<p>Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. The most PFC did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, norm-bounded external disturbances, the system states and control matrices are modified. In addition, the vehicle time-varying longitudinal speed is considered, and a vehicle lateral dynamic model based on Linear Parameter Varying (LPV) is established by utilizing a polytope with finite vertices. Then the Min-Max robust MPC state feedback control law is obtained at every timestamp by solving a set of matrix inequalities which are derived from Lyapunov stability and the minimization of the worst-case in infinite-horizon quadratic objective function. Compared to adaptive MPC, nonlinear MPC, and cascade LPV control, the proposed robust LPV MPC shows improved tracing accuracy along vehicle lateral dynamics. Finally, the state feedback switched LPV control theory with separate Lyapunov functions under both arbitrary switching and average-dwell-time (ADT) switching conditions are studied and applied to cover the path following control in full speed range. Numerical examples, tracking effectiveness, and convergence analysis are provided to demonstrate and ensure the control effectiveness and strong robustness of the proposed algorithms.</p>

A generic plant-wide control structure is proposed for the optimal operation of a grinding mill circuit. An economic objective function is defined for the grinding mill circuit with reference to the economic objective of the larger mineral processing plant. A mineral processing plant in this study consists of a comminution and a separation circuit and excludes the extractive metallurgy at a metal refinery. The comminution circuit's operational performance primarily depends on the mill's performance. Since grindcurves define the operational performance range of a mill, the grindcurves are used to define the setpoints for the economic controlled variables for optimal steady-state operation. For a given metal price, processing cost, and transportation cost, the proposed structure can be used to define the optimal operating region of a grinding mill circuit for the best economic return of the mineral processing plant. The plant-wide control structure identifies the controlled and manipulated variables to ensure the grinding mill circuit can be maintained at the desired operating condition. The plant-wide control framework specifies regulatory and supervisory control aims which can be achieved by means of non-linear model-based control. An impediment to implementing model-based control is the computational expense to solve the non-linear optimisation function. To resolve this issue, the reference-command tracking version of model predictive static programming (MPSP) is applied to a grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of Model Predictive Control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, is compared to the performance of a standard non-linear MPC (NMPC) technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and NMPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices, and using a closed form expression to update the control. The MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for on-line applications of the NMPC philosophy to real-world industrial process plants. The MPSP and NMPC simulation studies above assume full-state feedback. However, this is not always possible for industrial grinding mill circuits. Therefore, a non-linear observer model of a grinding mill is developed which distinguishes between the volumetric hold-up of water, solids, and the grinding media in the mill. Solids refer to all ore small enough to discharge through the end-discharge grate, and grinding media refers to the rocks and steel balls. The rocks are all ore too large to discharge from the mill. The observer model uses the accumulation rate of solids and the discharge rate as parameters. It is shown that with mill discharge flow-rate, discharge density, and volumetric hold-up measurements, the model states and parameters are linearly observable. Although instrumentation at the mill discharge is not yet included in industrial circuits because of space restrictions, this study motivates the benefits to be gained from including such instrumentation. An Extended Kalman Filter (EKF) is applied in simulation to estimate the model states and parameters from data generated by a grinding mill simulation model from literature. Results indicate that if sufficiently accurate measurements are available, especially at the discharge of the mill, it is possible to reliably estimate grinding media, solids and water hold-ups within the mill. Such an observer can be used as part of an advanced process control strategy. / 'n Generiese aanlegwye beheerstruktuur vir die optimale beheer van 'n maalmeulkring word voorgehou. 