Search Results (12)

This paper presents a comprehensive framework for optimising vehicle performance, integrating advanced simulation techniques with optimisation methodologies. The aim is to find the best racing line, as well as the optimal combination of parameters and control inputs to make a car as fast as possible around a given track, with a focus on energy deployment and recovery, active torque distribution and active aerodynamics. The problem known as the Minimum Lap Time Problem is solved using optimal control methods and direct collocation. The solution covers the modelling of the track, vehicle dynamics, active aerodynamics, and a comprehensive representation of the powertrain including motor, engine, transmission, and drivetrain components. This integrated simulator allows for the exploration of different vehicle configurations and track layouts, providing insights into optimising vehicle design and vehicle control simultaneously for improved performance and energy efficiency. Test results demonstrate the effect of active torque distribution on performance under various conditions, enhanced energy efficiency and performance through regenerative braking, and the added value of including parameter optimisation within the optimisation framework. Notably, the simulations revealed interesting behaviours similar to lift-and-coast strategies, depending on the importance of energy saving, thereby highlighting the effectiveness of the proposed control strategies. Also, results demonstrate the positive effect of active torque distribution on performance under various conditions, attributed to the higher utilization of available adherence. Furthermore, unlike a simpler single-track model, the optimal solution required fine control of the active aerodynamic systems, reflecting the complex interactions between different subsystems that the simulation can capture. Finally, the inclusion of parameter optimisation while considering all active systems, further improves performance and provides valuable insights into the impact of design choices. Full article

In this paper, we present the development of a non-linear model predictive controller for the trajectory tracking of a quadrotor using the CasADi optimization framework. The non-linear dynamic model of the quadrotor was derived using Newton–Euler equations, and the control algorithm and drone dynamics were wrapped in Matlab. The proposed controller was tested by simulating the tracking of a 3D helical reference trajectory, and its efficiency was evaluated in terms of numerical performance and tracking accuracy. The results showed that the proposed controller leads to faster computational times, approximately 20 times faster than the Matlab toolbox (nlmpc), and provides better tracking accuracy than both the Matlab toolbox and classical PID controller. The robustness of the proposed control algorithm was also tested and verified under model uncertainties and external disturbances, demonstrating its ability to effectively eliminate tracking errors. Full article

This paper presents a nonlinear model predictive control with terminal cost (NMPC–WTC) algorithm and its open/closed-loop system analysis and simulation validation for accurate and stable path tracking of autonomous vehicles. The path tracking issue is formulated as an optimal control problem. In order to improve the squeezing phenomenon of traditional NMPC, a discrete-time nonlinear model predictive controller with terminal cost is then designed, in which the state error of last step is augmented. The cost function of NMPC–WTC consists of two parts: (1) the traditional NMPC cost function responding to tracking errors and controller output, and (2) the augmented terminal cost. The algorithm was implemented on CasADi numerical optimization framework, which is free, open-source and developed for nonlinear optimization. The open-loop and closed-loop simulation results are then presented to demonstrate the improved performance in tracking accuracy and stability compared to traditional model predictive controller. Full article

Preparative chromatography is a well-established operation in chemical and biotechnology manufacturing. Chromatography achieves high separation performances, but often has to deal with the yield versus purity trade-off as the optimization criterium regarding through-put. The initial trade-off is often disturbed by the well-known phenomenon of chromatogram shifts over process lifetime, and has to be corrected by operators via adjustment of peak fraction cutting. Nevertheless, with regard to autonomous operation and batch to continuous processing modes, an advanced process control strategy is needed to identify and correct shifts from the optimal operation point automatically. Previous studies have already presented solutions for batch-to-batch variance and process control options with the aid of rigorous physico-chemical process modeling. These models can be implemented as distinct digital twins as well as statistical process operation data analyzers. In order to utilize such models for advanced process control (APC), the model parameters have to be updated with the aid of inline Process Analytical Technology (PAT) data to describe the actual operational status. This updating process also includes any operational change phenomena that occur, and its relation to their physico-chemical root cause. Typical phenomena are fluid dynamic changes due to packing breakage, channelling or compression as well as mass transfer and phase equilibrium-related separation performance decrease due to adsorbent aging or feed and buffer composition changes. In order to track these changes, an Artificial Neural Network (ANN) is trained in this work. The ANN training is in this first step, based on the simulation results of a distinct and previously experimentally validated process model. The model is implemented in the open source tool CasADi for Python. This allows the implementation of interfaces to process control systems, among others, with relatively low effort. Therefore, PAT signals can easily be incorporated for sufficient adjustment of the process model for appropriate process control. Further steps would be the implementation of optimization routines based on PAT and ANN predictions to derive optimal operation points with the model. Full article

