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A novel technique, involving U-model based IMC (internal model control), is proposed for the adaptive control of nonlinear dynamic plants. The proposed scheme combines the robustness of the IMC and the ability of neural networks to identify arbitrary nonlinear functions, with the control-oriented nature of the U-model to achieve adaptive tracking of stable nonlinear plants. The proposed structure...
This paper presents the SC-MER, safety control for Mars exploration rover, an innovative traction control scheme for wheeled mobile vehicles. The system is thought to be used on space mission rovers and is based on fuzzy logic and competitive neural networks to achieve optimal navigation on rough terrain with variable morphology. The main goal of this research is to minimize the power consumption...
This paper presents an approach to process fault diagnosis (FDI) in nonlinear dynamical systems, based on a bank of neural observers. Each neural observer is tuned to a particular fault and predicts, using its embedded model, the expected values for the sensor readings. The residuals, the difference between the sensor readings and the predicted readings, are used as fault indicators. Each neural observer...
The paper introduces a novel approach for the kinematic coordination of mechanical robot micro-grippers on the basis of neural networks. Conventional robot systems use specialized grippers for specific tasks. For objects with an amorphous structure, variable shape or small dimensions, conventional grippers become unreliable due to several reasons. The present paper presents an approach on the basis...
In this work we introduce a model for studying the distribution and control of atmospheric pollution from PMiQ. The model is based on the use of a cellular neural network (CNN) and more precisely on the integration of the mass-balance equation; at the same time it simulates the scenario regarding a planar grid describing the whole studied area (the city of Palermo) by means of a CNN and a set of Bayesian...
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