Applications of A.I. in control systems
This Paper Presents a case study of an aircraft central system that can tolerate control surface failures using Semantic control theory. In this application, the semantic control system uses system identification and the control mixer concept to exploit the analytical or control redundancy among the control surfaces and thus accommodate the failures. System identification Is performed using artificial intelligence techniques. With this approach unpredictable faults can also be accomodated, which is normally not realized using the conventional techniques.
Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review
One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.
ADAPTIVE CONTROL FOR AVOIDANCE OR EVASION IN AN UNCERTAIN ENVIRONMENT
For a given class of uncertain dynamical systems we consider the problem of obtaining controllers which assure that the system state avoids a prescribed region. Each controller considered is a combination of a memoryless controller and an adaptive controller. Constructive sufficient conditions are given for the existence of these avoidance controllers. The results are applied to linear systems and pursuit-evasion problems.