We introduce a control strategy to solve the regulation control problem, from the perspective of trajectory planning, for an uncertain 3D overhead crane. The proposed solution was developed based on an adaptive control approach that takes advantage of the passivity properties found in this kind of systems. We use a trajectory planning approach to preserve the accelerations and velocities inside of realistic ranges, to maintaining the payload movements as close as possible to the origin. To this end, we carefully chose a suitable S-curve based on the Bezier spline, which allows us to efficiently handle the load translation problem, considerably reducing the load oscillations. To perform the convergence analysis, we applied the traditional Lyapunov theory, together with Barbalat’s lemma. We assess the effectiveness of our control strategy with convincing numerical simulations.

Departing from analyzing a general input-saturated second-order system, a Lyapunov-based constructive procedure for the output-feedback stabilization of the constantly perturbed DC-DC buck power converter is presented. The proposed scheme also incorporates a Σ-Δ modulator. The obtained control scheme is developed in two parts: a proportional-integral-type family of algorithms devoted to regulating the uncertain system by using measurements of the output voltage and a Σ-Δ modulator dedicated to transforming the control action into a {0, 1} discrete-valued signal. This modulator is a kind of a sliding controller, which can be seen as a subsystem with an on-off output. This combination, the PI-type family of controllers and the Σ-Δ modulator, assures the global stability of the closed-loop system, even if parametric uncertainties are presented. The convergence analysis was carried out using Lyapunov's method. The theoretical results are confirmed using real-time experimental tests, which demonstrate the anti-windup scheme and the Σ-Δ modulator efficiency. The experiments consider the saturation of the control input and disturbances, having obtained convincing results.

Nowadays, more than half of the world’s population lives in urban areas, and this number continues increasing. Consequently, there are more and more scientific publications that analyze health problems of people associated with living in these highly urbanized locations. In particular, some of the recent work has focused on relating people’s health to the quality and quantity of urban green areas. In this context, and considering the huge amount of land area in large cities that must be supervised, our work seeks to develop a deep learning-based solution capable of determining the level of health of the land and to assess whether it is contaminated. The main purpose is to provide health institutions with software capable of creating updated maps that indicate where these phenomena are presented, as this information could be very useful to guide public health goals in large cities. Our software is released as open source code, and the data used for the experiments presented in this paper are also freely available.

We present a proportional–integral–derivative-based controller plus an adaptive slide surface to solve the trajectory tracking control problem for a fully actuated vessel with unknown parameters perturbed by slowly varying external unknown dynamics. The controller design assumes that the vessel moves at low speed and frequency, its physical parameters are unknown, and its state is measurable. The control approach ensures error tracking convergence toward a small vicinity at the origin. We conduct the corresponding stability analysis using the Lyapunov theory and saturation functions. We tested the controller through two numerical experiments—a turning ellipse maneuver and a rest-to-rest maneuver—where the vessel parameters were unknown, and we obtained satisfactory results.