'n Ekonomiese doelwitfunksie is gedefinieer vir die maalmeulkringbaan met verwysing tot die ekonomiese doelwit van die groter mineraalverwerkingsaanleg. 'n Mineraalverwerkingsaanleg bestaan in hierdie studie slegs uit die vergruisings- en skeidingskringbane. Die ekstraktiewe metallurgie by die metaal raffinadery word uitgesluit. Die vergruisingskringbaan se operasionele werksverrigting is hoofsaaklik van die maalmeul se werksverrigting afhanklik. Aangesien maalkurwes die bereik van die maalmeul se werksverrigting beskryf, kan die maalkurwes gebruik word om die stelpunte van die ekonomiese beheerveranderlikes te definieer vir werking by optimale gestadigde toestand. Gegewe 'n bepaalde metaalprys, bedryfskoste, en vervoerkoste, kan die voorgestelde struktuur gebruik word om die optimale werksgebied vir die maalmeulkring te definieer vir die beste ekonomiese gewin van die algehele mineraalverwerkingsaanleg. Die aanlegwye beheerstruktuur omskryf die beheerveranderlikes en manipuleerbare veranderlikes wat benodig word om die maalmeulkring by die gewenste werksgebied te handhaaf. Die aanlegwye beheerstruktuur spesifiseer regulatoriese en toesighoudende beheer doelwitte. Hierdie doelwitte kan bereik word deur gebruik te maak van nie-lineêre model gebaseerde beheer. Die probleem is dat die bewerkingskoste om nie-lineëre optimeringsfunksies op te los 'n struikelblok is om model gebaseerde beheer op industriële aanlegte toe te pas. Ter oplossing hiervan, word die stelpunt-volg weergawe van model gebaseerde voorspellende statiese programmering (MVSP) toegepas op 'n maalmeulkringbaan. MVSP is 'n innoverende optimale beheertegniek, en bestaan uit 'n kombinasie van die filosofieë van model gebaseerder voorspellende beheer (MVB) en aanpassende dinamiese programmering. Die verrigting van die voorgestelde MVSP beheertegniek word vergelyk met die verrigting van 'n standaard nie-lineëre MVB (NMVB) tegniek deur beide beheertegnieke op dieselfde aanleg vir dieselfde toestande toe te pas. Resultate dui aan dat die MVSP beheertegniek in staat is om die gekose stelpunt te midde van model-aanleg wanaanpassing, steurnisse, en metingsgeraas te volg. Die verrigting van MVSP en NMVB vergelyk goed, maar MVSP bied duidelike voordele. Die bewerkingspoed vir MVSP word vinniger gemaak deur die dinamiese optimeringsprobleem in 'n laeorde statiese optimeringsprobleem te omskep, die sensitiwiteitsmatrikse rekursief uit te werk, en deur 'n geslote uitdrukking ter opdatering van die beheeraksie te gebruik. Die MVSP beheertegniek benodig normaalweg slegs 'n paar iterasies om tot 'n oplossing te konvergeer, selfs indien beperkings op die insette toegepas word. Om die rede word MVSP as 'n potensiële kandidaat beskou vir aanlyntoepasings van die NMVB filosofie op industriële aanlegte. Die MVSP en NMVB simulasie studies hierbo neem aan dat volle toestandterugvoer moontlik is. Hierdie is nie altyd moontlik vir industriële maalmeulkringbane nie. Om die rede is 'n nie-lineêre waarnemingsmodel van 'n maalmeul ontwikkel. Die model onderskei tussen die volumetriese hoeveelheid water, vaste stowwe, en maalmedia in die meul. Vaste stowwe verwys na alle erts wat klein genoeg is om deur die uitskeidingsif aan die ontslagpunt van die meul te vloei. Maalmedia verwys na rotse en staalballe in die meul, met rotse wat te groot is om deur die uitskeidingsif te vloei. Die waarnemingsmodel maak gebruik van die ontslaantempo en die opeenhopingstempo van vaste stowwe as parameters. Indien die meul se ontslagvloeitempo, ontslagdigtheid, en totale volumetriese aanhouding gemeet word, is alle toestande en parameters van die waarnemingsmodel lineêr waarneembaar. Alhoewel instrumentasie by die meul se ontslagpunt as gevolg van ruimte beperkings nog nie op industriële aanlegte ingesluit word nie, dui hierdie studie die voordele aan wat verkrygbaar is deur sulke instrumentasie in te sluit. 'n Verlengde Kalman Filter (VKF) word in simulasie gebruik om die model se toestande en parameters af te skat. 'n Bestaande maalmeul simulasie model vanuit die literatuur word gebruik om die nodige data vir die VKF te genereer. Resultate dui aan dat indien die metings akkuraat genoeg is, veral by die ontslagpunt van die meul, betroubare afskattings van die volumetriese hoeveelheid maalmedia, vaste stowwe, en water in die meul gemaak kan word. So 'n afskatter kan vorentoe gebruik word as deel van 'n gevorderde prosesbeheer strategie. / Thesis (PhD)--University of Pretoria, 2016. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

