Robust nonlinear control of vectored thrust aircraft

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations

IDENTIFICATION OF HEAT RELEASE SHAPES AND COMBUSTION CONTROL OF AN LTC ENGINE

Low Temperature Combustion (LTC) regimes have gained attention in internal combustion engines since they deliver low nitrogen oxides (NOx) and soot emissions with higher thermal efficiency and better combustion efficiency, compared to conventional combustion regimes. However, the operating region of these high-efficiency combustion regimes is limited as it is prone to knocking and high in-cylinder pressure rise rate outside the engine safe zone. By allowing multi-regime operation, high-efficiency region of the engine is extended. To control these complex engines, understanding and identification of heat release rate shapes is essential. Experimental data collected from a 2 liter 4 cylinder LTC engine with in-cylinder pressure measurements, is used in this study to calculate Heat Release Rate (HRR). Fractions of early and late heat release are calculated from HRR as a ratio of cumulative heat release in the early or late window to the total energy of the fuel injected into the cylinder. Three specific HRR patterns and two transition zones are identified. A rule based algorithm is developed to classify these patterns as a function of fraction of early and late heat release percentages. Combustion parameters evaluated also showed evidence on characteristics of classification. Supervised and unsupervised machine learning approaches are also evaluated to classify the HRR shapes. Supervised learning method ( Decision Tree)is studied to develop an automatic classifier based on the control inputs to the engine. In addition, supervised learning method (Convolutional Neural Network (CNN)) and unsupervised learning method (k-means clustering) are studied to develop an automatic classifier based on HRR trace obtained from the engine. The unsupervised learning approach wasn\u27t successful in classification as the arrived k-means centroids didn\u27t clearly represent a particular combustion regime. Supervised learning techniques, CNN method is found with a classifier accuracy of 70% for identifying heat release shapes and Decision Tree with the accuracy of 74.5% as a function of control inputs. On rule based classified traces with the use of principle component analysis (PCA) and linear regression, heat release rate classifiers are built as a function of engine input parameters including, Engine speed, Start of injection (SOI), Fuel quantity (FQ) and Premixed ratio (PR). The results are then used to build a linear parameter varying (LPV) model as a function of the modelled combustion classifiers by using the least square support vector machine (LS-SVM) approach. LPV model could predict CA50(Combustion phasing), IMEP (indicated mean effective pressure) and MPRR (maximum pressure rise rate) with a RMSE of 0.4 CAD, 16.6 kPa and 0.4 bar/CAD respectively. The designed LPV model is then incorporated in a model predictive control (MPC) platform to adjust CA50, IMEP and MPRR. The results show the designed LTC engine controller could track CA50 and IMEP with average error of 1.2 CAD and 6.2 kPa while limiting MPRR to 6 bar/CAD. The controller uses three engine inputs including, SOI, PR and FQ as manipulated variables, that are optimally changed to control the LTC engine

Dynaamisten mallien puoliautomaattinen parametrisointi käyttäen laitosdataa

The aim of this thesis was to develop a new methodology for estimating parameters of NAPCON ProsDS dynamic simulator models to better represent data containing several operating points. Before this thesis, no known methodology had existed for combining operating point identification with parameter estimation of NAPCON ProsDS simulator models. The methodology was designed by assessing and selecting suitable methods for operating space partitioning, parameter estimation and parameter scheduling. Previously implemented clustering algorithms were utilized for the operating space partition. Parameter estimation was implemented as a new tool in the NAPCON ProsDS dynamic simulator and iterative parameter estimation methods were applied. Finally, lookup tables were applied for tuning the model parameters according to the state. The methodology was tested by tuning a heat exchanger model to several operating points based on plant process data. The results indicated that the developed methodology was able to tune the simulator model to better represent several operating states. However, more testing with different models is required to verify general applicability of the methodology.Tämän diplomityön tarkoitus oli kehittää uusi parametrien estimointimenetelmä NAPCON ProsDS -simulaattorin dynaamisille malleille, jotta ne vastaisivat paremmin dataa useista prosessitiloista. Ennen tätä diplomityötä NAPCON ProsDS -simulaattorin malleille ei ollut olemassa olevaa viritysmenetelmää, joka yhdistäisi operointitilojen tunnistuksen parametrien estimointiin. Menetelmän kehitystä varten tutkittiin ja valittiin sopivat menetelmät operointiavaruuden jakamiselle, parametrien estimoinnille ja parametrien virittämiseen prosessitilan mukaisesti. Aikaisemmin ohjelmoituja klusterointialgoritmeja hyödynnettiin operointiavaruuden jakamisessa. Parametrien estimointi toteutettiin uutena työkaluna NAPCON ProsDS -simulaattoriin ja estimoinnissa käytettiin iteratiivisia optimointimenetelmiä. Lopulta hakutaulukoita sovellettiin mallin parametrien hienosäätöön prosessitilojen mukaisesti. Menetelmää testattiin virittämällä lämmönvaihtimen malli kahteen eri prosessitilaan käyttäen laitokselta kerättyä prosessidataa. Tulokset osoittavat että kehitetty menetelmä pystyi virittämään simulaattorin mallin vastaamaan paremmin dataa useista prosessitiloista. Kuitenkin tarvitaan lisää testausta erityyppisten mallien kanssa, jotta voidaan varmistaa menetelmän yleinen soveltuvuus