Salt stress is one of the main limiting factors for plant growth and crop yield. Halophytes have been postulated as a new food source since they are able to grow under saline environments and have suitable minerals and bioactive compounds. See fennel Crithmum maritimum L. is a facultative halophyte moderately tolerant to salinity. This study was carried out in order to determine the effect spraying methyl jasmonate (MeJa) on the leaves had on the growth and nutritional quality of NaCl-treated sea fennel plants grown in a hydroponic system. For that, the seedlings were treated with (a) 0.5 mM MeJa, (b) 150 mM NaCl, and (c) 0.5 mM MeJa + 150 mM NaCl. The results showed that NaCl reduced the shoot biomass of baby leaf plants, but the addition of MeJa enabled partial recovery. At the same time, when compared with the plants treated only with NaCl, MeJa favoured the Ca and K uptake and translocation to the leaves of saline-treated plants. However, MeJa did not reduce Na levels. In all treatments, nitrate and nitrite ions were in the range of the acceptable daily intake (ADI) and essential fatty acid content was elevated, although the addition of MeJa to NaCl-treated plants reduced linolenic and linoleic acid contents as compared to the plants treated only with NaCl. Total phenolic compounds were not recovered by MeJa after their decrease by salinity and no differences in antioxidant activity was found between treatments. However, all the plants maintained their antioxidant nutritional properties and increased total flavonoids after MeJa spraying to NaCl-treated plants. These results showed that MeJa spraying alleviated the negative effects of salt stress in C. maritimum grown in floating systems, improving the growth of their edible parts and increasing the total flavonoid and mineral content without affecting the total antioxidant capacity of the plant. Full article

The use of arginine deiminase (ADI) for arginine depletion therapy is an attractive anticancer approach. Combination strategies are needed to overcome the resistance of severe types of cancer cells to this monotherapy. In the current study, we report, for the first time, that the antioxidant N-acetylcysteine (NAC), which has been used in therapeutic practices for several decades, is a potent enhancer for targeted therapy that utilizes arginine deiminase. We demonstrated that pegylated arginine deiminase (ADI-PEG 20) induces apoptosis and G0/G1 phase arrest in murine MC38 colorectal cancer cells; ADI-PEG 20 induces Ca²⁺ overload and decreases the mitochondrial membrane potential in MC38 cells. ADI-PEG 20 induced the most important immunogenic cell death (ICD)-associated feature: cell surface exposure of calreticulin (CRT). The antioxidant NAC enhanced the antitumor activity of ADI-PEG 20 and strengthened its ICD-associated features including the secretion of high mobility group box 1 (HMGB1) and adenosine triphosphate (ATP). In addition, these regimens resulted in phagocytosis of treated MC38 cancer cells by bone marrow-derived dendritic cells (BMDCs). In conclusion, we describe, for the first time, that NAC in combination with ADI-PEG 20 not only possesses unique cytotoxic anticancer properties but also triggers the hallmarks of immunogenic cell death. Hence, ADI-PEG 20 in combination with NAC may represent a promising approach to treat ADI-sensitive tumors while preventing relapse and metastasis. Full article

Calculating an open-channel steady flow is of main interest in many situations; this includes defining the initial conditions for the unsteady simulation or the computation of the water level for a given discharge. There are several applications that require a very short computation time in order to envisage a large number of runs, for example, uncertainty analysis or optimization. Here, an optimized algorithm was implemented for the fast and efficient computation of a 1-D steady flow. It merges several techniques: a pseudo-time version of the Saint-Venant equations, an evolutionary domain and the use of a non-linear Krylov accelerator. After validation of this new algorithm, we also showed that it performs well in scalability tests. The computation cost evolves linearly with the number of nodes. This was also corroborated when the execution time was compared to that obtained by the non-linear solver, CasADi. A real-world example using a 9.5 km stretch of river confirmed that the computation times were very short compared to a standard time-dependent computation. Full article

