Toward the synthesis of state-space controllers, a neural-genetic model based on the linear quadratic regulator design for the eigenstructure assignment of multivariable dynamic systems is presented. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network (RNN) to perform the selection of the weighting matrices and the algebraic Riccati equation solution, respectively. A fourth-order electric circuit model is used to evaluate the convergence of the computational intelligence paradigms and the control design method performance. The genetic search convergence evaluation is performed in terms of the fitness function statistics and the RNN convergence, which is evaluated by landscapes of the energy and norm, as a function of the parameter deviations. The control problem solution is evaluated in the time and frequency domains by the impulse response, singular values, and modal analysis.

To prevent and mitigate the environmental impact of the transportation and extraction of oil and its derivatives, the conception, design, development, and implementation of an embedded system for wireless sensor networks (WSN) is presented is this paper. The proposed embedded system is a static sensor node that detects and classifies pollutants in aquatic environments using machine learning and IoT (Internet of Things) approaches. The article presents the development of the sensor node, which consists of three phases. In the first phase, the conception and modeling of the embedded system are presented, including mathematical modeling of the node, the node’s power supply system, WSN communication structure, pollutant detection, and classification via machine learning and IoT. The implementation of the static sensor node is presented in the second phase of the project, which includes functional modeling of the measurement, the architecture of the embedded system, and its physical structure. In the last phase, the detection and classification tests of the proposed sensor node are presented, including implementing five sensors. They are evaluated indoors by analyzing seawater samples with gasoline and diesel, pH and turbidity measurements of seawater and freshwater with gasoline, and experiments through direct and indirect measurements of seawater and diesel. Since the initial results of the indoor experiments are satisfactory, the proposed sensor node is regarding as a promising device for detecting and classifying pollutants in real-world aquatic environments.

An anti-collision method for multi-agent systems based on the centralization and decentralization effects in a control protocol is presented in this article. In this context, a matrix called Anti-Laplacian is defined, it acts as a decentralizing agents when the UAVs are on a conflicting route. The matrix adjustment occurs through proportionality relations with the Laplacian Matrix from an interaction graph between the agents. The adjustment method aims a balance between centering and decentering to avoid collisions. Controlled quadcopters follow the trajectory indicated by virtual agents that act as guides for the real ones. The tests are performed via simulation for the most critical cases, with protocols involving flock centralization. As a virtual agent, a first-order model is used in the simulation, the method efficiency is observed by varying the number of agents involved.

A data-driven approximation formulation for the state reconstruction problem of dynamical systems is presented in this paper. Without the assumption of an explicit mathematical model, the Hamilton–Jacobi–Bellman (HJB) based approach of a data-driven state reconstruction design method for monitoring and output-feedback control of dynamical systems is presented. The proposed state reconstruction design is based on a dynamic programming approach. To evaluate the proposed state reconstruction, computational experiments are conducted using only dynamical system model output data. The sensitivity of the algorithm parameters is also analyzed and discussed. The performance evaluation is analyzed in terms of the error metrics of the discrete linear quadratic regulator with output feedback under the value iteration algorithm through a reinforcement learning strategy.

In this paper, a bioinspired method in the magnetic field memory of the bees, applied in a rover of precision pollination, is presented. The method calculates sharpness features by entropy and variance of the Laplacian of images segmented by color in the HSV system in real-time. A complementary positioning method based on area feature extraction between active markers was developed, analyzing color characteristics, noise, and vibrations of the probe in time and frequency, through the lateral image of the probe. From the observed results, it can be seen that the unsupervised method does not require previous calibration of target dimensions, histogram, and distances involved in positioning. The algorithm showed less sensitivity in the extraction of sharpness characteristics regarding the number of edges and greater sensitivity to the gradient, allowing unforeseen operation scenarios, even in small sharpness variations, and robust response to variance local, temporal, and geophysical of the magnetic declination, not needing luminosity after scanning, with the two freedom of degrees of the rotation.