Data-driven control systems based on Deep Reinforcement Learning (DRL) agents are emerging as a promising alternative to traditional control approaches in power converter applications because of the enhanced robustness under uncertainties, external disturbances, and system nonlinearities. However, the hyperparameters of required neural networks– such as the number of layers, neurons, and activation functions– are still selected through empirical tuning, resulting in suboptimal performance and limited generalization. This work presents a data-driven control system for current tracking problem in three-phase voltage source inverter (VSI) using a simple perceptron as actor policy within a Reinforcement Learning framework. The control policy is trained using Soft Actor Critic (SAC) algorithm in order to generate continuous duty cycle using a simple state vector. Two neural network (NN) architectures are evaluated: a simple perceptron, and a simple perceptron with extended state inputs, including real-time current measurements. In addition, the performance of the data-driven control system is analyzed in terms of root mean square error (RMSE) and total harmonic distortion (THD). Results disclose that a simple perceptron is enough to control the VSI for the specific current tracking problem. Under this configuration, the proposed controller achieves a maximum RMSE of 1.20 A and a THD of 6.28% at a 5 kHz sampling frequency. The results validate the applicability of compact, data-driven control architectures for power electronic converters, offering a balance between computational efficiency and control performance.

This paper studies the application of Reinforcement Learning (RL) techniques for path planning of Skid-Steer Mobile Robots (SSMRs) operating in complex environments with obstacles and terrain constraints. With previously recognized characteristics of traditional RL techniques, the path-planning strategy is designed using Q-Learning (QL), Deep Q Networks (DQN), and Deep Deterministic Policy Gradient (DDPG) techniques. The proposed strategy addresses challenges such as large navigation maps, high-dimensional state spaces, static/dynamic obstacles, and wheel-terrain interactions in slip conditions. By integrating information from navigation maps, range sensor data, and robot kinematics, the traction and turning actions of SSMRs are controlled. The proposed strategy was first trained with an SSMR dynamic model designed to characterize real-world conditions, simulated in environments similar to open-pit mines, and then field-tested on a Cat®262C loader. Through several trials, it was demonstrated that the tested RL algorithms successfully plan reachable paths and achieve robust performance. In particular, QL and DQN approaches demonstrated effectiveness while maneuvering on structural regions characterized by predictable workspace. Conversely, DDPG excelled in adapting to changing slippery scenarios, achieving an improved success rate of 98.3%, an average path efficiency of 83.8%, and enhanced learning with consistent cumulative reward larger than that compared to QL and DQN. These findings are expected to contribute to energy resource savings in robots operating along optimized paths in mining environments.

Power converters are specialized systems that transform electrical energy within industrial application. Although advances in conventional control systems have driven increasingly optimal power converter operation, the efficiency of current topologies can be reduced significantly due to their intricate nature, susceptibility to disturbances, and non-linear characteristics. In this regard, Machine Learning algorithms emerge as advanced techniques to increase the robustness to disturbances of model-based control systems. This work presents a model-free control framework based on a Deep Neural Network that autonomously determines load system parameters. A feedfor-ward neural network has been conceptualized, designed, and constructed using a huge data set of a three-phase voltage source inverter driven by a Linear Algebra-based controller (LABC). Empirical findings reveal that Deep Adaptive Linear Based Algebra Control is able to reduce the Root Mean Square Error from 1.68 A to harmonic ± ±0.29 A and achieve a notable enhancement in total distortion, with a reduction from 5.04% to 1.96% compared to LABC.

The phenotyping of plants is becoming more relevant to effectively managing the expectations associated with a product with certified quality, enhancing profitability, and increasing field and crop productivity. Although several solutions have been proposed to characterize plants in physical and biochemical aspects, the main contributions have been related to costly dedicated instruments. This work presents an instantaneous estimator for plant functional traits by harnessing the harvested power from an electric field energy harvester (EFEH). Specifically, we establish the detected correlation between twenty vegetation indices associated with water content and the opencircuit voltage (VOC) and short-circuit current (ISC) of an EFEH assembled with natural leaves. To this end, several 10×3 cm2EFEHs were assembled using natural leaves sourced from two distinct species: Magnolia Obovata and Ravenala Madagascariensis. Each EFEH underwent a four-stage dehydration process. The primary outcome of this work is the exploration of VOCand ISCto retrieve fuel moisture content (FMC) and equivalent water thickness (EWT) based on machine learning models. The results indicated that the electrical parameter with the highest coefficient of determination was ISC, which presented an R2of up to 0.7691 and 0.7639 to retrieve FMC and EWT, respectively.

Bare-earth extraction in forested regions has been considered challenging because of the lack of ground point information. In these regions, vision systems cannot capture any information about ground points under the canopy. Thus, the challenge of generating a digital terrain model by cameras increases. Nevertheless, one might alleviate ground filtering using vegetation’s features (e.g., chlorophyll). In this regard, this article evaluated two machine-learning approaches [i.e., conditional random field (CRF), artificial neural network (ANN)] for generating digital terrain models when biophysical or biochemical features of vegetation are given. Terrain models were generated from multispectral image-based point clouds. A fivefold cross-validation methodology evaluated the CRF and ANN. The point clouds were retrieved from two study areas at different illumination and flight altitudes. Vegetation features were computed as vegetation indices from the multispectral point clouds. Results suggested that by using these indices, the classification of ground points could be enhanced. In particular, the vegetation indices that yielded the best outcomes were normalized difference vegetation index, green NDVI, and modified chlorophyll absorption reflectance index. Moreover, it was shown that CRF generates elevation models more smoothly than a triangular irregular network method. Thus, a CRF could be promising for classifying ground points in forested regions using geometric and vegetation features from a photogrammetric point cloud.

Additive manufacturing is a disruptive technology that enables the efficient construction of lighter and stronger concrete structures. In general, fabrication procedures are produced by industrial 3D printers, which constantly deposit concrete to ensure high building standards and reduce potential deformations in generated components. However, the deposition rate of building materials remains an empirical and heuristic procedure that depends on prior knowledge of the model. This work introduces a methodology to automatically detect deformations in printed layers by analyzing the 3D characterization of concrete structures. To this end, the performance of a monocular camera, LiDAR, and LiDAR-camera is studied according to point cloud density and 3D map reconstruction. In addition, a portable ground-based system for detecting possible deformations is conceived, manufactured, and experimentally tested. Empirical findings show that the proposed system is capable of detecting printed layer variation with a low error of 0.3%, revealing that the low-cost sensors can be an autonomous and highly reliable solution for deformation detection in concrete structures.