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See detailAssessing the performance of coordinated predictive control strategies on urban-motorway networks
Rinaldi, Marco UL; Viti, Francesco UL

in IFAC-PapersOnLine (2018, July), 51(9), 285-290

Coordination and integration of different traffic control policies have been of considerable interest in research in the last decades and, recently, have been object of large scale implementation trials ... [more ▼]

Coordination and integration of different traffic control policies have been of considerable interest in research in the last decades and, recently, have been object of large scale implementation trials. In the setting of peri-urban motorway systems, coordination of various kinds of controllers must however be accompanied by accurate prediction of both propagation of flows and queues in the network, as well as the users’ response in terms of route choice. In this paper, we showcase through a real-life case study how coordination and prediction are both essential when performing hybrid urban-motorway control. Simulation results of a Model Predictive Control application are compared to simpler local control approaches, and the impact of coordinated intersection control and, additionally, Ramp Metering is evaluated. [less ▲]

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See detailNonlinear Model Predictive Control of an Oil Well with Echo State Networks
Jordanou, Jean Panaioti; Camponogara, Eduardo; Antonelo, Eric Aislan UL et al

in IFAC-PapersOnLine (2018), 51

In oil production platforms, processes are nonlinear and prone to modeling errors, as the flowregime and components are not entirely known and can bring about structural uncertainties,making designing ... [more ▼]

In oil production platforms, processes are nonlinear and prone to modeling errors, as the flowregime and components are not entirely known and can bring about structural uncertainties,making designing predictive control algorithms for this type of system a challenge. In thiswork, an efficient data-driven framework for Model Predictive Control (MPC) using Echo StateNetworks (ESN) as prediction model is proposed. Differently from previous work, the ESN model for MPC is only linearized partially: while the free response of the system is kept fullynonlinear, only the forced response is linearized. This MPC framework is known in the literatureas the Practical Nonlinear Model Predictive Controller (PNMPC). In this work, by using theanalytically computed gradient from the ESN model, no finite difference method to compute derivatives is needed as in PNMPC. The proposed method, called PNMPC-ESN, is applied tocontrol a simplified model of a gas lifted oil well, managing to successfully control the plant,obeying the established constraints while maintaining setpoint tracking. [less ▲]

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See detailOn the Unknown Input Functional Observer Design via Polytopic Lyapunov Function: Application to a Quadrotor Aerial Robots Landing
Bezzaoucha, Souad UL; Voos, Holger UL; Darouach, Mohamed

in IFAC-PapersOnLine (2017, July)

In this paper, a constructive procedure to design functional unknown input observer for nonlinear continuous time systems under the Polytopic Takagi-Sugeno framework (also known as multiple models systems ... [more ▼]

In this paper, a constructive procedure to design functional unknown input observer for nonlinear continuous time systems under the Polytopic Takagi-Sugeno framework (also known as multiple models systems) is proposed. Applying the Lyapunov theory, Linear Matrix Inequalities (LMI)s conditions are deduced which are solved for feasibility to obtain observer design matrices. To reject the effect of unknown input, classical approach of decoupling the unknown input for the linear case is used. A comparative study between single and Polytopic Lyapunov function is made in order to prove the relaxation effect of the Multiple functions. A solver based solution is then proposed. It will be shown through applicative example (a Quadrotor Aerial Robots Landing) that even if the proposed LMIs solver based solution may look conservative, an adequate choice of the solver makes it suitable for the application of the proposed approach. [less ▲]

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See detailThermodynamically constrained averaging theory for cancer growth modelling
Albrecht, Marco UL; Sciumè, Giuseppe; Lucarelli, Philippe UL et al

in IFAC-PapersOnLine (2016), 49(26), 289-294

In Systems Biology, network models are often used to describe intracellular mechanisms at the cellular level. The obtained results are difficult to translate into three-dimensional biological systems of ... [more ▼]

In Systems Biology, network models are often used to describe intracellular mechanisms at the cellular level. The obtained results are difficult to translate into three-dimensional biological systems of higher order. The multiplicity and time dependency of cellular system boundaries, mechanical phenomena and spatial concentration gradients affect the intercellular relations and communication of biochemical networks. These environmental effects can be integrated with our promising cancer modelling environment, that is based on thermodynamically constrained averaging theory (TCAT). Especially, the TCAT parameter viscosity can be used as critical player in tumour evolution. Strong cell-cell contacts and a high degree of differentiation make cancer cells viscous and support compact tumour growth with high tumour cell density and accompanied displacement of the extracellular material. In contrast, dedifferentiation and losing of cell-cell contacts make cancer cells more fluid and lead to an infiltrating tumour growth behaviour without resistance due to the ECM. The fast expanding tumour front of the invasive type consumes oxygen and the limited oxygen availability behind the invasive front results automatically in a much smaller average tumour cell density in the tumour core. The proposed modelling technique is most suitable for tumour growth phenomena in stiff tissues like skin or bone with high content of extracellular matrix. [less ▲]

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See detailOptimising time-series experimental design for modelling of circadian rhythms: the value of transient data
Mombaerts, Laurent UL; Mauroy, Alexandre UL; Goncalves, Jorge UL

in IFAC-PapersOnLine (2016, October)

Circadian clocks consist of complex networks that coordinate the daily cycle of most organisms. In light/dark cycles, the clock is synchronized (or entrained) by the environment, which corresponds to a ... [more ▼]