Identification of low order models for large scale processes

Many industrial chemical processes are complex, multi-phase and large scale in nature. These processes are characterized by various nonlinear physiochemical effects and fluid flows. Such processes often show coexistence of fast and slow dynamics during their time evolutions. The increasing demand for a flexible operation of a complex process, a pressing need to improve the product quality, an increasing energy cost and tightening environmental regulations make it rewarding to automate a large scale manufacturing process. Mathematical tools used for process modeling, simulation and control are useful to meet these challenges. Towards this purpose, development of process models, either from the first principles (conservation laws) i.e. the rigorous models or the input-output data based models constitute an important step. Both types of models have their own advantages and pitfalls. Rigorous process models can approximate the process behavior reasonably well. The ability to extrapolate the rigorous process models and the physical interpretation of their states make them more attractive for the automation purpose over the input-output data based identified models. Therefore, the use of rigorous process models and rigorous model based predictive control (R-MPC) for the purpose of online control and optimization of a process is very promising. However, due to several limitations e.g. slow computation speed and the high modeling efforts, it becomes difficult to employ the rigorous models in practise. This thesis work aims to develop a methodology which will result in smaller, less complex and computationally efficient process models from the rigorous process models which can be used in real time for online control and dynamic optimization of the industrial processes. Such methodology is commonly referred to as a methodology of Model (order) Reduction. Model order reduction aims at removing the model redundancy from the rigorous process models. The model order reduction methods that are investigated in this thesis, are applied to two benchmark examples, an industrial glass manufacturing process and a tubular reactor. The complex, nonlinear, multi-phase fluid flow that is observed in a glass manufacturing process offers multiple challenges to any model reduction technique. Often, the rigorous first principle models of these benchmark examples are implemented in a discretized form of partial differential equations and their solutions are computed using the Computational Fluid Dynamics (CFD) numerical tools. Although these models are reliable representations of the underlying process, computation of their dynamic solutions require a significant computation efforts in the form of CPU power and simulation time. The glass manufacturing process involves a large furnace whose walls wear out due to the high process temperature and aggressive nature of the molten glass. It is shown here that the wearing of a glass furnace walls result in change of flow patterns of the molten glass inside the furnace. Therefore it is also desired from the reduced order model to approximate the process behavior under the influence of changes in the process parameters. In this thesis the problem of change in flow patterns as result of changes in the geometric parameter is treated as a bifurcation phenomenon. Such bifurcations exhibited by the full order model are detected using a novel framework of reduced order models and hybrid detection mechanisms. The reduced order models are obtained using the methods explained in the subsequent paragraphs. The model reduction techniques investigated in this thesis are based on the concept of Proper Orthogonal Decompositions (POD) of the process measurements or the simulation data. The POD method of model reduction involves spectral decomposition of system solutions and results into arranging the spatio-temporal data in an order of increasing importance. The spectral decomposition results into spatial and temporal patterns. Spatial patterns are often known as POD basis while the temporal patterns are known as the POD modal coefficients. Dominant spatio-temporal patterns are then chosen to construct the most relevant lower dimensional subspace. The subsequent step involves a Galerkin projection of the governing equations of a full order first principle model on the resulting lower dimensional subspace. This thesis can be viewed as a contribution towards developing the databased nonlinear model reduction technique for large scale processes. The major contribution of this thesis is presented in the form of two novel identification based approaches to model order reduction. The methods proposed here are based on the state information of a full order model and result into linear and nonlinear reduced order models. Similar to the POD method explained in the previous paragraph, the first step of the proposed identification based methods involve spectral decomposition. The second step is different and does not involve the Galerkin projection of the equation residuals. Instead, the second step involves identification of reduced order models to approximate the evolution of POD modal coefficients. Towards this purpose, two different methods are presented. The first method involves identification of locally valid linear models to represent the dynamic behavior of the modal coefficients. Global behavior is then represented by ‘blending’ the local models. The second method involves direct identification of the nonlinear models to represent dynamic evolution of the model coefficients. In the first proposed model reduction method, the POD modal coefficients, are treated as outputs of an unknown reduced order model that is to be identified. Using the tools from the field of system identification, a blackbox reduced order model is then identified as a linear map between the plant inputs and the modal coefficients. Using this method, multiple local reduced LTI models corresponding to various working points of the process are identified. The working points cover the nonlinear operation range of the process which describes the global process behavior. These reduced LTI models are then blended into a single Reduced Order-Linear Parameter Varying (ROLPV) model. The weighted blending is based on nonlinear splines whose coefficients are estimated using the state information of the full order model. Along with the process nonlinearity, the nonlinearity arising due to the wear of the furnace wall is also approximated using the RO-LPV modeling framework. The second model reduction method that is proposed in this thesis allows approximation of a full order nonlinear model by various (linear or nonlinear) model structures. It is observed in this thesis, that, for certain class of full order models, the POD modal coefficients can be viewed as the states of the reduced order model. This knowledge is further used to approximate the dynamic behavior of the POD modal coefficients. In particular, reduced order nonlinear models in the form of tensorial (multi-variable polynomial) systems are identified. In the view of these nonlinear tensorial models, the stability and dissipativity of these models is investigated. During the identification of the reduced order models, the physical interpretation of the states of the full order rigorous model is preserved. Due to the smaller dimension and the reduced complexity, the reduced order models are computationally very efficient. The smaller computation time allows them to be used for online control and optimization of the process plant. The possibility of inferring reduced order models from the state information of a full order model alone i.e. the possibility to infer the reduced order models in the absence of access to the governing equations of a full order model (as observed for many commercial software packages) make the methods presented here attractive. The resulting reduced order models need further system theoretic analysis in order to estimate the model quality with respect to their usage in an online controller setting

