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Event detection has become an important research issue in the decision making process of dynamic systems where one should be able to comprehend the situation in order to make an appropriate decision for future development. And machine intelligence is required to provide a solution for automated event detection. In this paper we propose a hybrid fuzzy logic system that has been used for detecting event,...
Orthonormal function expansions have been used extensively in the context of linear and nonlinear systems identification, since they result in a significant reduction in the number of required free parameters. In particular, Laguerre basis expansions have been used in the context of biological/physiological systems identification, due to the exponential decaying characteristics of the Laguerre orthonormal...
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, the problem of robust fault detection using an interval observer for dynamic systems characterized by LPV (linear parameter varying) models is presented. The observer faces the robustness problem using two complementary strategies. Modeling uncertainties are considered unknown but bounded by intervals. Their effect is addressed using an interval state observation method based on zonotope...
This paper considers the stability and constrained stabilization of dynamic systems described by second or higher order vector differential equations. Such systems arise in various applications including electromechanical systems, aerodynamics, structural analysis, robotics and vibration systems, in which the stabilization and improved performance play a crucial role. Due to the fact that the coefficient...
The multi-level recursive method is a new statistical prediction theory of dynamic systems. The multi-level recursive method is used to predict ship's rolling movements with time series prediction for the first time, which is a combined multi-level recursive method characteristic. In the same system, two different statistical prediction theories are compared through simulations. The case result shows...
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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