Viewer based linear system frequency domain analysis and numerical simulation methods are presented. Finally, simulation methods for continuous systems driven by stochastic inputs are discussed. Statistical analyses of simulation results are given such as probability density function, correlation and power spectral density of the signals in the systems with illustrative examples.

Exercises and chapter references cited are included. Simulating modeling, Simulating library, Block-diagram manipulation, Linear time invariant (LIT), Frequency domain analysis, Time domain analysis, Stochastic signal Statistical analysis Correlation analysis Power spectrum analysis Chapter-SO Commonly Used Blocks and Intermediate-level Modeling Skills This chapter will take a closer examination of some commonly used Simulating blocks and their uses in Simulating modeling so in the end of this chapter, readers will be at the intermediate-level in Simulating modeling techniques.

First covered is a simple example used to further demonstrate the model representation and modeling skills such as including vectored block modeling and model decoration techniques. Important problems such as the concept of algebraic loops and their elimination, and also the zero-crossing detection method are discussed. Then, Simulating modeling of linear multivariate systems is illustrated, where the L TTL block in the Control System Toolbox is recommended for simplifying the modeling process.

Commonly used blocks important in Simulating modeling applications such as the lookup table and various switches are explored. General methods in constructing piecewise linear nonlinearities are introduced for both cases: memory’s nonlinearities and nonlinearities with memories. Simulating modeling techniques for various kinds of differential equations are demonstrated in a dedicated section. These include ordinary differential equations, differential algebraic equations, delay differential equations, switching differential equations and even fractional-order (Nan-integer-order) differential equations.

Simulation result visualization is essential in any simulation task so various visualization output blocks in Simulating are presented, such as scope output, workspace variable output and gauges output. More advanced Simulating output visualization methods are presented, including three- emotional animation methods with virtual reality techniques. Fundamental world modeling with VRRP is briefly introduced as well, and the VRRP models driven by MUTUAL and Simulating output are discussed. Finally, subsystem modeling is introduced using subsystem masking techniques.

An illustrative example of Simulating modeling of a complicated system is presented in detail. Exercises and chapter references cited are included. Algebraic loop, L TTL blocks, Nonlinear blocks, differential algebraic equations, delay differential equations, switching differential equations, fractional-order differential equations, VRRP, World modeling, subsystem masking Chapter-06 Advanced Techniques in Simulating Modeling and Applications Simulating offers powerful direct graphical based programming-free methods to get system simulation tasks completed.

In practice, since graphical methods have some limitations, it may be necessary to use command-line based modeling and design methods together with graphical methods. In this chapter, advanced techniques of command-line modeling and application are presented by first introducing how to use MAT LAB commands to create Simulating models. By command-line drawing techniques, complicated Simulating oodles can be created. Then, issues to note during the execution of Simulating models is introduced are discussed. Elimination techniques of nonlinear systems are also addressed.

In particular, the Pad ‘ e approximation to pure time delays is further discussed. It can be seen that not all the models can be constructed with Simulating graphical methods alone. Some of the complicated models can only be created and analyzed using MUTUAL commands. Thus, using a dedicated section, advanced techniques are presented for creating complicated models. S-function programming techniques will be presented ND illustrated and their use in simulation of automatic disturbance rejection control (DARK) systems will be demonstrated as a case study.

Finally, command-line based optimal controller design technique with Simulating models is introduced, and optimal controller design methods for nonlinear plants are also presented as an advanced Simulating modeling application example. Exercises and chapter references cited are included. Elimination, Delay approximation, S-function programming, Masking S-function block, Automatic disturbance rejection control, Optimal controller design, Global optimization,

Chapter-07 Modeling and Simulation Of Engineering Systems Simulating models can always be constructed since low-level Simulating blocks can be used to model any dynamical system with arbitrary complexity. However, for complex engineering (and non-engineering) system simulation tasks, this chapter explains, promotes and demonstrates the multi-domain physical modeling strategy advocated and implemented in Simulating. Many well-established and specialized blockades in various disciplines have been developed for use with Simulating. Some of the blockades have been developed and integrated in the Simple framework.

This chapter dedicated the first section in introducing the concept of multi-domain physical modeling and an introduction to the Simple bollocks. Then, in detail, electrical system modeling with Comportment’s and other blockades is addressed. The rest of the chapter covers the modeling and simulation of electronic systems, motor drive systems and mechanical systems with a lot of examples. Multi-domain physical modeling Simple, electrical system modeling, electronic systems, Spice circuit model, motor drive systems, mechanical systems, mechanical CAD model Chapter- 08 Modeling and Simulation of Non-Engineering Systems

This chapter serves as a showcase to demonstrate that MUTUAL/Simulating can also be used directly in modeling and simulation of many non-engineering systems. There are also a lot of third-party programs and blockades developed by scholars worldwide. This chapter is only a small showcase chapter. First, modeling and simulation of pharmaceutics systems are presented. Compartment modeling is briefly introduced, and physiology based pharmaceutics modeling methods and nonlinear generalized predictive control of anesthetic processes are shown.

Then, a dedicated section is included for MUTUAL/Simulating based image and video processing. Image Processing Toolbox and Computer Vision System Toolbox bollocks are also presented, and real-time video processing systems are explored. In many non-engineering systems, the finite state machine concept is important. How to use Stateless to model and simulate complicated supervision problems is presented. Stateless also generalizes the capabilities of logical or switched systems modeling, and we show that this can be used to describe systems with loops of conditional processes.

Finally, this chapter ends with a section on modeling and simulation of discrete event systems and a queuing system s used as an example to demonstrate the use of the Sentiments bollocks. pharmaceutics systems Compartment modeling predictive control image and video processing finite state machine concept Stateless switched systems Sentiments bollocks discrete event systems queuing system Chapter-09 Hardware-in-the-loop Simulation and Real-time Control Simulation is mostly numerical. However, simulation could be analog.

Yet, in this chapter, we will show that the simulation can be partly numerical and partly analog. Including the dynamic plant in the loop of simulation, is referred to as a hardware-in-the-loop simulation. Since this kind of simulation s often performed in real-time, it is sometimes referred to as real-time simulation. This has been made very simple in MUTUAL/Simulating due to the Real-Timekeeper’s provided by Metaphors that can translate the Simulating models into C code, and the standalone executable files can also be generated using this tool, so that real-time control can be performed.

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