Implementation of an adaptive generalized predictive control algorithm over an heterogeneous parallel architecture

In this paper a parallel implementation of an Adaptive Generalized Predictive Control (AGPC) algorithm is presented. Since the AGPC algorithm needs to be fed with knowledge of the plant transfer function, the parallelization of a standard Recursive Least Squares (RLS) estimator and a GPC predictor is discussed here. Also, since a matrix inversion operation is required in the GPC predictor algorithm, special attention is given to its parallelization. A small DSP network with up to 2 processors is used to investigate, the performance of the parallel implementation. To exploit an heterogeneous architecture the parallel algorithm is mapped over a network builded up of transputers as communication elements, and DSPs as computing elements. Further some heterogeneous topologies are compared. Execution times and efficiency results of the RLS and GPC steps are presented to show the performance of the parallel algorithm, over different topologies.