Platooning is when vehicles are driving close aftereach other at a set distance and it is a promising method toimprove the traffic of todays infrastructure. Several approachesfor platooning can be taken and in this paper a vision-basedimplementation has been studied. With a camera that detectsthe orientation of a marker attached to a small vehicle, it hasbeen examined how the pitch of the marker can be exploitedto perform vision-based platooning considering the road grade.A model predictive control strategy is presented to maintain aplatooning distance with the potential of utilizing road topography.The aim of the project was to use this information tominimize brake and motor forces of the platooning vehicle. Thestrategy was based on relative vehicle states, detectable by acamera. The model predictive controller was implemented onsmall robotic vehicles and tested on a flat surface. The controllerwas successful in converging towards the wanted distance andcapable of reaching a steady state speed. The results showed thatit took 15 seconds for the system to reach a steady state. / Konvojkörning är när fordon kör nära efter varandra med ett bestämt avstånd och det är en lovande metod för att förbättra trafiken i dagens infrastruktur. Åtskilliga tillvägagångssätt kan tas och i denna artikel så har ett visionsbaserat genomförande studerats. Med en kamera som upptäcker orienteringen av en markör som sitter på ett litet fordon så har det undersökts hur markörens lutningsvinkel kan utnyttjas för att utföra en visionsbaserad konvojkörning med hänsyn till vägens lutning. En model predictive control-strategi är presenterad för att bibehålla ett bestämt konvojavstånd med möjligheten att använda vägens topografi. Projektets mål var att använda denna information för att minska bromsoch motorkrafter för det konvojkörande fordonet. Strategin grundades på fordonets relativa tillstånd som var detekterbara med en kamera. En model predicitve control utfärdades på små robotfordon och testades på en platt yta. Kontrollern var framgångsrik i att konvergera mot det önskade avståndet och kapabel till att nå ett stabilt tillstånd för hastigheten. Resultaten t det tog 15 sekunder för fordonets hastighet att nå det stabila tillståndet. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Autonomous vehicles have in recent years grownin popularity. An autonomous car has the potential to safelymaneuver in an efficient manner. This in combination with thefocus on increased road safety has put higher emphasis onimplementing an overtaking controller. Model Predictive Control(MPC) is very useful because it can handle linear constraintsand works for autonomous driving. I implemented the controlsystem in Python and did tests on its overtake capability usingdifferent velocities, car distances and initial speeds. Constraintswere implemented so that the autonomous vehicle did not collidewith another vehicle or drive outside the road when overtaking.The results show that a safe overtake could be performed undercertain conditions. The MPC algorithm is proven useful butdifficult to optimize. / Autonoma fordon har lyckats locka till sig mer populäritet under de senaste åren. En autonom bil har möjligheten att manövrera på ett säkert och effektivt sätt. Detta i kombination med ett fokus att öka vägsäkerheten har lagt större press på att implementera reglersystem för omkörningar. Modell prediktiv reglering (MPC) är användbar för den kan hantera linjära bivillkor och fungerar till autonomon körning. Ett reglersystem är implementerat i Python och testades på sin omkörningförmåga med olika hastigheter, avstånd och begynnelse hastigheter. Implementationen utformades med bivillkor som att det autonoma fordonet inte ska krocka med ett annat fordon eller köra utanför vägen i en omkörning. Resultaten visar att det gick att köra om på ett säkert sätt med vissa förutsättningar. MPC algoritmen har visat sig användbar men svår att optimera. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

A major challenge in the transportation industry is how to reduce the emissions of greenhouse gases. One way of achieving this in vehicles is to drive more fuel-efficiently. One recently developed technique that has been successful in reducing the fuel consumption is the look-ahead cruise controller, which utilizes future conditions such as road topography. In this this thesis, similar methods are used in order to reduce the fuel consumption of heavy-duty vehicles driving in environments where the required and desired velocity vary. The main focus is on vehicles in urban driving, which must alter their velocity due to, for instance, changing legal speed restrictions and the presence of intersections. The driving missions of such vehicles are here formulated as optimal control problems. In order to restrict the vehicle to drive in a way that does not deviate too much from a normal way of driving, constraints on the velocity are imposed based on statistics from real truck operation. In a first approach, the vehicle model is based on forces and the cost function involves the consumed energy. This problem is solved both offline using Pontryagin's maximum principle and online using a model predictive controller with a quadratic program formulation. Simulations show that 7 % energy can be saved without increasing the trip time nor deviating