Flexible Lyapunov Functions and Applications to Fast Mechatronic Systems

The property that every control system should posses is stability, which translates into safety in real-life applications. A central tool in systems theory for synthesizing control laws that achieve stability are control Lyapunov functions (CLFs). Classically, a CLF enforces that the resulting closed-loop state trajectory is contained within a cone with a fixed, predefined shape, and which is centered at and converges to a desired converging point. However, such a requirement often proves to be overconservative, which is why most of the real-time controllers do not have a stability guarantee. Recently, a novel idea that improves the design of CLFs in terms of flexibility was proposed. The focus of this new approach is on the design of optimization problems that allow certain parameters that define a cone associated with a standard CLF to be decision variables. In this way non-monotonicity of the CLF is explicitly linked with a decision variable that can be optimized on-line. Conservativeness is significantly reduced compared to classical CLFs, which makes \emph{flexible CLFs} more suitable for stabilization of constrained discrete-time nonlinear systems and real-time control. The purpose of this overview is to highlight the potential of flexible CLFs for real-time control of fast mechatronic systems, with sampling periods below one millisecond, which are widely employed in aerospace and automotive applications.Comment: 2 figure

Networked control system – an overview

Networked Control System (NCS) is fetching researchers’ interest from many decades. It’s been used in industry which range from manufacturing, automobile, aviation, aerospace to military. This paper gives the general architecture of NCS and its fundamental routes. It also touches to its advantages and disadvantages and some of the popular controller which include PID (Proportional-Integral-Derivative) and MPC (Model Predictive Control)

Harnessing the Power of Digital Twins for Enhanced Material Behavior Prediction and Manufacturing Process Optimization in Materials Engineering

The advent of Industry 4.0 and the digital revolution have brought forth innovative technologies such as digital twins, which have the potential to redefine the landscape of materials engineering. Digital twins, virtual representations of physical entities, can model and predict material behavior, enabling enhanced design, testing, and manufacturing of materials. However, the comprehensive utilization of digital twins for predictive analysis and process optimization in materials engineering remains largely uncharted. This research intends to delve into this intriguing intersection, investigating the capabilities of digital twins in predicting material behavior and optimizing manufacturing processes, thereby contributing to the evolution of advanced materials manufacturing. Our study will commence with a detailed exploration of the concept of digital twins and their specific applications in materials engineering, emphasizing their ability to simulate intricate material behaviors and processes in a virtual environment. Subsequently, we will focus on exploiting digital twins for predicting diverse material behaviors such as mechanical properties, failure modes, and phase transformations, demonstrating how digital twins can utilize a combination of historical data, real-time monitoring, and sophisticated algorithms to predict outcomes accurately. Furthermore, we will delve into the role of digital twins in optimizing materials manufacturing processes, including casting, machining, and additive manufacturing, illustrating how digital twins can model these processes, identify potential issues, and suggest optimal parameters. We will present detailed case studies to provide practical insights into the implementation of digital twins in materials engineering, including the advantages and challenges. The final segment of our research will address the current challenges in implementing digital twins, such as data quality, model validation, and computational demands, proposing potential solutions and outlining future directions. This research aims to underline the transformative potential of digital twins in materials engineering, thereby paving the way for more efficient, sustainable, and intelligent material design and manufacturing processes

Predictive Control Framework for Thermal Management of Automotive Fuel Cell Systems at High Ambient Temperatures

Environmental conditions have a significant effect on the performance of fuel cell systems. This paper studies the vehicle hydrogen consumption, the thermal management system, and the thermal loads of an automotive fuel cell system. A predictive control framework for thermal management is investigated to minimize the overall hydrogen consumption. Initially, a numerical modeling approach for the automotive fuel cell system is presented from electrochemical and thermal perspectives. Then, the problem formulation related to the thermal management strategy is presented and solved with an optimization method based on dynamic programming (DP). The implemented DP exploits the a priori knowledge of the driving mission to appropriately control the fuel cell system gross power and the operation of the radiator fan, the coolant pump, and the compressor. Optimization constraints involve maintaining the fuel cell stack temperature below the operational limit and avoiding the thermal system from being activated when the vehicle is at rest. The fuel cell system is tested while the vehicle performs different numbers of repetitions of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) at high ambient temperature. Using the proposed predictive control framework for thermal management, results demonstrate that an average 62.5% to 63.0% efficiency can be attained by the fuel cell stack in extreme ambient conditions both in short distance and long distance driving missions

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

ARMD Workshop on Materials and Methods for Rapid Manufacturing for Commercial and Urban Aviation

This report documents the goals, organization and outcomes of the NASA Aeronautics Research Mission Directorates (ARMD) Materials and Methods for Rapid Manufacturing for Commercial and Urban Aviation Workshop. The workshop began with a series of plenary presentations by leaders in the field of structures and materials, followed by concurrent symposia focused on forecasting the future of various technologies related to rapid manufacturing of metallic materials and polymeric matrix composites, referred to herein as composites. Shortly after the workshop, questionnaires were sent to key workshop participants from the aerospace industry with requests to rank the importance of a series of potential investment areas identified during the workshop. Outcomes from the workshop and subsequent questionnaires are being used as guidance for NASA investments in this important technology area

Extremum Seeking-based Iterative Learning Linear MPC

In this work we study the problem of adaptive MPC for linear time-invariant uncertain models. We assume linear models with parametric uncertainties, and propose an iterative multi-variable extremum seeking (MES)-based learning MPC algorithm to learn on-line the uncertain parameters and update the MPC model. We show the effectiveness of this algorithm on a DC servo motor control example.Comment: To appear at the IEEE MSC 201