LPV model-based robust diagnosis of flight actuator faults

A linear parameter-varying (LPV) model-based synthesis, tuning and assessment methodology is developed and applied for the design of a robust fault detection and diagnosis (FDD) system for several types of flight actuator faults such as jamming, runaway, oscillatory failure, or loss of efficiency. The robust fault detection is achieved by using a synthesis approach based on an accurate approximation of the nonlinear actuator-control surface dynamics via an LPV model and an optimal tuning of the free parameters of the FDD system using multi-objective optimization techniques. Real-time signal processing is employed for identification of different fault types. The assessment of the FDD system robustness has been performed using both standard Monte Carlo methods as well as advanced worst-case search based optimization-driven robustness analysis. A supplementary industrial validation performed on the AIRBUS actuator test bench for the monitoring of jamming, confirmed the satisfactory performance of the FDD system in a true industrial setting

Design robust controllers for load reduction in wind turbines

This thesis determines a design methodology of robust and multivariable controllers based on the H∞ norm reduction and on LPV (Linear Parameter Varying) techniques for load reduction in wind turbines. In order to do this, a 5 MW offshore wind turbine model based on the ‘Upwind’ European project is developed using GH Bladed, which is a wind turbine modelling specific software package. These controllers work in the above rated control zone, where the non-linearities of the wind turbine appear with more intensity. The main control objective in this zone is to keep the generator working at the nominal values of rotational speed and torque to correctly extract the nominal electric power in high winds. Furthermore, new control objectives are included to mitigate the loads in different components of the wind turbine, which involves the need of a multivariable control design. The family of linear models extracted from the non-linear model is used to design the proposed controllers. In this work, the family of linear models extracted from the GH Bladed is high ordered due to the complexity and accuracy of the wind turbine model. The Robust Control and LPVMAD MATLAB toolboxes are used to make the controller synthesis. LPVMAD is a toolbox developed by the scientific control group directed by Prof. Dr. Carsten Scherer at the Stuttgart University. After an exhaustive analysis of the State of the Art about the wind turbine control systems, a baseline control strategy based on classical control methods is initially designed. Five monovariable, MISO (Multiple Input Single Output) and multivariable robust control strategies, based on the H∞ norm reduction, are presented to improve the benefits of the baseline controller. These controllers fulfill some control objectives to mitigate the loads in the wind turbine: generator speed regulation, drive train mode damping, tower first fore-aft and side-to-side first mode damping and rotor alignment. The designed H∞ controllers generate control signals of generator torque, collective pitch blade angle and individual pitch angles for each blade. On the other hand, two LPV control strategies are designed to improve the generator speed regulation in the above rated zone generating collective pitch angle set-point values. The first LPV controller consists of the interpolation of three H∞ controllers designed in three different operational points. The second LPV controller synthesis is based on a LMI (Linear Matrix Inequalities) solution using the LPVMAD toolbox and a wind turbine LPV model. The wind turbine multivariable LPV modelling process is also explained in this thesis. The designed controllers are validated in GH Bladed and an exhaustive analysis is carried out to calculate the fatigue load reduction