Axis control using model predictive control: identification and friction effect reduction

International audienceThis paper treats the identification and control of a machining center by means of predictive control, specifically focusing on the aspect of reducing friction effect. The machine tool is a five-axis CNC Mikron machine, in the context of HSM "High Speed Machining", with open control architecture. The axes are internally controlled by current and speed PI controllers in a classical cascade framework. In an external position loop, a predictive controller is considered instead of a classical position proportional controller with a feed forward action. The novelties stressed in the paper are the identification and the tuning of the predictive controller in order to reduce the impact of the frictions. The two-degree of freedom controller obtained using predictive strategy permits to adjust separately the tracking performance and the disturbance rejection. The tracking performance is tuned to reduce the contour error and the disturbance rejection is tuned by means of a disturbance model in order to reduce the friction impact. First, based on a nonlinear simulation model considering the frictions in the axis, a numerical model is derived by least square identification. Afterwards this numerical model is used to synthetize a predictive GPC controller reducing the impact of the friction. The benefit of the proposed structure is analyzed by means of experimental tests and a comparison with the classical position loop control with speed feed-forward. The experimental results are obtained for a two-axis trajectory, showing that the resulting experimental contour errors are smaller using the predictive controller. As perspective the paper proposes to use a control structure including only an internal current controller and external predictive position loop, without velocity loop

Nonlinear Model Predictive Control of the Air Path of a Turbocharged Gasoline Engine Using Laguerre Functions

International audienceObjectives in terms of pollutant emissions and fuel consumption reduction have led car manufacturers to enhance the technical definitions of combustion engines. The latter should now be considered as multiple-input multiple-output nonlinear systems with saturated actuators. This considerably increases the challenge regarding the development of optimal control laws under the constraints of constant cost reductions in the automotive industry. In the present paper, the use of a nonlinear model predictive control (NMPC) scheme is studied for the air path control of a turbocharged gasoline engine. Specifically, a zero dimension physics-based model is combined with parameterization of the future control trajectory. The use of Laguerre polynomials is shown to increase flexibility for the future control trajectory at no cost in computational requirements. This increase in flexibility leads to an improvement of the transient response of the closed-loop with respect to traditional approaches. This practical application shows that this approach makes it easier to fine-tune the NMPC scheme when dealing with engine air path control

Explicit-Ready Nonlinear Model Predictive Control for Turbocharged Spark-Ignited Engine

International audienceThe trend to reduce the engine size in automotive industry is motivated by more restrictive pollutant emissions standards. That is why engine technical definitions have become more and more complicated. The control challenge has also grown since engines are now considered as highly nonlinear multi-input multi-output systems with saturated actuators. In this context, the need for model-based control laws is bigger than ever. In this study we propose a nonlinear model predictive control strategy based on a physical engine model. Moreover, we also underline the benefit of using a thermodynamic engine term in the objective function. Finally, the design and calibration choices consciously fulfill the criterions of the use of an explicit approach for the real time implementation

Explicit Nonlinear Model Predictive Control of the Air Path of a Turbocharged Spark-Ignited Engine

International audiencePollutant emissions and fuel economy objectives have led car manufacturers to develop innovative and more sophisticated engine layouts. In order to reduce time-to-market and development costs, recent research has investigated the idea of a quasi-systematic engine control development approach. Model based approaches might not be the only possibility but they are clearly predetermined to considerably reduce test bench tuning work requirements. In this paper, we present the synthesis of a physics-based nonlinear model predictive control law especially designed for powertrain control. A binary search tree is used to ensure real-time implementation of the explicit form of the control law, computed by solving the associated multi-parametric nonlinear problem

Parallel Guiding Virtual Fixtures: Control and Stability

International audienceGuiding virtual fixtures have been proposed as a method for human-robot co-manipulation. They constrain the motion of the robot to task-relevant trajectories, which enables the human to execute the task more efficiently, accurately and/or ergonomically. When sequences of different tasks must be solved, multiple guiding virtual fixtures are required, and the appropriate guide for the current task must be detected automatically. To this end, we propose a novel control scheme for multiple guiding virtual fixtures that are active in parallel. Furthermore, we determine under which conditions using multiple fixtures is stable. Finally, we perform a pilot study for a real-world application with a humanoid robot

Distributed Model Predictive Control of a Hydro-Power Valley by Dual Decomposition

International audienceIn this paper, a suboptimal distributed MPC approach for linear interconnected systems is considered, where it is assumed that the systems are coupled through their control inputs and an optimal reference tracking problem for the overall system is solved. The approach is applied to distributed MPC of a hydro-power valley case study

Long-term antibiotic therapy in patients with surgery-indicated not undergoing surgery infective endocarditis

