The short term optimization and control of district heating networks is of great interest for Energy Industries because of the technical, economical and environmental benefits which could be earned from an appropriate management. However, models of such complicated systems are strongly non linear and suffer from important uncertainties. In this article, models well suited to industrial issues are first designed. The whole technological string "production -distributionconsumption" is taken into account. The aim of this study is then to compute an optimal and robust control law for the network. Because of the errors in consumers' demand prediction and modelling uncertainties, a closed loop strategy has to be used to compute a robust control law for the district heating network. In this paper, a robust predictive control strategy of the network is thus developed. The method has been successfully tested on a benchmark network created by EDF ('Electricite de France') and some results are presented here.
In the framework of environment preservation, microalgae biotechnology appears as a promising alternative for CO₂ mitigation. Advanced control strategies can be further developed to maximize biomass productivity, by maintaining these microorganisms in bioreactors at optimal operating conditions. This article proposes the implementation of Nonlinear Predictive Control combined with an on-line estimation of the biomass concentration, using dissolved carbon dioxide concentration measurements. First, optimal culture conditions are determined so that biomass productivity is maximized. To cope with the lack of on-line biomass concentration measurements, an interval observer for biomass concentration estimation is built and described. This estimator provides a stable accurate interval for the state trajectory and is further included in a nonlinear model predictive control framework that regulates the biomass concentration at its optimal value. The proposed methodology is applied to cultures of the microalgae Chlorella vulgaris in a laboratory-scale continuous photobioreactor. Performance and robustness of the proposed control strategy are assessed through experimental results.
This paper proposes a novel strategy for completing a flight plan with a quadrotor UAV, in the context of aerial video making. The flight plan includes different types of waypoints to join, while respecting flight corridors and bounds on the derivatives of the position of the quadrotor. To this aim, non-uniform clamped B-splines are used to parameterize the trajectory. The latter is computed in order to minimize its overall duration, while ensuring the validation of the waypoints, satisfying the flight corridors and respecting the maximum magnitude on its derivatives. A receding waypoint horizon is used in order to split the optimization problem into smaller ones, which reduces the computation load when generating pieces of trajectories. The effectiveness of the proposed trajectory generation technique is demonstrated by simulation and through an outdoor flight experiment on a quadrotor.
The objective of this study is to design a Nonlinear Model Predictive Controller for a microalgae culture process to regulate the biomass concentration at a chosen setpoint. The optimization problem is discretized and transformed into a nonlinear programming problem, solved by Control Vector Parametrization technique. However, the performances of the NMPC usually decrease when the true plant evolution deviates significantly from that predicted by the model. Therefore, a control approach that considers model uncertainty is further considered by adding a system-model error signal which represents the gap between the system output and the model prediction. In order to reduce the influence of measurement noise introduced by sensors and to have a smooth control signal, a penalty term on the control variation is added in the objective function. Finally, the method is validated in simulation and numerical results are given to illustrate the efficiency of the control strategy for setpoint tracking in the presence of parameter uncertainties, measurement noise and light variation.
Controlling microalgae cultivation, i.e., a crucial industrial topic today, is a challenging task since the corresponding modeling is complex, highly uncertain and timevarying. A model-free control setting is therefore introduced in order to ensure a high growth of microalgae in a continuous closed photobioreactor. Computer simulations are displayed in order to compare this design to an input-output feedback linearizing control strategy, which is widely used in the academic literature on photobioreactors. They assess the superiority of the model-free standpoint both in terms of performances and implementation simplicity.Key Words-Microalgae, photobioreactor, model-free control, intelligent proportional controller, input-output feedback linearizing controller.
This paper proposes a receding waypoint horizon strategy generating a piecewise polynomial trajectory with minimum jerk and predictive tracking of camera references for quadrotors, in the context of autonomous aerial singlesequence shots in a static environment. In order to deal with the limited on-board computation resources, the camera control is performed with an undersampled model predictive controller generating a set-point trajectory and a feedforward control signal, both used by a larger frequency controller. The performance of the overall strategy is illustrated with a real flight, on a Parrot Bebop 2 drone.
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