on the wind turbine components, as well as to analyze load mitigation in some extreme cases. The controllers are tested in a real time prototype which allows to carry out HIL (Hardware in the Loop) simulations. A GUI interface tool is developed in MATLAB to determine a sequential method making easier the controller design explained in this thesis. Finally, the proposed design methodology of robust and multivariable controllers is applied to a commercial 3 MW wind turbine.Tesi honek aldagai anitzeko kontrolatzaile sendoak diseinatzeko metodologia bat ezartzen du, non kontrolatzaileak H∞ normaren gutxitzean eta LPV (Linear Parameter Varying) kontrol-tekniketan oinarrituta dauden, haize-errotetako karga mekanikoak murrizteko. Horretarako, 'Upwind' europar proiektuan definitutako 5 MWeko itsas haize-errotaren eredua garatu da GH Bladed softwarean. Kontrolatzaile horien diseinua 'above rated' izeneko funtzionamendu-zonalderako da. Zonalde horretan haize-erroten ez-linealtasunak garrantzi handikoak dira eta haize-errotaren funtzionamendua biratze-abiadura eta momentu nominaletan egin nahi da, horrela haize altuetan potentzia nominala lortu ahal izateko. Hauxe helburu nagusia izanda, beste kontrol-helburuak ere kontuan hartzen dira: haize-errotaren osagai desberdinetan karga mekanikoak txikitzea kontrolatzaileen diseinua aldagai anitzeko ikuspuntu batetik eginez. GH Bladed paketean definitutako eredu ez-linealaren linealizaziotik lortzen den eredu linealen familia erabiltzen da kontrolatzaileak diseinatzeko, nahiz eta oso orden handiko ereduak izan modelatze-konplexutasuna dela-eta. Kontrolatzaileak sortzeko MATLAB-eko kontrol sendoaren 'toolbox'-a erabiltzen da eta baita Dr. Carsten Scherer-en lantaldeak garatutako LPVMAD 'toolbox'-a ere. Haize-errotentzako kontrol-sistemen Arte-Egoeraren analisi sakon baten ondoren, hasieran, erreferentzi kontrolatzaile bat diseinatzen da, normalean erabiltzen diren kontrolatzaile klasikoetan oinarrituta. Tesian bost kontrolatzaile sendo, H∞ normaren txikitzean oinarrituak, aurkezten dira, aldagai bakarrekoak, MISO (Multiple Input Single Output) eta aldagai aniztzekoak, alde batetik erreferentzi kontrol-estrategiaren prestazioak hobetzeko eta beste aldetik haize-errotetan karga mekaniken murrizketak eragiten dituzten helburuak betetzeko: sortzailearen abiadura angeluarra erregulatzea, potentzi trenaren modua moteltzea, dorrearen aurre-atzerako eta alboko lehenengo bibrazio-moduetan haizearen efektuak murriztea eta errotorea lerrokatzea. Kontrolatzaileek sortzaileentzako momentuen kontrol-seinaleak, itxoroskientzat pitch-angelu kolektiboa eta baita itxoroski bakoitzarentzat pitch-angelu independenteak ere sortzen dituzte, inposatutako kontrolhelburuak betetzeko. Horietatik at, beste bi LPV kontrol-estrategia diseinatzen dira 'above rated' funtzionamendu-zonaldean sortzailearen abiadura angeluarraren kontrola hobetzeko pitch-angelu kolektiboaren kontsignen bidez. Lehenengo LPV kontrolatzailea hiru funtzionamendu-puntu desberdinetan diseinaturiko hiru H∞ kontrolatzaileen interpolazioan datza. Bigarren LPV kontrolatzailearen diseinua, ordea, LMI (Linear Matrix Inequalities) sistema baten askatzean datza, LPVMAD 'toolbox'-a eta haize-errotaren LPV eredu bat erabiliz. Haize-errota baten aldagai anitzeko LPV modelatze-prozesua ere zehatz-mehatz azaltzen da tesi honetan. Diseinatutako kontrolatzaileak GH Bladed paketean balioztatu dira analisi sakon baten bidez, non neke-kargen eta mutur-kargen murrizketak haize-errotaren osagai desberdinetan kalkulatzea ahalbideratzen baita. Kontrolatzaileak HIL (Hardware in the Loop) simulazioak egitea errazten duen denbora errealeko prototipo batean ere probatu dira, kontrolatzaileen funtzionamendu egokia ziurtatzen duena. Garatutako kontrolatzaileen diseinua errazteko interfaze grafiko bat gauzatu da MATLAB-en, non tesian aurkeztutako kontrolatzaile bakoitzaren diseinua prozedura sekuentzial baten bidez egin ahal izan