Cette thèse est dédiée à l’analyse du retard (de calcul ou induit par la communication), qui représente un des paramètres sensibles, et qui doit être pris en compte, pour la mise en œuvre de la Commande Prédictive en temps réel d’un processus dynamique. Dans la première partie, nous avons abordé le problème d’existence des ensembles D-invariants et avons fourni par la suite des conditions nécessaires et/ou suffisantes pour l’existence de ces ensembles. En outre, nous avons détaillé quelques nouvelles idées sur la construction des ensembles D-invariants en utilisant des algorithmes itératifs et d’autres algorithmes basés sur des techniques d’optimisation à deux niveaux. La seconde partie a été consacrée à l’étude du problème de robustesse des systèmes linéaires discrets affectés par un retard variable en boucle fermée avec un contrôleur affine par morceaux défini sur une partition polyédrale de l’espace d’état. L’étude a porté sur l’analyse de la fragilité d’une telle loi commande en présence du retard dans la boucle. Nous avons décrit les marges d’invariance robustes définies comme étant le plus grand sous-ensemble de l’incertitude paramétrique pour lequel l’invariance positive est garantie par rapport à la dynamique en boucle fermée en présence du retard. La dernière partie de cette thèse s’est articulée autour de la conception des lois de commande prédictives avec un attention particulière aux modèles linéaires discrets décrivant des dynamiques affectées par des contraintes en présence du retard. Nous avons proposé plusieurs méthodes offrant différentes solutions au problème de stabilisation locale sans contrainte. Afin d’assurer la stabilité et de garantir la satisfaction des contraintes, nous avons exploité le concept d’invariance et à l’aide du formalisme "ensemble terminal-coût terminal", un problème d’optimisation a été formulé où les états sont forcés d’atteindre l’ensemble maximal admissible d’états retardés/D-invariant à la fin de l’horizon de prédiction. Enfin, nous avons étudié le problème de stabilisation des systèmes continus commandés en réseau soumis à des retards incertains et éventuellement variant dans le temps. Nous avons montré que les ensembles λ-D-contractifs peuvent être utilisés comme ensembles cibles où la stratégie de commande consiste en un simple problème de programmation linéaire ’LP’ qui peut être résolu en ligne. / The research conducted in this thesis has been focusing on Model Predictive Control (MPC) and the implication of network induced time-varying delays. We have addressed, in the first part of this manuscript, the existence problem and the algorithmic computation of positive invariant sets in the state space of the original discrete delay difference equation. The second part of these thesis has been devoted to the study of the robustness problem for a specific class of dynamical systems, namely the piecewise affine systems, defined over a polyhedral partition of the state space in the presence of variable input delay. The starting point was the construction of a predictive control law which guarantees the existence of a non-empty robust positive invariant set with respect to the closed-loop dynamic. The variable delay inducing in fact a model uncertainty, the objective was to describe the robust invariance margins defined as the largest subset of the parametric uncertainty for which the positive invariance is guaranteed with respect to the closed-loop dynamics in the presence of small and large delays. The last part has been dedicated to Model Predictive Control design with a specific attention to linear discrete time-delay models affected by input/state constraints. The starting point in the analysis was the design of a local stabilizing control law using different feedback structures. We proposed several design methods offering different solutions to the local unconstrained stabilization problem. In order to ensure stability and guarantee input and state constraints satisfaction of the moving horizon controller, the concept of positive invariance related to time-delay systems was exploited. Using the "terminal setterminal cost" design, the states were forced to attain the maximal delayed-state admissible set at the end of the prediction horizon. Finally, we have investigated the stabilization problem of Networked Control Systems ’NCSs’ subject to uncertain, possibly time-varying, network-induced delays. We showed that λ-D-contractive sets can be used as a target sets in a set induced Lyapunov function control fashion where a simple Linear Programming ’LP’ problem is required to be solved at each sampling instance.

