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Swig software wrapper manual#
This is a much more satisfying task than the manual tuning of horrendous look up tables with hundreds of parameters for a PID controller. The control engineer in this context needs to decide what constraints should the system respect and what should be the objective to be minimized.
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Optimization-based control methods aim at tackling these issues by describing the task of the controller by beans of an optimization problem, which is both intuitive to understand and easier to tune. However, their intrinsic flaws, such as the difficulty in treating multiple- input-multiple-output systems and the non-intuitive parameter tuning in order to achieve constraint satisfaction, become more and more conspicuous as the dynamical systems increase in complexity. Pretty much everything can be described as a system with inputs and outputs and whenever the output of such a system needs to exhibit a desired behavior, a controller is required.Ĭonventional control techniques, such as the famous PID controller, often perform well enough and have unarguably conquered the largest part of the industry.
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Therefore, the primary goal of this paper is to cover these topics.Ĭontrol theory has played a key role in many technological advancements of the past decades. Although SWIG has been in use for more than 6 years, little has been published on its design and the underlying mechanisms that make it work. By automating language integration, SWIG enables scientists to use scripting languages at all stages of software development and allows existing software to be more easily integrated into a scripting environment. SWIG requires no modifications to existing code and uses existing source to create bindings for nine different target languages including Python, Peal, Tcl, Ruby, Guile, and Java.
Swig software wrapper generator#
To solve this problem, an extensible compiler, simplified wrapper and interface generator (SWIG), has been developed to automate the task of integrating compiled code with scripting language interpreters. However, a common problem faced by the developers of a scripted Scientific application is that of integrating compiled code with an interpreter. Scripting languages such as Python and Tcl are a powerful tool for the construction of flexible scientific software because they provide scientists with an interpreted problem solving environment and they provide a modular framework for controlling software components written in C, C++, and Fortran.