den. Azkenean, aldagai anitzeko kontrolatzaile sendoen diseinurako proposaturiko metodologia 3 MWeko haize-errota komertzial batean aplikatu egin da.Esta tesis establece una metodología de diseño de controladores robustos multivariables basados en la reducción de la norma H∞ y en técnicas de control LPV (Linear Parameter Varying) para la reducción de cargas en aerogeneradores. Para ello, se ha desarrollado un modelo de un aerogenerador offshore de 5 MW definido en el proyecto europeo 'Upwind' mediante el software de modelado específico de aerogeneradores GH Bladed. El diseño de estos controladores se centra en la zona de funcionamiento denominada 'above rated', donde se manifiestan con mayor importancia las no-linealidades del aerogenerador y en la que se pretende mantener el funcionamiento del generador en sus valores nominales de velocidad de giro y par para la correcta extracción de potencia nominal a vientos altos. Además de este objetivo principal, se incluyen nuevos objetivos de control que minimicen las cargas en las diferentes partes del aerogenerador haciendo que el diseño de los controladores requiera un punto de vista multivariable. Para el diseño de los controladores se utiliza la familia de modelos lineales extraída de la linealización del modelo no lineal, en este caso definido en GH Bladed, siendo estos modelos de un orden elevado debido a la complejidad del modelado. Para la síntesis de los controladores se utiliza las 'toolbox' de MATLAB de control robusto y la 'toolbox' LPVMAD desarrollada por el grupo de trabajo del Prof. Dr. Carsten Scherer. Tras un profundo análisis del estado del arte sobre los sistemas de control en los aerogeneradores, inicialmente se diseña una estrategia de control referencia basada en los controladores clásicos comúnmente utilizados. En la tesis se presentan cinco controladores robustos monovariables, MISO (Multiple Input Single Output) y multivariables basados en la reducción de la norma H∞ para mejorar las prestaciones de la estrategia de control referencia y que cumplen con diferentes objetivos de control que implican una reducción de cargas en el sistema: regulación de la velocidad angular del generador, amortiguamiento del modo del tren de potencia, reducción del efecto del viento sobre los primeros modos adelante-atrás y lateral de la torre y alineamiento del rotor. Los controladores generan señales de control de par en el generador, ángulo de pitch colectivo en las palas y ángulos independientes de pitch para cada pala con la finalidad de satisfacer los objetivos de control impuestos. Por otro lado, se diseñan dos estrategias de control LPV para mejorar la regulación de velocidad angular del generador en la zona de 'above rated' mediante consignas de ángulo de pitch colectivo. El primer control LPV consiste en la interpolación de tres controladores H∞ diseñados en tres puntos de operación diferentes, mientras que la síntesis del segundo controlador LPV se basa en la solución de un sistema LMI (Linear Matrix Inequalities) mediante la toolbox LPVMAD y utilizando el modelo LPV del aerogenerador. El proceso de modelado LPV multivariable de un aerogenerador también es explicado con detenimiento en esta tesis. Los controladores diseñados son validados en GH Bladed mediante un exhaustivo análisis que permite calcular la reducción de cargas extremas y cargas de fatiga en los diferentes componentes del aerogenerador. Los controladores son probados en un prototipo en tiempo real que permite realizar simulaciones HIL (Hardware in the Loop) que ratifican el correcto funcionamiento de los controladores. Para facilitar el diseño de estos controladores se ha implementado una interfaz gráfica en MATLAB que permite establecer un procedimiento secuencial para el diseño de cada controlador explicado en la tesis. Finalmente, la metodología propuesta para el diseño de controladores robustos multivariables se ha aplicado a un aerogenerador comercial de 3 MW