Les dernières années ont permis des développements très rapides, tant au niveau de l’élaboration que de l’application, d’algorithmes de commande prédictive non linéaire (CPNL), avec une gamme relativement large de réalisations industrielles. Un des obstacles les plus significatifs rencontré lors du développement de cette commande est lié aux incertitudes sur le modèle du système. Dans ce contexte, l’objectif principal de cette thèse est la conception de lois de commande prédictives non linéaires robustes vis-à-vis des incertitudes sur le modèle. Classiquement, cette synthèse peut s’obtenir via la résolution d’un problème d’optimisation min-max. L’idée est alors de minimiser l’erreur de suivi de la trajectoire optimale pour la pire réalisation d'incertitudes possible. Cependant, cette formulation de la commande prédictive robuste induit une complexité qui peut être élevée ainsi qu’une charge de calcul importante, notamment dans le cas de systèmes multivariables, avec un nombre de paramètres incertains élevé. Pour y remédier, une approche proposée dans ces travaux consiste à simplifier le problème d’optimisation min-max, via l’analyse de sensibilité du modèle vis-à-vis de ses paramètres afin d’en réduire le temps de calcul. Dans un premier temps, le critère est linéarisé autour des valeurs nominales des paramètres du modèle. Les variables d’optimisation sont soit les commandes du système soit l’incrément de commande sur l’horizon temporel. Le problème d’optimisation initial est alors transformé soit en un problème convexe, soit en un problème de minimisation unidimensionnel, en fonction des contraintes imposées sur les états et les commandes. Une analyse de la stabilité du système en boucle fermée est également proposée. En dernier lieu, une structure de commande hiérarchisée combinant la commande prédictive robuste linéarisée et une commande par mode glissant intégral est développée afin d’éliminer toute erreur statique en suivi de trajectoire de référence. L'ensemble des stratégies proposées est appliqué à deux cas d'études de commande de bioréacteurs de culture de microorganismes. / The last few years have led to very rapid developments, both in the formulation and the application of Nonlinear Model Predictive Control (NMPC) algorithms, with a relatively wide range of industrial achievements. One of the most significant challenges encountered during the development of this control law is due to uncertainties in the model of the system. In this context, the thesis addresses the design of NMPC control laws robust towards model uncertainties. Usually, the above design can be achieved through solving a min-max optimization problem. In this case, the idea is to minimize the tracking error for the worst possible uncertainty realization. However, this robust approach tends to become too complex to be solved numerically online, especially in the case of multivariable systems with a large number of uncertain parameters. To address this shortfall, the proposed approach consists in simplifying the min-max optimization problem through a sensitivity analysis of the model with respect to its parameters, in order to reduce the calculation time. First, the criterion is linearized around the model parameters nominal values. The optimization variables are either the system control inputs or the control increments over the prediction horizon. The initial optimization problem is then converted either into a convex optimization problem, or a one-dimensional minimization problem, depending on the nature of the constraints on the states and commands. The stability analysis of the closed-loop system is also addressed. Finally, a hierarchical control strategy is developed, that combines a robust model predictive control law with an integral sliding mode controller, in order to cancel any tracking error. The proposed approaches are applied through two case studies to the control of microorganisms culture in bioreactors.

