MASSCLEAN - MASSive CLuster Evolution and ANalysis Package - Description, Tests, and Results

MASSCLEAN is a new, sophisticated and robust stellar cluster image and photometry simulation package. This package is able to create color-magnitude diagrams and standard FITS images in any of the traditional optical and near-infrared bands based on cluster characteristics input by the user, including but not limited to distance, age, mass, radius and extinction. At the limit of very distant, unresolved clusters, we have checked the integrated colors created in MASSCLEAN against those from other simple stellar population (SSP) models with consistent results. Because the algorithm populates the cluster with a discrete number of tenable stars, it can be used as part of a Monte Carlo Method to derive the probabilistic range of characteristics (integrated colors, for example) consistent with a given cluster mass and age. We present the first ever mass dependent integrated colors as a function of age, derived from over 100,000 Monte Carlo runs, which can be used to improve the current age determination methods for stellar clusters.Comment: 4 pages, 5 figures, Proceedings International Astronomical Union Symposium No. 266, Star Clusters - Basic Galactic Building Blocks throughout Time and Space, Editors: Richard de Grijs & Jacques R. D. Lepin

Lateral Wind Estimation and Backstepping Compensation for Safer Self-Driving Racecars

This paper addresses the lateral wind gust estimation and compensation problem for racecar models. A wind-sensorless solution, i.e. a solution not using direct wind measures, is proposed. More precisely, by modeling the wind disturbance as a fully unknown input signal, an input-state observer is derived using only information about the vehicle’s longitudinal speed and lateral pose relative to the road. The observer is characterized by a simple structure, explicit closed-form, direct implementability on a micro-controller, and dead-beat property, i.e. it ensures the convergence of the estimation error in a finite time. Moreover, leveraging on the reconstructed wind data, a backstepping wind-compensation controller is also proposed, allowing asymptotic tracking of a path with desired curvature and providing the end-user with a free control parameter specifying the desired tracking speed. Formal proofs of the estimation error and tracking error convergence are given. Performance evaluation of the proposed solution is obtained in simulation by closing in the loop the full nonlinear model of a real racecar, the Robocar system, with the proposed estimation and control method. Both the estimator and the controller are shown to outperform existing solutions, even in the presence of noisy measurements

Block-Based Models and Theorem Proving in Model-Based Development

This paper presents a methodology to integrate computer-assisted theorem proving into a standard workflow for model-based development that uses a block-based language as a modeling and simulation tool. The theorem prover provides confidence in the results of the analysis as it guides the developers towards a correct formalization of the system under development

Design and Validation of Cyber-Physical Systems Through Co-Simulation: The Voronoi Tessellation Use Case

This paper reports on the use of co-simulation techniques to build prototypes of co-operative autonomous robotic cyber-physical systems. Designing such systems involves a mission-specific planner algorithm, a control algorithm to drive an agent performing its task; and the plant model to simulate the agent dynamics. An application aimed at positioning a swarm of unmanned aerial vehicles (drones) in a bounded area, exploiting a Voronoi tessellation algorithm developed in this work, is taken as a case study. The paper shows how co-simulation allows testing the complex system at the design phase using models created with different languages and tools. The paper then reports on how the adopted co-simulation platform enables control parameters calibration, by exploiting design space exploration technology. The INTO-CPS co-simulation platform, compliant with the Functional Mock-up Interface standard to exchange dynamic simulation models using various languages, was used in this work. The different software modules were written in Modelica, C, and Python. In particular, the latter was used to implement an original variant of the Voronoi algorithm to tesselate a convex polygonal region, by means of dummy points added at appropriate positions outside the bounding polygon. A key contribution of this case study is that it demonstrates how an accurate simulation of a cooperative drone swarm requires modeling the physical plant together with the high-level coordination algorithm. The coupling of co-simulation and design space exploration has been demonstrated to support control parameter calibration to optimize energy consumption and convergence time to the target positions of the drone swarm. From a practical point of view, this makes it possible to test the ability of the swarm to self-deploy in space in order to achieve optimal detection coverage and allow unmanned aerial vehicles in a swarm to coordinate with each other