Modeling and Control of Maximum Pressure Rise Rate in RCCI Engines

Low Temperature Combustion (LTC) is a combustion strategy that burns fuel at lower temperatures and leaner mixtures in order to achieve high efficiency and near zero NOx emissions. Since the combustion happens at lower temperatures it inhibits the formation of NOx and soot emissions. One such strategy is Reactivity Controlled Compression Ignition (RCCI). One characteristic of RCCI combustion and LTC com- bustion in general is short burn durations which leads to high Pressure Rise Rates (PRR). This limits the operation of these engines to lower loads as at high loads, the Maximum Pressure Rise Rate (MPRR) hinders the use of this combustion strategy. This thesis focuses on the development of a model based controller that can control the Crank Angle for 50% mass fraction burn (CA50) and Indicated Mean Effective Pressure (IMEP) of an RCCI engine while limiting the MPRR to a pre determined limit. A Control Oriented Model (COM) is developed to predict the MPRR in an RCCI engine. This COM is then validated against experimental data. A statistical analysis of the experimental data is conducted to understand the accuracy of the COM. The results show that the COM is able to predict the MPRR with reasonable accuracy in steady state and transient conditions. Also, the COM is able to capture the trends during transient operation. This COM is then included in an existing cycle by cycle dynamic RCCI engine model and used to develop a Linear Parameter Varying (LPV) representation of an RCCI engine using Data Driven Modeling (DDM) approach with Support Vector Machines (SVM). This LPV representation is then used along with a Model Predictive Controller (MPC) to control the CA50 and IMEP of the RCCI engine model while limiting the MPRR. The controller was able to track the desired CA50 and IMEP with a mean error of 0.9 CAD and 4.7 KPa respectively while maintaining the MPRR below 5.8 bar/CAD

Identification of Multimodel LPV Models with Asymmetric Gaussian Weighting Function

This paper is concerned with the identification of linear parameter varying (LPV) systems by utilizing a multimodel structure. To improve the approximation capability of the LPV model, asymmetric Gaussian weighting functions are introduced and compared with commonly used symmetric Gaussian functions. By this mean, locations of operating points can be selected freely. It has been demonstrated through simulations with a high purity distillation column that the identified models provide more satisfactory approximation. Moreover, an experiment is performed on real HVAC (heating, ventilation, and air-conditioning) to further validate the effectiveness of the proposed approach