This paper presents an obstacle-avoidance trajectory tracking method based on a nonlinear model prediction, with a dynamic environment considered in the trajectory tracking of nonholonomic mobile robots for obstacle avoidance. In this method, collision avoidance is embedded into the trajectory tracking control problem as a nonlinear constraint of the position state, which changes with time to solve the obstacle-avoidance problem in dynamic environments. The CasADi toolkit was used in MATLAB to generate a real-time, efficient C++ code with inequality constraints to avoid collisions. Trajectory tracking and obstacle avoidance in dynamic and static environments are trialed using MATLAB and CasADi simulations, and the effectiveness of the proposed control algorithm is verified. Full article

This study is aimed at exploring the mechanism underlying the homeostasis between myogenesis and adipogenesis in skeletal muscle using a special porcine model with a distinct phenotype on muscle growth rate and intramuscular fat deposition. Differentiation potential of muscle-derived Myo-lineage cells of lean-type pigs was significantly enhanced relative to obese-type pigs, while that of their Adi-lineage cells was similar. Single-cell RNA sequencing revealed that lean-type pigs reserved a higher proportion of Myo-lineage cells in skeletal muscle relative to obese-type pigs. Besides, Myo-lineage cells of the lean-type pig settled closer to the original stage of muscle-derived progenitor cells. Proteomics analysis found that differentially expressed proteins between two sources of Myo-lineage cells are mainly involved in muscle development, cell proliferation and differentiation, ion homeostasis, apoptosis, and the MAPK signaling pathway. The regulation of intracellular ion homeostasis, Ca²⁺ in particular, significantly differed between two sources of Myo-lineage cells. Ca²⁺ concentration in both cytoplasm and endoplasmic reticulum was lower in Myo-lineage cells of lean-type pigs relative to obese-type pigs. In conclusion, a higher proportion and stronger differentiation capacity of Myo-lineage cells are the main causes for the higher capability of myogenic differentiation and lower intramuscular fat deposition. Relative low concentration of cellular Ca²⁺ is advantageous for Myo-lineage cells to keep a potent differentiation potential. Full article

This paper presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). The aim of the proposed method is to obtain a solution for the automatic identification and assessment of different power quality condition levels that may be caused by different working conditions of an observed electrical power network (EPN). CA is used for identifying the period when the power quality data represents a different level. GPQIs are proposed to calculate a simplified assessment of the power quality condition of the data collected using CA. Two proposed global power quality indices have been introduced for this purpose, one for 10-min aggregated data and the other for events—the aggregated data index (ADI) and the flagged data index (FDI), respectively. In order to investigate the advantages and disadvantages of the proposed method, several investigations were performed, using real measurements in an electrical power network with distributed generation (DG) supplying the copper mining industry. The investigations assessed the proposed method, examining whether it could identify the impact of DG and other network working conditions on power quality level conditions. The obtained results indicate that the proposed method is a suitable tool for quick comparison between data collected in the identified clusters. Additionally, the proposed method is implemented for the data collected from many measurement points belonging to the observed area of an EPN in a simultaneous and synchronous way. Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multiparameter analysis of power quality data addressed to particular measurement points. Full article

Lifting is a frequently used offshore operation. In this paper, a nonlinear model predictive control (NMPC) scheme is proposed to overcome the sudden peak tension and snap loads in the lifting wires caused by lifting speed changes in a wind turbine blade lifting operation. The objectives are to improve installation efficiency and ensure operational safety. A simplified three-dimensional crane-wire-blade model is adopted to design the optimal control algorithm. A crane winch servo motor is controlled by the NMPC controller. The direct multiple shooting approach is applied to solve the nonlinear programming problem. High-fidelity simulations of the lifting operations are implemented based on a turbulent wind field with the MarIn and CaSADi toolkit in MATLAB. By well-tuned weighting matrices, the NMPC controller is capable of preventing snap loads and axial peak tension, while ensuring efficient lifting operation. The performance is verified through a sensitivity study, compared with a typical PD controller. Full article

We present the open-source software framework in JModelica.org for numerically solving large-scale dynamic optimization problems. The framework solves problems whose dynamic systems are described in Modelica, an open modeling language supported by several different tools. The framework implements a numerical method based on direct local collocation, of which the details are presented. The implementation uses the open-source third-party software package CasADi to construct the nonlinear program in order to efficiently obtain derivative information using algorithmic differentiation. The framework is interfaced with the numerical optimizers IPOPT and WORHP for finding local optima of the optimization problem after discretization. We provide an illustrative example based on the Van der Pol oscillator of how the framework is used. We also present results for an industrially relevant problem regarding optimal control of a distillation column. Full article