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The objective of this study was to assess the changes in meat colour over time in Aubrac cattle of both genders. Specifically, the study concentrated on Longissimus Dorsi muscles, examining the colour values of the meat at 0, 24, and 48 hours after death. The development of the CIE Lab* colour space allows for the expression of colours in a three-dimensional system. The L*, a*, and b* values represent lightness, the red-green colour component, and the yellow-blue colour component, respectively. These values enable the evaluation and description of the colour of a meat sample. Taking into account the overall obtained results (example: the meat colour saturation recorded an average value of 14.28 ± 1.11 in males, and 17.72 ± 2.76 in females), we can appreciate that in the Aubrac cattle breed, there are significant differences between males and females, concerning all analysed parameters regarding colour (brightness, hue intensity, colour intensity of the meat, colour saturation, and Hue index) observed in the anatomical region under study.

On the 23rd of September 2022, a small intensive aquaculture unit populated with rainbow trout (Oncorhynchus mykiss) reported increased mortality in adults and juvenile fish. The unit comprised 12 enclosed concrete basins with a capacity of ten cubic meters of water, populated with 150 kg of fish each. Fish were subjected to a clinical examination on the site, after which whole fish were harvested for a bacteriological and histopathological examination. Water quality parameters were examined using classic biochemical methods and Fourier Transform Infrared Spectroscopy in order to find out whether the environment in which the fish live is also a predisposing factor that could facilitate different pathogens and induce a state of disease in the fish. Real-time PCR was performed on strains of Aeromonas spp. sampled from the fish to accurately identify the pathogen species. The goal was to accurately identify the problems and predisposing factors that lead to disease outbreaks.

Abstract This paper proposes a model predictive control (MPC) approach for non‐linear systems where the non‐linear dynamics are embedded inside a linear parameter‐varying (LPV) representation. The non‐linear MPC problem is therefore replaced by an LPV MPC problem, without using linearization. Compared to general non‐linear MPC, advantages of this approach are that it allows for the tractable construction of a terminal set and cost, and that only a single convex program must be solved online. The key idea that enables proving recursive feasibility and stability, is to restrict the state evolution of the non‐linear system to a time‐varying sequence of state constraint sets. Because in LPV embeddings, there exists a relationship between the scheduling and state variables, these state constraints are used to construct a corresponding future scheduling tube. Compared to non‐time‐varying state constraints, tighter bounds on the future scheduling trajectories are obtained. Computing a scheduling tube in this setting requires applying a non‐linear function to the sequence of constraint sets. Outer approximations of this non‐linear projection‐based scheduling tube can be found, e.g., via interval analysis. The computational properties of the approach are demonstrated on numerical examples.

Modular Multilevel Converters (MMCs) are a topology that can scale several voltage levels to obtain higher efficiency and lower harmonics than most voltage-source converters. MMCs are very attractive for renewable energy applications and fast charging stations for electric vehicles, where they can improve performance and reduce costs. However, due to the complex architecture and the large number of submodules, the current control of modular multilevel converters is a challenging task. The standard solution in practice relies on hierarchical decoupling and single-input-single-output control loops, which are limited in performance. Linearization-based model predictive control was already proposed for current control in MMCs, as it can optimize transient response and better handle constraints. In this paper, we show that the validity of linear MMC models significantly limits the prediction horizon length, and we propose a nonlinear MPC (NMPC) solution for current control in MMCs to solve this issue. With NMPC, we can employ long prediction horizons up to 100 compared to a horizon of 10, which is the limit for the prediction range of a linear MMC model. Additionally, we propose an alternative MMC prediction model and corresponding cost function, which enables directly controlling the circulating current and improves the capacitor voltages’ behavior. Using the state-of-the-art in sequential quadratic programming for NMPC, we show that the developed NMPC algorithm can meet the real-time constraints of MMCs. A performance comparison with a time-varying linearization-based MPC for an MMC topology used in ultra-fast charging stations for electric vehicles illustrates the benefits of the developed approach.

This paper proposes a predictive driver coaching (PDC) system for fuel economy driving for hybrid electric trucks using upcoming static map and dynamic traffic data. Unlike traditional methods that optimize over engine torque and brake to obtain a speed profile, we propose to optimize over driving modes of trucks to achieve a trade-off between fuel consumption and trip time. The optimal driving mode is provided to the driver as a coaching recommendation. To obtain the optimal solution, the truck dynamics are firstly modeled as a hybrid controlled switching dynamical system with autonomous subsystems and then a hybrid optimal control problem (HOCP) is formulated. The problem is solved using an algorithm based on discrete hybrid minimum principle. A warm-start strategy to reduce algorithmic iterations is used by employing a shrinking horizon strategy. In addition, an extensive analysis of the proposed algorithm is provided. We prove that the the coasting mode is never optimal given the truck configuration and and we provide a guideline for tuning parameters to maintain the optimal mode sequence. Finally, the algorithm is validated using real-world data from baseline driving tests using a DAF hybrid truck. Significant reduction in fuel consumption is achieved when the data is perfectly available.

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 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.