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A solution to the long standing problem of the pendulum on a cart is presented. This solution insolves a reformulation of the original sjstem model in order to show that the inverted pendulum system belongs to a particular class of nonlinear systems: a class consisting of four cascaded integrators and a nonlinear "perturbation" term. Our controller first brings the pendulum close to the vertical unstable equilibrium p i n t and then regulates the cart position around the origin. We prore that using the proposed control strategy, all state rariables converge to zero for a &en set of initial conditions ($(O), B(0)) belonging to a special domain of attraction.

This study investigates the possibility of inducing damage diagnosis capabilities in carbon fiber reinforced polymer composite slabs using custom-built integrated sensors and conventional, affordable equipment. The concept utilizes magnetoelastic strips integrated via 3D printing procedures in composite slabs. Under external mechanical loading, the strip magnetization changes due to the magnetoelastic phenomenon. Accordingly, electrical signals may be passively induced in conventional reception coil circuits placed at a distance from the slab. Since these signals quantify the vibrating slab’s response, which is affected by the slab’s structural integrity, damage may be detected when specific signal characteristics change. Two main issues are examined, namely the ability of receiving meaningful (with respect to noise) electrical signals from the built-in strips despite their contact-less passive reception, and the potential of diagnosing damage using such signals. Hence, slabs of various sizes and levels of structural damage (notches) have been vibrated at different frequencies and amplitudes. Treating the experimental data from integrated strips by applying the proposed processing framework allows for calculating eigenfrequencies sensitive to occurring damage (and its severity), as verified by finite element models of the vibrating slabs. Accordingly, damage may be detected and evaluated via the currently proposed experimental testing and analysis framework.

A statistical diagnostic scheme investing on the novel concept of freshness (instead of spoilage) determination in fish industry is presented. Drawing on fault diagnosis expertise, the scheme relies on cost-effective vibration-like testing of fish samples and accurate (under typical uncertainties) stochastic modeling of their response. Via the identified models, specific quantities related to the sample's texture (itself indicative of its freshness) are computed. Then, such quantities from test samples of unknown freshness are compared with the nominal "fresh-fish" values for determining the freshness degree. This comparison is based on custom-built statistical hypothesis tests, capable of issuing reliable decisions while taking into account the risk of the decision-making process. Tests involving 0-, 3-and 6-day-old fish samples show very promising results.Index Terms-Fault diagnosis, statistical decision-making, stochastic systems, industrial application, fish freshness.