In this paper, we deal with nonholonomic wheeled mobile robots (WMR) modeled as uncertain nonlinear systems. Sources of uncertainties can be due to erroneous estimation of mass, inertia, and center of gravity and due to payload time-varying. They also can be considered as external disturbances generated from unstructured environments. We are proposing the use of a robust linear quadratic regulator (RLQR) to deal with tracking problems of WMR. In order to guarantee the effectiveness of this control approach, the robot posture is measured through a high-precision motion capture system. This RLQR encompasses in a unified framework all state and output uncertain parameters of the system and does not depend on any auxiliary parameter to be tuned. It is useful to be used in online applications. Experimental results are presented with a comparative study among the R-LQR, the nonlinear ∞ control via game theory, and the standard proportional-derivative controller plus computed torque (PD+CT).
Ceramic parts are increasingly replacing metal parts due to their excellent physical, chemical and mechanical properties, however they also make them difficult to manufacture by traditional machining methods. The developments carried out in this work are used to estimate tool wear during the grinding of advanced ceramics. The learning process was fed with data collected from a surface grinding machine with tangential diamond wheel and alumina ceramic test specimens, in three cutting configurations: with depths of cut of 120μm, 70 μm and 20μm. The grinding wheel speed was 35m/s and the table speed 2.3m/s. Four neural models were evaluated, namely: Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Networks and the Adaptive Neuro-Fuzzy Inference System. The models' performance evaluation routines were executed automatically, testing all the possible combinations of inputs, number of neurons, number of layers, and spreading. The computational results reveal that the neural models were highly successful in estimating tool wear, since the errors were lower than 4%.
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