Identificação e controle de processos não lineares utilizando redes neurais artificiais

Orientador: Rubens Maciel FilhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuimicaResumo: Considerando que a maioria dos processos industriais de interesse da Engenharia Química apresentam certo grau de não linearidade inerente ou introduzido por sistemas de controle automático, surge a importante necessidade de se investigar o desempenho de novas técnicas advindas da inteligência artificial, cujo interesse aqui está nas redes neurais artificiais, capazes de lidar com não linearidades de modo direto. Realizou-se neste trabalho uma ampla revisão bibliográfica referente à identificação e controle de sistemas não lineares. As várias possibilidades de identificação de sistemas dinâmicos utilizando modelos empíricos paramétricos foram apresentadas segundo uma visão unificada, com ênfase nos métodos baseados em redes neurais artificiais. Revisou-se também de modo amplo as principais técnicas de controle desenvolvidas para processos não lineares assim como as principais aplicações reportadas na literatura no âmbito da Engenharia Química. Posteriormente, utilizando-se de dois processos característicos da Engenharia Química, a saber, (1) dois reatores tanques conectados em série, nos quais ocorrem uma reação exotérmica, e com troca térmica; (2) evaporador de duplo efeito; foram discutidas várias possibilidades de identificação e controle utilizando redes neurais, em diversos níveis. Os resultados, obtidos por simulação computacional, mostram o potencial de utilização das redes neurais (na forma NNARX e NNSSIF), especialmente nas técnicas de controle preditivo, onde os melhores resultados foram obtidos. O primeiro sistema considerado possui dinâmica complexa e uma entrada e uma saída apenas (SISO), sendo que o segundo sistema possui múltiplas entradas e saídas (MIMO). Unindo técnicas advindas da inteligência artificial, como as redes neurais artificiais, métodos clássicos de identificação e a moderna teoria de controle, mostrou-se como estas metodologias podem ser utilizadas com sucesso na busca de melhores desempenhos dos processos químicos sob a ação do controle automáticoAbstract: Considering that most of the industrial processes of interest to Chemical Engineering present a certain degree of inherent non-linearity or one introduced by systems of automatic control, an important necessity of inquiring about the performance the new techniques derived from artificial intelligence, whose interest here is in the artificial neural networks, capable of dealing with non-linearity in a straightforward way. It was made in this work a thorough bibliographical review related to the identification and control of non-linear systems. The various possibilities of identification of dynamic systems using parametric empirical models were presented according to a unifying view, with emphasis in the methods based on artificial neural networks. The main control techniques developed for non-linear processes as well as the main uses reported in literature on the Chemical Engineering field were also thoroughly reviewed. Afterwards, making use of two processes typical ofthe Chemical Engineering, (1) two tanks reactors connected in series, in which a exothermal reaction occur, with thermal exchange; (2) double effect evaporator; various possibilities of identification and control using neural networks were discussed, in several levels. The results, obtained through computer simulation, show the potential usage of neural networks (in the form NNARX and NNSSIF), especially in the techniques of predictive control, where the best results were obtained. The first system taken into account has complex dynamics and Single Input, Single Output (SISO), whereas the second system has Multiple Input, Multiple Output (MIMO). Uniting techniques derived from artificial intelligence, such as artificial neural networks, classic methods of identification and the modem theory of control, it was shown that these methodologies can be successfully used in the search for better performances of chemical processes under the action of automatic controlDoutoradoDesenvolvimento de Processos QuímicosDoutor em Engenharia Químic

Sliding-mode Learning Of A Neuro-adaptive Robust Control Configuration

Performance and robustness are highly desirable characteristics for any control method. But they are not found in general simultaneously in the same configuration due to its opposite nature. Recently, the compromise between robustness and performance has motivated new studies, mixing adaptive and robust methods, which are complementary in dealing with uncertainties and parameter variation. Variable structure control is a very successful adaptive method which has attracted much attention recently due to its inherent robustness, and also because it is equally applied to linear and nonlinear systems. The basic idea is to restrict the state space of a given plant through a so called sliding surface, whose dynamics is simpler than the original plant dynamics. Enforcing a state-space trajectory from the initial condition of the plant to reach the surface in finite time, once there the plant remains on the surface and its dynamics is substituted by the surface dynamics. For adequately designed surfaces, they present the invariance property, guaranteeing an intrinsic robustness because the new dynamics does not depend on the plant parameters. Associating sliding-mode algorithms to artificial neural networks, some of the recently proposed configurations may present simultaneously good performance and robustness. In this work, a new configuration is proposed, implementing a neuro-adaptive control method using the variable structure approach to adjust the neural network weights, and presenting also robustness. The main idea is to add to a regular controller signal, a second control signal generated by an artificial neural network, in order to compensate for perturbations of the plant. An adaptive online learning is adopted whose transient signals are expected do not disturb the main control loop. It is expected also that the controller performance will be maintained through a wide variation of operational conditions of the plant, independently of perturbations caused by structural and parametric variations or nonlinearities not considered in the model. The configuration is explored through two different cases, when there is an acceptable linear model and when such model is not available. Numerical simulations presenting good results justify the expectations for the configuration.324152420Zhou, K., Ren, Z., A new controller architecture for high performance, robust, and fault-tolerant control (2001) IEEE Transaction on Automatic Control, 46 (10), pp. 1613-1618. , OctoberGao, W., Hung, J.C., Variable structure control of nonlinear systems: A new approach (1993) IEEE Transaction on Industrial Eletronics, 40 (1), pp. 45-56. , FebruaryDecarlo, R.A., Zak, S.H., Matthews, G.P., Variable structure control of nonlinear multivariable systems: A tutorial (1988) Proceedings of the IEEE, 76 (3), pp. 212-232Hung, Y.J., Hung, J.C., Variable structure control: A survey (1993) IEEE Trans. on Industrial Electronics, 40 (1), pp. 2-22Yu, H., Lloyd, S., Variable structure adaptive control of robot manipulators (1997) IEE Proc.-control Theory Appl., 144 (2), pp. 167-176Sun, F.C., Sun, Z.Q., Zhang, R.J., Chen, Y.B., Neural adaptive tracking controller for robot manipulators with unknown dynamics (2000) IEE Proc.-control Theory Appl., 147 (3), pp. 366-370Jung, S., Hsia, T.C., Robust neural force control scheme under uncertainties in robot dynamics and unknown environments (2000) IEEE Trans. on Industrial Electronics, 47 (2), pp. 403-412Topalov, A.V., Kaynak, O., Online learning in adaptive neurocontrol schemes with sliding mode algorithm (2001) IEEE Trans. on Systems, Man and Cybernetics, 31 (3), pp. 445-450Barambones, O., Etxebarria, V., Robust neural control for robotic manipulator (2002) Automatica, 38, pp. 235-242Haykin, S., (1998) Neural Networks: A Comprehensive Foundation, , Prentice Hall, 2nd EditionKhanmohammadi, S., Modified adaptive discrete control system containing neural estimator and neural controller (2000) Artificial Intelligence in Engineering, 14, pp. 31-38Frank, P.M., Ding, X., (1997) Survey of Robust Residual Generation and Evaluation Methods in Observer-based Fault Detection Systems, , Elsevier Science LtdLewis, F.L., Jagannathan, Yesildirek, (1999) Neural Network Control of Robot Manipulators and Nonlinear Systems, , Taylor & Franci