Automatic parallelization of matricial algorithms

The introduction of parallel processing architectures allowed the real time impelemtation of more sophisticated control algorithms with tighter specifications in terms of sampling time. However, to take advantage of the processing power of these architectures the control engeneer, due to the lack of appropriate tools, must spend a considerable amount of time in the parallelizaton of the control algorithm.

Preface to the special section on algorithms and architectures for real-time control

Rapid developments in microelectronics and computer science continue to fuel new opportunities for real-time control engineers. The ever-increasing system complexity and sophistication, environmental legislation, economic competition, safety and reliability constitute some of the driving forces for the research themes presented at the IFAC Workshop on Algorithms and Architectures for Real-Time Control (AARTC'2000). The Spanish Society for Automatic Control hosted AARTC'2000, which was held at Palma de Maiorca, Spain, from 15 to 17 May. This workshop was the sixth in the series.

Comparison of on-line learning algorithms for RBF models in greenhouse environmental control problems

The problem with the adequacy of radial basis function neural networks to model the inside air temperature as a function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed.

Algorithms and architectures for real-time control

Algorithm and Architectures for Real-Time Control Workshop had the objective to investigate the state of the art and to present new research and application results in software and hardware for real-timecontrol, as well as to bring together engeneers and computer scientists who are researchers, developers and practitioners, both from the academic and the industrial world.

Completely supervised training algorithms for B-spline neural networks and neuro-fuzzy systems

Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By interducing the relationship between B-spline neural networks and certain types of fuzzy models, training algorithms developed initially for neural networks can be adapted by fuzzy systems.

Adaptive generalized predictive control algorithm implemented over an heterogeneous parallel architecture

In this paper a parallel implementation of an Adaprtive 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.

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

In this paper a parallel implementation of an Adaprtive 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.

Real-time parameter estimation of dynamic temperature and humidity models for greenhouse adaptive climate control

A real-time parameter estimator for the climate discrete-time dynamic models of a greenhouse located at the North of Portugal are presented. The experiments showed that the second order models identified for the air temperature and humidity achieve a close agreement between simulated and experimantal data.

Real-time parameter estimation of dynamic temperature models for greenhouse environmental control

For a greenhouse located at UTAD-University, the methods used to estimate in real-time the parameters of the inside air temperature model will be described. The structure and the parameters of the climate discrete-time dynamic model were previously identified using data acquired during two different periods of the year.

Learning control systems

Novel method of controller (PID) autotuning, involving neural networks and genetic algorithms: to employ neural networks to map the identification measures and controller parameters to objective functions, adapt these models on-line; to employ the genetic algorithm to perform on-line minimization.