Model-based control for automotive applications

The number of distributed control systems in modern vehicles has increased exponentially over the past decades. Today’s performance improvements and innovations in the automotive industry are often resolved using embedded control systems. As a result, a modern vehicle can be regarded as a complex mechatronic system. However, control design for such systems, in practice, often comes down to time-consuming online tuning and calibration techniques, rather than a more systematic, model-based control design approach. The main goal of this thesis is to contribute to a corresponding paradigm shift, targeting the use of systematic, model-based control design approaches in practice. This implies the use of control-oriented modeling and the specification of corresponding performance requirements as a basis for the actual controller synthesis. Adopting a systematic, model-based control design approach, as opposed to pragmatic, online tuning and calibration techniques, is a prerequisite for the application of state-of-the-art controller synthesis methods. These methods enable to achieve guarantees regarding robustness, performance, stability, and optimality of the synthesized controller. Furthermore, from a practical point-of-view, it forms a basis for the reduction of tuning and calibration effort via automated controller synthesis, and fulfilling increasingly stringent performance demands. To demonstrate these opportunities, case studies are defined and executed. In all cases, actual implementation is pursued using test vehicles and a hardware-in-the-loop setup. • Case I: Judder-induced oscillations in the driveline are resolved using a robustly stable drive-off controller. The controller prevents the need for re-tuning if the dynamics of the system change due to wear. A hardware-in-the-loop setup, including actual sensor and actuator dynamics, is used for experimental validation. • Case II: A solution for variations in the closed-loop behavior of cruise control functionality is proposed, explicitly taking into account large variations in both the gear ratio and the vehicle loading of heavy duty vehicles. Experimental validation is done on a heavy duty vehicle, a DAF XF105 with and without a fully loaded trailer. • Case III: A systematic approach for the design of an adaptive cruise control is proposed. The resulting parameterized design enables intuitive tuning directly related to comfort and safety of the driving behavior and significantly reduces tuning effort. The design is validated on an Audi S8, performing on-the-road experiments. • Case IV: The design of a cooperative adaptive cruise control is presented, focusing on the feasibility of implementation. Correspondingly, a necessary and sufficient condition for string stability is derived. The design is experimentally tested using two Citroën C4’s, improving traffic throughput with respect to standard adaptive cruise control functionality, while guaranteeing string stability of the traffic flow. The case studies consider representative automotive control problems, in the sense that typical challenges are addressed, being variable operating conditions and global performance qualifiers. Based on the case studies, a generic classification of automotive control problems is derived, distinguishing problems at i) a full-vehicle level, ii) an in-vehicle level, and iii) a component level. The classification facilitates a characterization of automotive control problems on the basis of the required modeling and the specification of corresponding performance requirements. Full-vehicle level functionality focuses on the specification of desired vehicle behavior for the vehicle as a whole. Typically, the required modeling is limited, whereas the translation of global performance qualifiers into control-oriented performance requirements can be difficult. In-vehicle level functionality focuses on actual control of the (complex) vehicle dynamics. The modeling and the specification of performance requirements are typically influenced by a wide variety of operating conditions. Furthermore, the case studies represent practical application examples that are specifically suitable to apply a specific set of state-of-the-art controller synthesis methods, being robust control, model predictive control, and gain scheduling or linear parameter varying control. The case studies show the applicability of these methods in practice. Nevertheless, the theoretical complexity of the methods typically translates into a high computational burden, while insight in the resulting controller decreases, complicating, for example, (online) fine-tuning of the controller. Accordingly, more efficient algorithms and dedicated tools are required to improve practical implementation of controller synthesis methods

An LP V/H∞ integrated Vehicle Dynamic Controller

International audienceThis paper is concerned with the design and analysis of a new multivariable LP V /H∞ (Linear Parameter Varying) robust control design strategy for Global Chassis Control. The main objective of this study is to handle critical driving situations by activating several controller subsystems in a hierarchical way. The proposed solution consists indeed in a two-step control strategy that uses semi-active suspensions, active steering and electro-mechanical braking actuators. The main idea of the strategy is to schedule the 3 control actions (braking, steering and suspension) according to the driving situation evaluated by a specific monitor. Indeed, on one hand, rear braking and front steering are used to enhance the vehicle yaw stability and lateral dynamics, and on the other hand, the semi-active suspensions to improve comfort and car handling performances. Thanks to the LP V /H∞ framework, this new approach allows to reach a smooth coordination between the various actuators, to ensure robustness and stability of the proposed solution, and to significantly improve the vehicle dynamical behavior. Simulations have been performed on a complex full vehicle model which has been validated using data obtained from experimental tests on a real Renault Mégane Coupé. Moreover, the suspension system uses Magneto-Rheological dampers whose characteristics have been obtained through experimental identification tests. A comparison between the proposed LPV/H∞ control strategy and a classical LTI/H∞ controller is performed using the same simulation scenarios and confirms the effectiveness of this approach