Recent decades have witnessed the phenomenal success of model predictive control (MPC) in a wide spectrum of domains, such as process industries, intelligent transportation, automotive applications, power systems, cyber security, and robotics. For constrained dynamic systems subject to uncertainties, robust MPC is attractive due to its capability of effectively dealing with various types of uncertainties while ensuring optimal performance concerning prescribed performance indices. But most robust MPC schemes require prior knowledge on the uncertainty, which may not be satisfied in practical applications. Therefore, it is desired to design robust MPC algorithms that proactively update the uncertainty description based on the history of inputs and measurements, motivating the development of adaptive MPC. This dissertation investigates four problems in robust and adaptive MPC from theoretical and application points of view. New algorithms are developed to address these issues efficiently with theoretical guarantees of closed-loop performance. Chapter 1 provides an overview of robust MPC, adaptive MPC, and self-triggered MPC, where the recent advances in these fields are reviewed. Chapter 2 presents notations and preliminary results that are used in this dissertation. Chapter 3 investigates adaptive MPC for a class of constrained linear systems with unknown model parameters. Based on the recursive least-squares (RLS) technique, we design an online set-membership system identification scheme to estimate unknown parameters. Then a novel integration of the proposed estimator and homothetic tube MPC is developed to improve closed-loop performance and reduce conservatism. In Chapter 4, a self-triggered adaptive MPC method is proposed for constrained discrete-time nonlinear systems subject to parametric uncertainties and additive disturbances. Based on the zonotope-based reachable set computation, a set-membership parameter estimator is developed to refine a set-valued description of the time-varying parametric uncertainty under the self-triggered scheduling. We leverage this estimation scheme to design a novel self-triggered adaptive MPC approach for uncertain nonlinear systems. The resultant adaptive MPC method can reduce the average sampling frequency further while preserving comparable closed-loop performance compared with the periodic adaptive MPC method. Chapter 5 proposes a robust nonlinear MPC scheme for the visual servoing of quadrotors subject to external disturbances. By using the virtual camera approach, an image-based visual servoing (IBVS) system model is established with decoupled image kinematics and quadrotor dynamics. A robust MPC scheme is developed to maintain the visual target stay within the field of view of the camera, where the tightened state constraints are constructed based on the Lipschitz condition to tackle external disturbances. In Chapter 6, an adaptive MPC scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. We develop an RLS-based set-membership based parameter to improve the prediction accuracy. In the proposed adaptive MPC scheme, a robustness constraint is designed to handle parametric and additive uncertainties. The proposed constraint has the offline computed shape and online updated shrinkage rate, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Chapter 7 shows some conclusion remarks and future research directions. / Graduate

No description available.

Medium-Voltage Direct-Current (MVDC) power system has been considered as the trending technology for future All-Electric Ships (AES) to produce, convert and distribute electrical power. With the wide employment of highrequency power electronics converters and motor drives in DC system, accurate and fast assessment of system dynamic behaviors , as well as the optimization of system transient performance have become serious concerns for system-level studies, high-level control designs and power management algorithm development. The proposed technique presents a coordinated and automated approach to determine the system adjustment strategy for naval power systems to improve the transient performance and prevent potential instability following a system contingency. In contrast with the conventional design schemes that heavily rely on the human operators and pre-specified rules/set points, we focus on the development of the capability to automatically and efficiently detect and react to system state changes following disturbances and or damages by incooperating different system components to formulate an overall system-level solution. To achieve this objective, we propose a generic model-based predictive management framework that can be applied to a variety of Shipboard Power System (SPS) applications to meet the stringent performance requirements under different operating conditions. The proposed technique is proven to effectively prevent the system from instability caused by known and unknown disturbances with little or none human intervention under a variety of operation conditions. The management framework proposed in this dissertation is designed based on the concept of Model Predictive Control (MPC) techniques. A numerical approximation of the actual system is used to predict future system behaviors based on the current states and the candidate control input sequences. Based on the predictions the optimal control solution is chosen and applied as the current control input. The effectiveness and efficiency of the proposed framework can be evaluated conveniently based on a series of performance criteria such as fitness, robustness and computational overhead. An automatic system modeling, analysis and synthesis software environment is also introduced in this dissertation to facilitate the rapid implementation of the proposed performance management framework according to various testing scenarios.

