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This contribution proposes a robust control scheme for a two-stage water pumping system based on an induction motor. In the first stage of the system, a boost converter is used for maximum power point tracking, while the second stage consists of a three-phase inverter that regulates the DC-bus voltage and the rotor speed of an induction motor. In order to achieve a fast-tracking of the maximum power, an improved maximum power point tracking algorithm based on a fuzzy logic controller is designed in this paper. On the other hand, a non-linear predictive controller is employed to drive the induction motor, due to its capability to eliminate the drawbacks of the conventional field-oriented control. The performances of the proposed control scheme are evaluated in the MATLAB/Simulink environment. The developed control scheme was compared with the conventional one (Perturb and Observe algorithm for maximum power point tracking and field-oriented control to control the induction motor). It is concluded that the developed control techniques offer excellent performances such as rapid maximum power point tracking, stable output power, and high-quality of power conversion. [ABSTRACT FROM AUTHOR]

In this work a MIMO non-linear predictive controller was developed for an extractive alcoholic fermentation process. The internal model of the controller was represented by two MISO Functional Link Networks (FLNs), identified using simulated data generated from a deterministic mathematical model whose kinetic parameters were determined experimentally. The FLN structure presents as advantages fast training and guaranteed convergence, since the estimation of the weights is a linear optimization problem. Besides, the elimination of non-significant weights generates parsimonious models, which allows for fast execution in an MPC-based algorithm. The proposed algorithm showed good potential in identification and control of non-linear processes.Neste trabalho um controlador preditivo não linear multivariável foi desenvolvido para um processo de fermentação alcoólica extrativa. O modelo interno do controlador foi representado por duas redes do tipo Functional Link (FLN), identificadas usando dados de simulação gerados a partir de um modelo validado experimentalmente. A estrutura FLN apresenta como vantagem o treinamento rápido e convergência garantida, já que a estimação dos seus pesos é um problema de otimização linear. Além disso, a eliminação de pesos não significativos gera modelos parsimoniosos, o que permite a rápida execução em algoritmos de controle preditivo baseado em modelo. Os resultados mostram que o algoritmo proposto tem grande potencial para identificação e controle de processos não lineares.

Gaussian processes are a probabilistic, non-parametric approach to modelling that allows easy merging of ordinary measured data and local linear models. This can be of particular importance in the identification of non-linear dynamic systems from experimental data, where there is usually more data available around the equilibrium points and only sparse data is available far from them. The utility of the Gaussian process model for predictive control is investigated in this paper and is illustrated on a simple first-order vehicle dynamics. [ABSTRACT FROM AUTHOR]

A general flatness-based framework for non-linear continuous-time predictive control is presented. It extends the results of Fliess and Marquez (2000) to the non-linear case. The mathematical setting, which is valid for multivariable systems, is provided by the theory of flatness-based exact feedforward linearisation introduced by the authors (Hagenmeyer and Delaleau 2003b). Thereby differential flatness does not only yield an easy calculation of the predicted trajectories considering the respective system constraints, but allows to use simple linear feedback parts in a two-degree-of-freedom control structure. Moreover, this formalism permits one to handle non-minimum phase systems, and furthermore to deal with parameter uncertainties and exogenous perturbations. Respective robustness analysis tools are available. Finally, an induction drive example is discussed in detail and experimental results for this fast electro-mechanical system are presented. [ABSTRACT FROM AUTHOR]

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Abstract: This work focuses on development of control algorithms by incorporating energy and water consumption to maintain climatic conditions in greenhouse. Advanced control algorithms can supply solutions to modern exploitations. The new developments usually require accurate models (probably multivariable and non-linear ones) and control methodologies capable of using these models. As an additional requirement it is important for the final application to be easy to use, so advanced control will not mean an increase in complexity of the manipulation of the installation. This article shows an alternative to classical climate control. It is based on two fundamental elements: an accurate non-linear model and a model-based predictive control (MBPC) that incorporate energy and water consumption. Genetic algorithms (GAs) play a key role in these two elements because functions to solve are non-convex and with local minima. First of all GAs supply a way to adjust the non-linear model parameters obtained from first principles, and finally GAs open the possibility of using non-linear model in the MBPC and of establishing a flexible cost index to minimize energy and water consumption. The results on a plastic greenhouse with arch-shaped roofs and for Mediterranean area are presented, important reduction in energy and water used in the cooling system (nebulization) is obtained. [Copyright &y& Elsevier]

In this work a SISO non-linear predictive controller was developed for an extractive alcoholic fermentation process. The internal model of the controller was represented by a functional link network (FLN). This model was identified using simulated data generated from a deterministic mathematical model whose kinetic parameters were determined experimentally. The FLN structure presented good non-linear approximation ability, with the advantage that the estimation of its weights is a linear optimization problem. The results show that the proposed algorithm has a great potential to identification and control of non-linear processes. [Copyright &y& Elsevier]