IFAC-PapersOnLine最新文献

While designing excitation signals for identification of industrial processes, it is important to obtain desired model accuracies, reduce the experimental time and limit the output amplitudes within the specified bounds to avoid serious disruptions of the nominal process operation. In this work, we design a multi-frequency multi-amplitude square wave (multi-square) input based on a nominal model by minimizing the experiment length and placing constraints on the model accuracy (in the frequency domain) and the output amplitudes. A separate design is carried out for each input where the resulting optimization problem has the same structure as a semi-definite program but with the decision variables restricted to integers corresponding to the number of half-periods of each square-wave. For processes with multiple inputs, the corresponding designs are carried out sequentially. The violations in the output constraints either due to model-plant mismatch or unmeasured disturbances should be mitigated by appropriate closed loop control actions. The efficacy of the proposed design is shown by means of a simulation case study.

Differentiating between various types of faults and classifying them based on their importance is essential for process fault detection and diagnosis. This classification helps operators to prioritize their actions based on the severity of the faults. This paper proposes a reservoir computing-based slow feature analysis (RCSFA) to model complex and nonlinear industrial processes and study its application in fault classification while integrated with a graph neural network (GNN) and majority voting ensemble causality detection. To make the algorithm robust to unseen faults, real-time operator feedback is included by utilizing operator eye tracking. The practical applicability of the proposed method and its application in fault classification is studied through an industrial application.

Over the years, hundreds of applications have proved the effectiveness of constraint-based methods to validate the definition of metabolic networks, determine the robustness of metabolic models, and analyze the flow of metabolites through a network. However, stoichiometric models do not include information on flux capacity via enzymatic activity. Methods combining biological data from genome to metabolome have been developed to obtain improved flux predictions and constrain the range of possible flux distributions. Yet, these models still lack relevant information to design de novo metabolic pathways. Expressing the exogenous enzymes induces a cell burden due to competition for cell resources between the exogenous genes and the endogenous host ones, compromising the performance of the designed pathway. Thus, optimal selection of the expression strength of the pathway enzymes is still a challenge. Host-aware models have been developed to tackle cell burden in the context of designing increasingly complex synthetic genetic circuits in synthetic biology. This paper suggests a method to integrate host-aware gene expression models with constraint-based modeling to maximize the flux through an exogenous pathway by optimizing promoter and ribosome binding site strengths, crucial parameters that define the required transcription and translation strengths of the pathway enzymes’ genes. This study considers the formation of p-coumaric acid, shows promising results, and paves the way for further investigations.

This paper presents a hierarchical MPC-based control framework for a real microgrid including solar panels and batteries, that considers the uncertainty from the point of view of faults and risks (F&R) mitigation. While fault management is applied during plant operation, risk management considers external factors that can change microgrid planning in the medium-long term. Due to their different time-scales, a two-layer control scheme is proposed using Model Predictive Control (MPC) at both levels. At the bottom layer, the fault-tolerant predictive controller optimizes the operation by manipulating inputs to follow microgrid set-points. A reconfiguration strategy is implemented using structured residuals and stochastic thresholds. On the other hand, the upper layer develops an optimal mitigation strategy, also based on MPC, to reduce the effects of risks obtained from external information, i.e., unexpected changes in demands, maintenance costs, or deviations in generation. The decision variables of this layer are the selection of mitigation actions to be undertaken, which minimise a proposed multicriteria objective function. different simulations have been carried out to show the efficacy of this methodology in a F&R scenario from a stochastic point of view.

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.

A two-degree-of-freedom Twin Rotor MIMO (Multiple-Input-Multiple-Output) System (TRMS) is an aerodynamic laboratory equipment at the School of Engineering, University of Leicester for control theory experimentation in the undergraduate (UG) curriculum, Open-Days (ODs), Offer-Holder-Days (OHDs) and research. It is crucial in demonstrating system modelling, simulation, real-time testing, open/closed loop control, and controller design (Proportional-Integral-Derivative, Linear Quadratic Regulator, Model Predictive Control). Feedback received indicates that TRMS experiments have successfully attracted many candidates at ODs/OHDs to the UG aerospace engineering degree programme while giving current students a sense of real-world applicability. Opportunities to further enrich the UG curriculum are explored.

This paper describes the Sensor Cube, an open-source hardware and software system for hands-on learning of sensor data processing. Various sensors, such as camera, GNSS, and inertial sensors, are included to teach sensor data analysis and processing. The sensor system is designed to work with popular operating systems without special drivers, and the data format is human-readable. Therefore, all sensor data is easily accessible through Python or C++ code without the need to learn an additional framework. In addition, the hardware design, firmware, and code examples for working with the sensor data are available as open-source.

The aim of the present study is to formulate a model that describes the dynamics of university students on the basis of continuous time differential equations and Petri Nets. Students are modeled by continuous time functions that represent their ability to deal with theoretical concepts and put them into practice. In addition, the curriculum is seen as a set of activities that students can select according to their willingness. The application of the model to public data of aerospace engineering students will be the subject of future work.