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In the last years, FML (Fuzzy Markup Language) is emerging as one of the most efficient and useful language to define a fuzzy control thanks to its capability of modeling Fuzzy Logic Controllers in a human-readable and hardware independent way, i.e. the so-called Transparent Fuzzy Controllers (TFCs). However, although a FML fuzzy control is suitable to be employed in a wide range of applications,...
The paper presents an indirect adaptive fuzzy-neuro control scheme for a general high-order nonlinear continuous system. In the proposed scheme a fuzzy-neuro controller constructed based on the fuzzy neural networks for approximating the unknown nonlinearities of dynamic systems, and a sliding mode controller constructed to compensate for the modelling errors of fuzzy neural networks are incorporated...
The approximation of humanoid robot by an inverted pendulum is one of the most used model to generate a stable motion using a planned Zero Moment Point (ZMP) trajectory. In this paper, we aim at proposing to improve the reliability of this model using system identification techniques. To achieve this goal, we propose an identification method which is the result of the comprehensive application of...
In this paper we present novel strategies to formulate and solve nonlinear robust optimal control problems for dynamic systems which are affine in the uncertainty. We suggest the definition of a constrained Lyapunov differential equation providing robustness interpretations with respect to L2-bounded disturbances in the context of inequality state constraints. This interpretation allows us to compute...
In this study a new type of state observer for dynamic systems containing non-linear polynomials is proposed. The stability of the structure and also the uniform convergence of the state estimates are analyzed. The simple and efficient algebraic criteria of Naslin normal damping polynomials permit synthesis of the parameters. A series of simulations illustrates the proposed developments based on a...
In model-based fault diagnosis for dynamic systems with uncertain parameters, an envelope of all fault-free behaviors can be determined from the model and used as a reference for detecting faults. We demonstrate here a method for generating an envelope that is rigorously guaranteed to be complete, but without significant overestimation. The method is based on an interval approach, but uses Taylor...
The method of the decision of a task of definition of a complete vector of a condition of dynamic systems described by the ordinary nonlinear differential equations is stated. In a method the circuit of the decision of a point-to-point regional task of the certain type is realized and the generalized algorithm of a conditional - optimum filtration is used.
System identification is a very important part in control theory for nonlinear analysis and optimization. In the past years, neural identification of dynamic systems gains great interest because of its powerful mapping capability. In this paper, a learning algorithm for the feedforward neural network named extreme learning machine (ELM) is applied for nonlinear system identification problem. The simulation...
Projection pursuit learning (PPL) refers to a well-known constructive learning algorithm characterized by a very efficient and accurate computational procedure oriented to nonparametric regression. It has been employed as a means to counteract some problems related to the design of artificial neural network (ANN) models, namely, the estimation of a (usually large) number of free parameters, the proper...
This work is focused on controlling nonlinear systems with uncertain dynamics arisen from external disturbances, unmodeled nonlinearities, and/or unpredictable faults. A simple yet effective "hybrid memory-based control" method is proposed to cope with such uncertainties. The method does not involve direct nonlinearity cancellation, or on-line estimation of upper bounds on uncertainties,...
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