In unmanned aerial vehicle (UAV) applications, fast-response collision avoidance is an essential requirement for a safe flight. This applies in particular to real-world settings with constrained hardware performance. With these challenges in mind, the authors have recently presented a collision avoidance strategy based on multiple artificial potential functions (MAPOFs). The strategy overcomes the typical drawbacks of the conventional, popular APOFs approach, such as chattering or deadlock. The main focus of the present paper is on the practical implementation and experimental demonstration of the proposed MAPOF strategy in two real-life outdoor scenarios using a commercial quadrotor and open-source software. The experiments show the effectiveness of the MAPOFs strategy in the presence of stationary obstacles as well as in the presence of another moving UAV. The experiments are complemented by additional simulation results to further illustrate and validate the proposed approach.

In this paper, we present a real-time control strategy for a unmanned aerial vehicle operating under external disturbances, utilizing a Tube-based Model Predictive Control (Tube-MPC) framework integrated with a zonotopic representation. The approach ensures robust control by enforcing a bounded deviation between the real system trajectory and the nominal trajectory. The UAV’s control problem is formulated as a linear MPC for nominal control, and a state feedback controller for disturbance rejection, using zonotopes to compute robust invariant sets. The proposed method is tested on a UAV equipped with a Pixhawk 4 flight controller and Raspberry Pi 5, where the system’s robustness is validated in outdoor experiments under disturbances. The results demonstrate both the system’s performance and safety under real-world conditions.

Based on an available digital twin of a Vero cell culture process composed of a dynamic mechanistic model and a soft sensor, the impact of viral amplification is studied and optimized in the process development environment of Sanofi (Marcy l’Etoile, France). The soft sensor (an extended Kalman filter) uses Raman probe online measurements of biomass and some metabolites to estimate the infection titer and is combined with a model predictive controller that aims to optimize the infection titer while regulating the main substrate concentration level. The setup is validated in simulation, and a first experimental investigation is discussed.

Collision avoidance is a problem largely studied in robotics, particularly in uncrewed aerial vehicle (UAV) applications. The main challenges in this area are hardware limitations, the need for rapid response, and the uncertainty associated with obstacle detection. Artificial potential functions (APOFs) are a prominent method to address these challenges. However, existing solutions lack assurances regarding closed-loop stability and may result in chattering effects. Hence, we propose a high-level control method for static obstacle avoidance based on multiple artificial potential functions (MAPOFs), with a set of switching rules with conditions on the parameter tuning ensuring the stability of the final position. The stability proof is established by analyzing the closed-loop system using tools from hybrid systems theory. Furthermore, we validate the performance of the MAPOF control through simulations and real-life experiments, showcasing its effectiveness in avoiding static obstacles.

Modeling the different dynamics in parallel flow regenerative kilns for the production of quicklime is one of the steps towards obtaining the digital twins of the kilns. This paper shows a data-driven modeling approach based on the dynamic mode decomposition algorithm leveraged with a maximum likelihood method that gives linear and discrete-time models of the system. This allows capturing the transient dynamics of the process for the design of subsequent observers of the unknown dynamics, for the implementation of anomaly detection algorithms and eventual control and optimization of the manufacturing process.

A macroscopic model that considers the shift to lactate consumption is proposed to simulate the therapeutic proteins produced by mammalian cells using human embryonic kidney 293 cells (HEK293-6E) cultures. The macroscopic model contains four macroscopic reactions: (i) substrate consumption and lactate production, (ii) lactate consumption, (iii) biomass death, and (iv) viable biomass maintenance. The maximum likelihood principal component analysis has been used to extract the number of reactions from experimental data collected in four batch experiments. The model parameters and confidence intervals are obtained via nonlinear least-squares identification. The model successfully predicts the dynamic behavior of cell growth and death, substrate consumption (glucose and glutamine), lactate production and consumption, and protein production. In addition, the proposed model structure is also validated as a generalized representation of the mammalian cell cultures that do not present lactate consumption.

This study shows how to combine the concepts of extended dynamic mode decomposition and model predictive control in a decentralized approach to drive interconnected oscillators. To achieve this goal, the predictive power of the decomposition algorithm is used to make an approximation of the state of the system, but also to predict the behavior of the interconnection input that affects a particular subsystem. As the prediction of the extended system captures the dynamics linearly, this approach is suitable for the design of local model predictive controllers such that each subsystem can be driven to a desired state despite not having complete knowledge of the other subsystems.