Co-simulated digital twin on the network edge: A vehicle platoon

This paper presents an approach to create high-fidelity models suited for digital twin application of distributed multi-agent cyber–physical systems (CPSs) exploiting the combination of simulation units through co-simulation. This approach allows for managing the complexity of cyber–physical systems by decomposing them into multiple intertwined components tailored to specific domains. The native modular design simplifies the building, testing, prototyping, and extending CPSs compared to monolithic simulator approaches. A system of platoon of vehicles is used as a case study to show the advantages achieved with the proposed approach. Multiple components model the physical dynamics, the communication network and protocol, as well as different control software and external environmental situations. The model of the platooning system is used to compare the performance of Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm. Moreover, exploiting the detailed model of vehicle dynamics, different road surface conditions are considered to evaluate the performance of the platooning system. Finally, taking advantage of the co-simulation approach, a solution to drive a platoon in critical road conditions has been proposed. The paper shows how co-simulation and design space exploration can be used for parameter calibration and the design of countermeasures to unsafe situations

Robust motion control of nonlinear quadrotor model with wind disturbance observer

This paper focuses on robust wind disturbance rejection for nonlinear quadrotor models. By leveraging on nonlinear unknown observer theory, it proposes a nonlinear dynamic filter that, using sensors already on-board the aircraft, can estimate in real-time wind gust signals in the three dimensions. The wind disturbance is then treated as input to the PD controller for a quick and robust flight pathway in presence of disturbances. With this scheme, the wind disturbance can be precisely estimated online and compensated in real-time. Hence, the quadrotor can successfully reach its desired attitude and position. To show the effective and desired performance of the method, simulation results are presented in Matlab/Simulink and ROS-enabled Gazebo platform

Precise Trajectory Tracking of Multi-Rotor UAVs Using Wind Disturbance Rejection Approach

This paper discusses the resilience of the UAV quadrotor to wind disturbances. An unknown input-state observer is presented that uses the Lipschitz method to estimate the internal states and disturbances of the quadrotor and compensate for them by varying the velocities of the four rotors. The observer intends to use existing sensor measurements to estimate the unknown states of the quadrotor and reconstruct the three-dimensional wind disturbances. The estimated states and external disturbances are sent to the PD controller, which compensates for the disturbances to achieve the desired position and attitude, as well as robustness and accuracy. The Lipschitz observer was designed using the LMI approach, and the results were validated using Matlab/Simulink and using the Parrot Mambo mini quadrotor

Towards an open database of assessment material for STEM subjects: requirements and recommendations from early field trials

If appropriately implemented, open databases of instruction material may help teaching and learning by providing content for teaching activities, scaffolding, and self-assessment. The paper presents the current results of the development and implementation of a database that is expressly built for promoting exchange of questions and exercises, together with the associated solutions among teachers for STEM subjects. Besides presenting and motivating the initiative (together with reporting its current status), the manuscript lists a series of lessons that have been learned while executing the project - including the need for proper management of authorship and version control of the uploaded material. Moreover, the manuscript describes which features any open database of instruction material should implement to aid improved usability, together with a series of nontrivial theoretical and practical problems for future scientific investigations (e.g., developing taxonomies for indexing the difficulty levels of the instruction material uploaded in the database that do not suffer from the subjective interpretability associated with the existing taxonomies)

Improving Power Delivery of Grid-Connected Induction Machine Based Wind Generators under Dynamic Conditions Using Feedforward Linear Neural Networks

In the conventional grid-connected Wind Energy Conversion System (WECS), the generator side inverter is typically controlled via Field Oriented Control (FOC), while Voltage Oriented Control (VOC) controls the grid side inverter. However, robust operation cannot be guaranteed during sudden changes in wind speeds and weak grid connections. This paper presents a novel method to improve the overall dynamic performance of on-grid induction machine-based wind generators. An online mechanical parameter estimation technique is devised using Recursive Least Squares (RLS) to compute the machine inertia and friction coefficient iteratively. An adaptive feedforward neural (AFN) controller is also proposed in the synchronous reference frame, which is constructed using the estimated parameters and the system's inverse. The output of the neural controller is added to the output of the speed PI controller in the outer loop of the FOC to enhance the speed response of the wind generator. A similar approach is taken to improve the classical VOC structure for the grid-side inverter. In this case, the RLS estimates the equivalent Thevenin's grid impedance in real-time. As for the adaptive action, two identical neural networks are integrated with the inner loop direct and quadrature axis current PI controllers. Under nominal operating conditions, it is observed that the PI+AFN provides a faster settling time for the generator's speed and torque response. Upon being subjected to variations in the wind speed, the PI+AFN outperforms the classical PI controller and attains a lower integral-time error. In addition, the proposed PI+AFN controller has a better ability to maintain the grid-side inverter stability during stochastic variations in grid impedance. One significant advantage of the proposed control approach is that no data for training or validation is required since the neural network weights are directly the output of the RLS estimator. Hardware verification for the improved FOC for wind generators using the adaptive controller is also made using the DSPACE 1007 AUTOBOX platform