Shipboard Power System (SPS) is known as an independent controlled small electric network powered by the distributed onboard generation system. Since many electric components are tightly coupled in a small space and the system is not supported with a relatively stronger grid, SPS is more susceptible to unexpected disturbances and physical damages compared to conventional terrestrial power systems. Among different distribution configurations, power-electronic based DC distribution is considered the trending technology for the next-generation U.S. Navy fleet design to replace the conventional AC-based distribution. This research presents appropriate control management frameworks to improve the Medium-Voltage DC (MVDC) shipboard power system performance. Model Predictive Control (MPC) is an advanced model-based approach which uses the system model to predict the future output states and generates an optimal control sequence over the prediction horizon. In this research, at first, a centralized MPC is developed for a nonlinear MVDC SPS when a high-power pulsed load exists in the system. The closed-loop stability analysis is considered in the MPC optimization problem. A comparison is presented for different cases of load prediction for MPC, namely, no prediction, perfect prediction, and Autoregressive Integrated Moving Average (ARIMA) prediction. Another centralized MPC controller is also designed to address the reconfiguration problem of the MVDC system in abnormal conditions. The reconfiguration goal is to maximize the power delivered to the loads with respect to power balance, generation limits and load priorities. Moreover, a distributed control structure is proposed for a nonlinear MVDC SPS to develop a scalable power management architecture. In this framework, each subsystem is controlled by a local MPC using its state variables, parameters and interaction variables from other subsystems communicated through a coordinator. The Goal Coordination principle is used to manage interactions between subsystems. The developed distributed control structure brings out several significant advantages including less computational overhead, higher flexibility and a good error tolerance behavior as well as a good overall system performance. To demonstrate the efficiency of the proposed approach, a performance analysis is accomplished by comparing centralized and distributed control of global and partitioned MVDC models for two cases of continuous and discretized control inputs.

Model predictive control (MPC) for switched reluctance machine (SRM) drives is studied in this thesis. The objective is to highlight the benefits of implementing MPC to overcome the main drawbacks of SRMs and position them as an attractive alternative among electrical drives. A comprehensive literature review of MPC for SRM is presented, detailing its current trends as an application still at an early stage. The different features of MPC are highlighted and paired with the most challenging and promising control objectives of SRMs. A vision of future research trends and applications of MPC-driven SRMs is proposed, thus drawing a road-map of future projects, barriers to overcome and potential developments. Several important applications can take advantage of the improved features that SRM can get with MPC, especially from the possibility of defining a unified control technique with the flexibility to adapt to different system requirements. The most important cluster for SRM drives is the high- and ultrahigh-speed operative regions where conventional machines cannot work efficiently. SRMs with MPC can complement then the existing demand for electrical drives with high performance under challenging conditions. Three techniques based on the finite control set model predictive control (FCS-MPC) approach are developed out of the proposed road-map. The first one defines a virtual-flux current tracking technique that improves the existing ones in operating at different speeds and more than one quadrant operation. The method is validated for low- and high- power SRMs in simulations and diverse types of current waveform, making it easy to adapt to existing current shaping techniques. It is also validated experimentally for different operating conditions and robustness against parameter variation. The second technique proposed a predictive torque control that bases its model on static-maps, thus avoiding complex analytical expressions. It improves its estimation through a Kalman filter. The third technique uses a virtual-flux predictive torque control, similar to the first technique for current tracking. The techniques are validated at a wide speed range, thus evidencing superiority in performance without modification on the control structure. / Thesis / Doctor of Philosophy (PhD)

Engine control becomes increasingly important in newer cars. It is therefore interesting to investigate if a relatively new control method as Model Predictive Control (MPC) can be useful in engine control in the future. One of the advantages of MPC is that it can handle contraints explicitly. In this thesis basics on turbocharged engines and the underlying theory of MPC is presented. Based on a nonlinear mean value engine model, linearized at multiple operating points, we then implement both a linear and a nonlinearMPC strategy and highlight implementation issues. The implemented MPC controllers calculate optimal wastegate position in order to track a requested torque curve and still make sure that the constraints on turbocharger speed and minimum and maximum opening of the wastegate are fulfilled.