发布时间 : 星期三 文章电气工程及其自动化优秀毕业论文英文更新完毕开始阅读c2f00d11ae1ffc4ffe4733687e21af45b207fe13
Figure2. Schematic of the Coupled-Tank plant [1]
To be more specific, the set above two experimental sequences are aimed at: 1. How to mathematically model the Coupled-Tank from first principles in order
to obtain the two open-loop transfer functions characterizing the system, in the Laplace domain.
2. How to linearize the obtained non-linear equation of motion about the quiescent
point of operation.
3. How to design, though pole placement, a proportional-plus-integral-plus-feed
forward-based controller for the Coupled-Tank system in order for it to meet the required design specifications for each configuration.
4. How to implement each configuration controller in real-time and evaluate their
actual performance.
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Chapter2. Software Introduction
2.1 MATLAB Introduction
MATLAB is a programming environment for algorithm development, data analysis, visualization, and numerical computation. Using MATLAB, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and FORTRAN.
You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. For a million engineers and scientists in industry and academia, MATLAB is the language of technical computing [2]. Key Features:
? High-level language for technical computing
? Development environment for managing code, files, and data ? Interactive tools for iterative exploration, design, and problem solving
? Mathematical functions for linear algebra, statistics, Fourier analysis, filtering,
optimization, and numerical integration
? 2-D and 3-D graphics functions for visualizing data ? Tools for building custom graphical user interfaces
? Functions for integrating MATLAB based algorithms with external applications
and languages, such as C, C++, Fortran, Java?, COM, and Microsoft Excel
MATLAB can be used in following works: (1).Creating transfer functions
A transfer function can be expressed as a numerator polynomial divided by a denominator polynomial, that is, F(s) =N(s)/D(s). The numerator, N(s), is represented by a row vector, numf, the contains the coefficients of N(s). Similarly, the denominator, D(s), is represented by a row vector, denf, that contains the coefficients of D(s). We
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form F(s) with the command, F=tf(numf,denf). F is called a linear time-invariant (LTI) object, or transfer function, can be used as an entity in other operations, such as addition or multiplication. (2)Time response
We can use MATLAB to calculate characteristics of a second order system, such as damping ratio, ?; natural frequency?n; percent overshoot, %OS; settling time, Ts; and peak time, Tp. (3)Stability
MATLAB can solve for the poles of a transfer function in order to determine stability. Also, we can use MATLAB to find the range of gain for stability by generating a loop, changing gain, and finding at what gain we obtain right-half-plane poles.
(4)Steady-state error
Static error constants are found using limsnG(s)ass?0. Once the static error constant is found, we can evaluate the steady-state error. (5)Root locus techniques
MATLAB allows root loci to be plotted with the rlocus(GH) command. Points on the root locus can be selected interactively using the ‘rlocfind’ command. MATLAB then yields the gain (K) at that point as well as all other poles (p) that have that gain. We can zoom in and out of the root locus by changing the range of axis values. The root locus can be drawn over a grid that shows constant damping ratio (?) and constant natural frequency (?n) (6)Frequency Response Techniques
We can use MATLAB to make Bode plots using bode(G), where G/(s)=numg/deng and G is an LTI transfer function object. Also, we can use MATLAB to make Nyquist diagrams using Nyquist (G) [2].
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2.2 Simulink introduction
SIMULINK is an environment for multi domain simulation and Model-Based Design for dynamic and embedded systems. It provides an interactive graphical environment and a customizable set of block libraries that let you design, simulate, implement, and test a variety of time-varying systems, including communications, controls, signal processing, video processing, and image processing.
Add-on products extend SIMULINK software to multiple modeling domains, as well as provide tools for design, implementation, and verification and validation tasks. SIMULINK is integrated with MATLAB, providing immediate access to an extensive range of tools that let you develop algorithms, analyze and visualize simulations, create batch processing scripts, customize the modeling environment, and define signal, parameter, and test data [3].
Key Features
? Extensive and expandable libraries of predefined blocks
? Interactive graphical editor for assembling and managing intuitive block diagrams ? Ability to manage complex designs by segmenting models into hierarchies of
design components
? Model Explorer to navigate, create, configure, and search all signals, parameters,
properties, and generated code associated with your model
? Application programming interfaces (APIs) that let you connect with other
simulation programs and incorporate hand-written code
? MATLAB Function blocks for bringing MATLAB algorithms into SIMULINK
and embedded system implementations
? Simulation modes (Normal, Accelerator, and Rapid Accelerator) for running
simulations interpretively or at compiled C-code speeds using fixed- or variable-step solvers
? Graphical debugger and profiler to examine simulation results and then diagnose
performance and unexpected behavior in your design
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