Performance objective extraction of optimal controllers: a hippocampal learning approach

Intention inference of autonomous vehicles is crucial to guarantee safety and to mitigate risk. This paper reports a performance objective extraction from expert’s data trajectories for experience transference and to uncover the hidden cost associated to the intent. The algorithm is inspired in the hippocampus learning system for experience exploitation that exhibits the human brain. The hippocampus is responsible of memory and to store past experiences to enable transfer learning and fast convergence.The proposed algorithm extracts, from expert’s data, the performance matrices associated to a hidden utility function using a complementary approach based on an off-policy policy iteration and a matrix extraction inverse reinforcement learning algorithms. Exact performance extraction is obtained by adding a constraint in terms of the measurements of the utility function in a batch-least squares algorithm. Convergence of the proposed approach is verified using Lyapunov recursions. Simulation studies are carried out to demonstrate the effectiveness of the proposed approach

Optimal control of nonlinear systems using experience inference human-behavior learning

Safety critical control is often trained in a simulated environment to mitigate risk. Subsequent migration of the biased controller requires further adjustments. In this paper, an experience inference human-behavior learning is proposed to solve the migration problem of optimal controllers applied to real-world nonlinear systems. The approach is inspired in the complementary properties that exhibits the hippocampus, the neocortex, and the striatum learning systems located in the brain. The hippocampus defines a physics informed reference model of the real-world nonlinear system for experience inference and the neocortex is the adaptive dynamic programming (ADP) or reinforcement learning (RL) algorithm that ensures optimal performance of the reference model. This optimal performance is inferred to the real-world nonlinear system by means of an adaptive neocortex/striatum control policy that forces the nonlinear system to behave as the reference model. Stability and convergence of the proposed approach is analyzed using Lyapunov stability theory. Simulation studies are carried out to verify the approach

Multi-spectral fusion using generative adversarial networks for UAV detection of wild fires

Wild fires are now increasingly responsible for immense ecological damage. Unmanned aerials vehicles (UAVs) are being used for monitoring and early-detection of wild fires. Recently, significant research has been conducted for using Deep Learning (DL) vision models for fire and smoke segmentation. Such models predominantly use images from the visible spectrum, which are operationally prone to large false-positive rates and sub-optimal performance across environmental conditions. In comparison, fire detection using infrared (IR) images has shown to be robust to lighting and environmental variations, but long range IR sensors remain expensive. There is an increasing interest in the fusion of visible and IR images since a fused representation would combine the visual as well as thermal information of the image. This yields significant benefits especially towards reducing false positive scenarios and increasing robustness of the model. However, the impact of fusion of the two spectrum on the performance of fire segmentation has not been extensively investigated. In this paper, we assess multiple image fusion techniques and evaluate the performance of a U-Net based segmentation model on each of the three image representations - visible, IR and fused. We also identify subsets of fire classes that are observed to have better results using the fused representation.European Union funding: 77830

Advancing fault diagnosis in aircraft landing gear: an innovative two-tier machine learning approach with intelligent sensor data management

Revolutionizing aircraft safety, this study unveils a pioneering two-tier machine learning model specifically designed for advanced fault diagnosis in aircraft landing gear systems. Addressing the critical gap in traditional diagnostic methods, our approach deftly navigates the challenges of sensor data anomalies, ensuring robust and accurate real-time health assessments. This innovation not only promises to enhance the reliability and safety of aviation but also sets a new benchmark in the application of intelligent machine-learning solutions in high-stakes environments. Our method is adept at identifying and compensating for data anomalies caused by faulty or uncalibrated sensors, ensuring uninterrupted health assessment. The model employs a simulation-based dataset reflecting complex hydraulic failures to train robust machine learning classifiers for fault detection. The primary tier focuses on fault classification, whereas the secondary tier corrects sensor data irregularities, leveraging redundant sensor inputs to bolster diagnostic precision. Such integration markedly improves classification accuracy, with empirical evidence showing an increase from 95.88% to 98.76% post-imputation. Our findings also underscore the importance of specific sensors—particularly temperature and pump speed—in evaluating the health of landing gear, advocating for their prioritized usage in monitoring systems. This approach promises to revolutionize maintenance protocols, reduce operational costs, and significantly enhance the safety measures within the aviation industry, promoting a more resilient and data-informed safety infrastructure

CKPerrusquia/CPhy-ML: Source Code

This repository provides the data and code of the paper "Uncovering Drone Intentions using Control Physics Informed Machine Learning

Cost inference of discrete-time linear quadratic control policies using human-behaviour learning

In this paper, a cost inference algorithm for discrete-time systems using human-behaviour learning is pro-posed. The approach is inspired in the complementary learning that exhibits the neocortex, hippocampus, and striatum learning systems to achieve complex decision making. The main objective is to infer the hidden cost function from expert's data associated to the hippocampus (off-policy data) and transfer it to the neocortex for policy generalization (on-policy data) in different systems and environments. The neocortex is modelled by a Q-learning and a least-squares identification algorithms for on-policy learning and system identification. The cost inference is obtained using a one-step gradient descent rule and an inverse optimal control algorithm. Convergence of the cost inference algorithm is discussed using Lyapunov recursions. Simulations verify the effectiveness of the approach

Uncovering Drone Intentions using Control Physics Informed Machine Learning: data

This repository provides the data and code of the paper "Uncovering Drone Intentions using Control Physics Informed Machine Learning"UKRI Trustworthy Autonomous Systems Node in Securit

Uncovering Drone Intentions using Control Physics Informed Machine Learning: data

This repository provides the data and code of the paper "Uncovering Drone Intentions using Control Physics Informed Machine Learning