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Robustifying analysis of the direct adaptive control of unknown multivariable nonlinear systems based on a new neuro-fuzzy method

Theodoridis D. C., Boutalis Y.S., Christodoulou M. A.

Journal of Artificial Intelligence and Soft Computing Research

2011

Vol. 1, No. 1

59--79

In this paper, we are dealing with the problem of directly regulating unknown multivariable affine in the control nonlinear systems and its robustness analysis. The method employs a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Systems (FS) operating in conjunction with High Order Neural Networks. In this way the unknown plant is modeled by a fuzzy - recurrent high order neural network structure (F-RHONN), which is of the known structure considering the neglected nonlinearities. The development is combined with a sensitivity analysis of the closed loop in the presence of modeling imperfections and provides a comprehensive and rigorous analysis showing that our adaptive regulator can guarantee the convergence of states to zero or at least uniform ultimate boundedness of all signals in the closed loop when a not-necessarily-known modeling error is applied. The existence and boundedness of the control signal is always assured by employing a method of parameter “Hopping” and “Modified Hopping”, which appears in the weight updating laws. Simulations illustrate the potency of the method showing that by following the proposed procedure one can obtain asymptotic regulation despite the presence of modeling errors. Comparisons are also made to simple recurrent high order neural network (RHONN) controllers, showing that our approach is superior to the case of simple RHONN’s.

Hybrid modeling of biomedical systems and measuring nonlinear characteristics of biosignals for improving quality of life

Darowski M., Klonowski W., Kozarski M., Ferrari G., Zieliński K., Stępień R.

Metrology and Measurement Systems

2007

Vol. 14, nr 1

89-100

The main purpose of this paper is to present some metrological aspects of the new concept of hybrid modeling (combined physical and in silico) of biological systems as well as possible applications of nonlinear (symbolic) biosignal analysis for improving quality of life through modeling and knowledge-based measurements in medicine.

Bounding approach to parameter estimation without prior knowledge on modeling error and application to quality modeling in drinking water distribution systems

Duzinkiewicz K.

Archives of Control Sciences

2006

Vol. 16, no. 1

51-70

Parameter estimation of an autoregressive with moving average and exogenous variable ARMAX model is discussed in this paper by using bounding approach. Bounds on the model structure error are assumed unknown, or known but too conservative. To reduce this conservatism, a point-parametric model concept is proposed, where there exist a set model parameters and modeling error corresponding to each input. Feasible parameter sets are defined for point-parametric model. bounded values on the model parameters and modeling error can then be computed jointly by tightening the feasible set using observations under deliberately designed input excitations. Finally, a constantly bounded parameter model is established, which can be used for robust output prediction and control.