Circadian clocks consist of complex networks that coordinate the daily cycle of most organisms. In light/dark cycles, the clock is synchronized (or entrained) by the environment, which corresponds to a constant rephasing of the oscillations and leads to a steady state regime. Some circadian clocks are endogenous oscillators with rhythms of about 24-hours that persist in constant light or constant darkness. This operating mechanism with and without entrainment provides flexibility and robustness to the clock against perturbations. Most of the clock-oriented experiments are performed under constant photoperiodic regime, overlooking the transitory regime that takes place between light/dark cycles and constant light or darkness. This paper provides a comparative analysis of the informative potential of the transient time-series data with the other regimes for clock modelling. Realistic data were simulated from 2 experimentally validated plant circadian clock models and sliced into several time windows. These windows represent the different regimes that take place before, meanwhile and after the switch to constant light. Then, a network inference tool was used over each window and its capability of retrieving the ground-truth of the network was compared for each window. The results suggest that including the transient data to the network inference technique significally improves its performance. [less ▲]

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See detailSystem Identification of a Vertical Riser Model with Echo State Networks
Antonelo, Eric Aislan UL; Camponogara, Eduardo; Plucenio, Agustinho

in IFAC-PapersOnLine (2015), 48(6), 304-310

System identification of highly nonlinear dynamical systems, important for reducing time complexity in long simulations, is not trivial using more traditional methods such as recurrent neural networks ... [more ▼]

System identification of highly nonlinear dynamical systems, important for reducing time complexity in long simulations, is not trivial using more traditional methods such as recurrent neural networks (RNNs) trained with back-propagation through time. The recently introduced Reservoir Computing (RC)∗∗The term reservoir used here is not related to reservoirs in oil and gas industry. approach to training RNNs is a viable and powerful alternative which renders fast training and high performance. In this work, a single Echo State Network (ESN), a flavor of RC, is employed for system identification of a vertical riser model which has stationary and oscillatory signal behaviors depending of the production choke opening input variable. It is shown experimentally that these different behaviors are learned by constraining the high-dimensional reservoir states to attractor subspaces in which the specific behavior is represented. Further experiments show the stability of the identified system. [less ▲]

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See detailA discrete/continuous numerical approach to multi-physics
Peters, Bernhard UL; Besseron, Xavier UL; Estupinan Donoso, Alvaro Antonio UL et al

in IFAC-PapersOnLine (2015), 28(1), 645-650

A variety of technical applications are not only the physics of a single domain, but include several physical phenomena, and therefore are referred to as multi-physics. As long as the phenomena being ... [more ▼]

A variety of technical applications are not only the physics of a single domain, but include several physical phenomena, and therefore are referred to as multi-physics. As long as the phenomena being taken into account is either continuous or discrete i.e. Euler or Lagrangian a homogeneous solution concept can be employed. However, numerous challenges in engineering include continuous and discrete phase simultaneously, and therefore cannot be solved only by continuous or discrete approaches. Problems include both a continuous and a discrete phase are important in applications of the pharmaceutical Industry e.g. drug production, agriculture and food processing industry, mining, construction and Agricultural machinery, metal production, power generation and systems biology. The Extended Discrete Element Method (XDEM) is a novel technique, which provides a significant advance for the coupled discrete and continuous numerical simulation concepts. It expands the dynamics of particles as described by the classical discrete element method (DEM) by a thermodynamic state or stress/strain coupled as fluid flow or structures for each particle in a continuum phase. XDEM additionally estimates properties such as the interior temperature and/or species distribution. These predictive capabilities are extended to fluid flow through an interaction by heat, mass and momentum transfer important for process engineering. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. [less ▲]

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See detailContinuous Glucose Monitoring: Using CGM to Guide Insulin Therapy Virtual Trials Results
Mombaerts, Laurent UL; Thomas, Felicity; Signal, Matthew et al

in IFAC-PapersOnLine (2015)

Continuous glucose monitoring (CGM) devices can measure blood glucose levels through interstitial measurements almost continuously (1-5min sampling period). However, they are not as accurate as glucose ... [more ▼]

Continuous glucose monitoring (CGM) devices can measure blood glucose levels through interstitial measurements almost continuously (1-5min sampling period). However, they are not as accurate as glucose readings from blood measurements. The relation between tissue and blood glucose is dynamic and the sensor signal can degrade over time. In addition, CGM readings contains high frequency noise and can drift between measurements. However, maintaining continuous glucose monitoring has the potential to improve the level of glycemic control achieved and reduce nurse workload. For this purpose, a simple model was designed and tested to see the effect of inherent CGM error on the insulin therapy protocol, STAR (Stochastic TARgeted). An error model was generated from 9 patients that had one Guardian Real-Time CGM device (Medtronic Minimed, Northridge, CA, USA) inserted into their abdomen as part of an observation trial assesing the accuracy of CGM measurements compared to a blood gas analyser and glucometer readings. A resulting error model was then used to simulate the outcomes if the STAR protocol was guided by CGM values on 183 virtual patients. CGM alarms for hyper- and hypo-glycaemic region were included to improve patient safety acting as 'guardrails'. The STAR CGM protocol gave good performance and reduced workload by ~50%, reducing the number of measurements per day per patient from 13 to 7. The number of hypoglycaemic events increased compared to the current STAR from 0.03% <2.2mmol/L to 0.32%. However, in comparison to other published protocols it is still a very low level of hypoglycaemia and less than clinically acceptable value of 5% <4.0mmol/L. More importantly this study shows great promise for the future of CGM and their use in clinic. With the a newer generation of sensors, specifically designed for the ICU, promising less noise and drift suggesting that a reduced nurse workload without compromising safety or performance is with in reach. [less ▲]

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