Fast quantum control helps reduce the influence of unavoided disturbances and hence plays a vital role in practical quantum technology and chemical reactions. Instead of optimizing the terminal cost like standard optimal quantum control methods, this paper formulates the problem as a trajectory optimization problem, and implements the sequential quadratic programming algorithm to search for short control fields. The core idea is to minimize the cumulative intermediate error to incentivize early achievement of the designed gate. The numerical result on the Toffoli gate demonstrates the effectiveness of the proposed method.

Sticking valves tend to cause cycles in control systems used in industry, degrading product quality and yield. Many attempts have been made to alleviate the impact of stiction. Mechanical knockers are used with success to knock loose the sticking components. Most other stiction compensation methods attempt to find ways to move the control output by an amount greater than the stiction band, while still getting the valve position as close as possible to the desired position. This paper shows how, instead of overcoming stiction and getting the valve position to the control output, the valve can be moved such that over time, the valve is on average at the correct position, while still moving the valve in increments that are larger than the stiction band.

Robot navigation around humans can be a challenging problem since human movements are hard to predict. Stochastic model predictive control (MPC) can account for such uncertainties and approximately bound the probability of a collision to take place. In this paper, to counteract the rapidly growing human motion uncertainty over time, we incorporate state feedback in the stochastic MPC. This allows the robot to more closely track reference trajectories. To this end the feedback policy is left as a degree of freedom in the optimal control problem. The stochastic MPC with feedback is validated in simulation experiments and is compared against nominal MPC and stochastic MPC without feedback. The added computation time can be limited by reducing the number of additional variables for the feedback law with a small compromise in control performance.

This paper presents a novel approach for estimating the vertical load exerted on the rear axle of agricultural vehicles during fieldwork using tire compression measurements. The system comprises an optical time-of-fight (TOF) sensor integrated into the wheel rim, a microcontroller, and an online identification algorithm. The sensor exhibits minimal measurement error, approximately 1%, which, when combined with effective preprocessing and a piecewise linear regression model fueled by tire pressure data, yields a root mean square error (RMSE) of around 7%, with a maximum drawdown of 3% when considering periodic updates. This technology promises to reduce production costs while enabling optimal tire pressure calibration, thereby ensuring reduced fuel consumption and improved comfort for agricultural vehicle operators.

Warehousing tasks, such as order picking, involve extensive manual materials handling, which can lead to a high physical workload and associated health risks for workers. The goal of this paper is to develop a task assignment decision support model suitable for application in a manually operated warehouse. The model aims to balance the physical workload across the employed order pickers based on an ergonomic indicator, leading to fairer task allocations. The main contribution of this paper is the incorporation of a linearized ergonomic indicator into a typical warehousing task assignment problem that allocates batches to order pickers. The linearized indicator facilitates the ergonomic evaluation during the solution procedure using a commercial solver.

This paper presents the latest results of a methodology applied in 2023 to a group of Control Systems students. This course is included in the Bachelor's Degree in Electrical Engineering. The methodology was initiated with a group of graduate students enrolled in the Discrete Time Systems course in 2019. The methodology includes new learning methods, resources, and new student and educator roles. With emphasis on the academic approach and practical skills, hybrid learning and the creation of spaces for exchange and interaction between lecturer and students. It proposes activities such as virtual laboratory, assignments through mobile applications, self-managed lessons in Moodle and design of a digital controller that exposes students to real processes. The results showed that the methodology helped to integrate basic concepts of control systems and provided opportunities for students to improve their professional profiles.

Drones are aerial robotic platforms that have applications in various domains of work and life. They are well appreciated by young people, especially when using them to play games. Mixing learning and entertainment has proven to be an efficient way of motivating students to better understand scientific curricula. This paper proposes a game development-based approach aiming to promote robotics and artificial intelligence (AI) to prospective engineering students and contribute to their motivation to pursue curricula in these fields. The game considered in this paper is a ”Rock-Paper-Scissors” (RPS) game involving an autonomous drone in interaction with human players. It was implemented in the context of a student project at ESIEA, a graduate school of Engineering in France, in connection with Robotics and AI curricula. The RPS drone has been used as a demonstrator during an open day at ESIEA, as a way of attracting prospective students by showing them how fun and full of learning outcomes projects in these fields can be. The main features of the RPS drone are presented in the paper, as well as elements of the curricula and learning outcomes related to this project. We also provide feedback from prospective students on the open day, and we present a preliminary study of the impact of the proposed approach on student motivation.

Increasing labor costs in retail have intensified the focus on order-fulfillment performance, particularly in warehouse operations. In this context, manual picker-to-parts systems, especially in cold storage environments processing deep-frozen products below -20°C, are of significant interest. Our study explores order picking in such cold storage environments, where products are packed into insulated, roll containers serving as load units. While existing research has explored the effects of product characteristics such as weight, volume, and different load units like pallets from a theoretical perspective, the specific interplay between load unit utilization levels and product volume remains underexplored in practical settings. To address this gap, we conducted a field study focusing on a cold storage of a prominent German brick-and-mortar grocery retailer and analyzed a comprehensive longitudinal dataset comprising 227,274 storage location visits by 29 order pickers in January 2023. Employing a mixed-effects model, we examined the impact of these factors on order picking time (OPT). Our findings revealed a subtle relationship: a 1% increase in load unit utilization level correlates with a 1% increase in OPT, signifying that higher utilization levels negatively impact pickers’ performance. Notably, our analysis also indicated that product volume moderates this effect. Specifically, lower product volumes can mitigate the adverse effects of high load unit utilization, whereas higher product volumes amplify these effects. This research offers valuable insights for warehouse managers, particularly in cold storage settings on finding the balance between OPT and load unit utilization.