| Resumo: | Parallelization of real time control algorithms is a problem that the control engineer must consider to meet tighter specifications in terms of plant sampling time. However, due to the lack of appropriate tools, the development time and prototyping of an efficient parallel algorithm is much higher than its sequential equivalent. It is also common that such implementation requires knowledge of system programming. Assuming that a control engineer must concentrate in implementing an efficient control strategy, rather than work around system dependent issues, the Matrix Algorithms Automatic Parallelization System –MAPS- programming environment was developed. If the control algorithm can be represented in a matricial form, as is the case of many, this programming environment puts in the hands of the control engineer the power of parallel processing at the cost of the sequential programming model. In this paper the automatic parallelization of an Adaptive Generalized Predictive Control - AGPC – algorithm is employed as an example, using the MAPS environment. It will be shown that, as long as MAPS supports the target hardware, porting an algorithm is just a matter of describing the parallel network topology in a simple block diagram. Finally, the performance of this parallel AGPC algorithm, mapped over some network topologies will be presented and discussed.
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