Background: To date, there is little information regarding management of patients with infective endocarditis (IE) that did not undergo an indicated surgery. Therefore, we aimed to evaluate prognosis of these patients treated with a long-term antibiotic treatment strategy, including oral long term suppressive antibiotic treatment in five referral centres with a multidisciplinary endocarditis team. Methods: This retrospective, multicenter study retrieved individual patient-level data from five referral centres in Spain. Among a total of 1797, 32 consecutive patients with IE were examined (median age 72 years; 78% males) who had not undergone an indicated surgery, but received long-term antibiotic treatment (LTAT) and were followed by a multidisciplinary endocarditis team, between 2011 and 2019. Primary outcomes were infection relapse and mortality during follow-up. Results: Among 32 patients, 21 had IE associated with prostheses. Of the latter, 8 had an ascending aorta prosthetic graft. In 24 patients, a switch to long-term oral suppressive antibiotic treatment (LOSAT) was considered. The median duration of LOSAT was 277 days. Four patients experienced a relapse during follow-up. One patient died within 60 days, and 12 patients died between 60 days and 3 years. However, only 4 deaths were related to IE. Conclusions: The present study results suggest that a LTAT strategy, including LOSAT, might be considered for patients with IE that cannot undergo an indicated surgery. After hospitalization, they should be followed by a multidisciplinary endocarditis team

Long-term antibiotic therapy in patients with surgery-indicated not undergoing surgery infective endocarditis

Background: To date, there is little information regarding management of patients with infective endocarditis (IE) that did not undergo an indicated surgery. Therefore, we aimed to evaluate prognosis of these patients treated with a long-term antibiotic treatment strategy, including oral long term suppressive antibiotic treatment in five referral centres with a multidisciplinary endocarditis team.Methods: This retrospective, multicenter study retrieved individual patient-level data from five referral centres in Spain. Among a total of 1797, 32 consecutive patients with IE were examined (median age 72 years; 78% males) who had not undergone an indicated surgery, but received long-term antibiotic treatment (LTAT) and were followed by a multidisciplinary endocarditis team, between 2011 and 2019. Primary outcomes were infection relapse and mortality during follow-up.Results: Among 32 patients, 21 had IE associated with prostheses. Of the latter, 8 had an ascending aorta prosthetic graft. In 24 patients, a switch to long-term oral suppressive antibiotic treatment (LOSAT) was considered. The median duration of LOSAT was 277 days. Four patients experienced a relapse during follow-up. One patient died within 60 days, and 12 patients died between 60 days and 3 years. However, only 4 deaths were related to IE.Conclusions: The present study results suggest that a LTAT strategy, including LOSAT, might be considered for patients with IE that cannot undergo an indicated surgery. After hospitalization, they should be followed by a multidisciplinary endocarditis team

Robustification de lois de commande prédictive par la paramétrisation de Youla

This PhD thesis presents a methodology for enhancing the robustness of predictive control laws, particularly the Generalised Predictive Control (GPC) strategy, based on the Youla parametrization. First, the GPC, its robustness characteristics, the equivalent RST polynomial controller and the usual methods used to robustify this kind of controller are presented. Then, the Youla parametrization is introduced. By means of the Youla parametrization, frequency and temporal closed loop specifications are formulated within a convex optimisation framework. This problem transformation is possible thanks to the parametrization of all stabilising controllers operated by the Youla parameter. However, as this parameter belongs to an infinite-dimensional space, the optimal solution can not yet be found. A sub-optimal solution belonging to a sub-space generated by an orthonormal base is numerically deduced. Specifications reflecting nominal performance and robustness stability using unstructured uncertainties are used. It is shown that the definition of temporal templates permits to easily adjust the compromise between robustness and performance.The developed methodology is then applied to robustify a GPC controlled positioning benchmark, including an induction motor, aiming at reducing the impact of measurement noise and inertia variation of the system while respecting a temporal template for the disturbance rejection. Comparison with results obtained with a more conventional controller is finally given.Cette thèse présente une méthode de robustification de lois de commande prédictive, notamment de la commande prédictive généralisée (GPC), basée sur la paramétrisation de Youla. Dans une première partie, on aborde la commande GPC, ses caractéristiques de robustesse, la structure du régulateur polynomial RST équivalent et les méthodes classiquement utilisées pour robustifier ce type de commande. Il est ensuite étudié la paramétrisation de Youla. Cet outil paramètre la classe de correcteurs stabilisant un système, et permet l’obtention de spécifications convexes en boucle fermée. Ces caractéristiques de la paramétrisation de Youla sont utilisées pour traduire le problème de robustification d’un correcteur en un problème d’optimisation convexe. Ce problème d’optimisation étant défini dans un espace de dimension infinie, en l’occurrence l’espace de l’ensemble de systèmes stables, une solution sous-optimale appartenant à un sous-espace généré par une base orthonormale est obtenue de façon numérique. Des spécifications de performance nominale et de robustesse en stabilité face à des incertitudes non structurées sont utilisées. Ces spécifications peuvent être exprimées soit par des critères fréquentiels, soit par des contraintes temporelles. Les contraintes temporelles, exprimées au moyen de gabarits, permettent d’ajuster de façon visuelle le compromis entre la robustesse et la performance à obtenir lors de la robustification.Cette méthodologie de robustification a été appliquée à la robustification d’un système électromécanique de positionnement contrôlé par un régulateur prédictif GPC. Le régulateur GPC a été robustifié afin de diminuer l’effet du bruit de mesure sur la commande et de garantir une performance face à des changements de l’inertie de la charge, tout en garantissant une dynamique pour le rejet de perturbation. Les résultats obtenus sont finalement comparés à ceux obtenus avec une structure de régulation standard pour ce type d’application