from a normal way of driving. In a second approach, the vehicle model is extended to include an engine and a gearbox with the objective of minimizing the fuel consumption. A fuel map for the engine and a polynomial function for the gearbox losses are extracted from experimental data and used in the model. This problem is solved using dynamic programming taking into consideration gear changes, coasting with gear and coasting in neutral. Simulations show that by allowing the use of coasting in neutral gear, 13 % fuel can be saved without increasing the trip time or deviating from a normal way of driving. Finally, an implementation of a rule-based controller into an advanced vehicle model in highway driving is performed. The controller identifies sections of downhills where fuel can be saved by coasting in neutral gear. / En stor utmaning för transportsektorn är hur utsläppen av växthusgaser ska minskas. Detta kan åstadkommas i fordon genom att köra bränslesnålare. En nyligen utvecklad teknik som har varit framgångsrik i att minska bränsleförbrukningen är framförhållningsreglering, som använder framtida förhållanden så som vägtopografi. I denna avhandling används liknande metoder för att minska bränsleförbrukningen i tunga fordon som kör i miljöer där önskad och tvingad hastighet varierar. Fokus ligger framförallt på fordon i stadskörning, där hastigheten måste varieras beroende på bland annat hastighetsbegränsningar och korsningar. Denna typ av körning formuleras här som optimala reglerproblem. För att hindra fordonet från att avvika för mycket från ett normalt körbeteende sätts begränsningar på tillåten hastighet baserat på statistik från verklig körning. Problemet angrips först genom att använda en fordonsmodell baserad på krafter och en kriteriefunktion innehållande energiförbrukning. Problemet löses både offline med Pontryagin's maximum princip och online med modellprediktiv reglering baserad på kvadratisk programmering. Simuleringar visar att 7 % energi kan sparas utan att öka körtiden eller avvika från ett normalt körbeteende. Problemet angrips sedan genom att utöka fordonsmodellen till att också innehålla motor och växellåda med målet att minimera bränsleförbrukningen. Specifik bränsleförbrukning och en polynomisk approximation av förlusterna i växellådan är extraherade från experiment och används i simuleringarna. Problemet löses genom dynamisk programmering som tar hänsyn till växling, släpning och frirullning. Simuleringar visar att 13 % bränsle kan sparas utan att öka körtid eller avvika från normalt körbeteende genom att tillåta frirullning. Slutligen görs en implementering av en regelbaserad regulator på en avancerad fordonsmodell för ett fordon i motorvägskörning. Regulatorn identifierar sektioner med nedförsbackar där bränsle kan sparas genom frirulllning. / <p>QC 20171011</p>

For vehicles equipped with electric motors there exist a possibility to recuperate energy during deceleration. This master’s thesis presents a driver support function, a Predictive Deceleration Control (PDC), that warns the driver when to release the accelerator pedal. If the driver follows the instructions from the function the vehicle will decelerate to an appropriate speed at the upcoming road feature, such as a roundabout. The function should both improve energy consumption and enhance driver comfort. This master’s thesis focused on how such a function can be implemented and a proof of concept was designed in a Matlab/Simulink environment. Within the scope of the proof of concept the function should warn the driver to release the accelerator pedal ahead of roundabouts, intersections, speed limit signs and stop signs. With the help of map information and the vehicle most probable path, the distance to the road features could be determined and the predicted braking distance to these road features could be calculated by the function. A feedforward controller was used to control the deceleration phase and was based on a longitudinal vehicle model. The PDC was tested both in a Lynk &amp; Co 01 and CEVT’s dynamic simulator and the results proved that the function can be implemented in for example a Lynk &amp; Co 01 without any additional hardware needed. However, it requires software that can provide the function with map information. During the tests performed during the master’s thesis, map information was acquired with a frequency of 1 Hz, but for the function to become more robust a higher update frequency is required. / För fordon utrustade med elektriska motorer finns det möjlighet till att återvinna energi under inbromsning. Det här examensarbetet presenterar ett förarhjälpmedel som varnar förare när denna ska släppa accelerationspedalen. Om föraren följer uppmaningen kommer bilen att minska hastigheten till en lämplig ingångshastighet för kommande vägobjekt, till exempel en cirkulationsplats. Funktionen ska både förbättra energiförbrukningen och öka förarstödet. Det här examensarbetet fokuserade på hur en sådan funktion kan implementeras och ett exempel på koncept utvecklades i en Matlab/Simulink miljö. Under utvecklingen av funktionen skulle den prediktiva retardationsregle- ringen varna förare att släppa accelerationspedalen innan cirkulationsplatser, korsningar, hastighetsskyltar och stoppskyltar. Men hjälp av kartdata och fordonets mest troliga väg kunde distansen till nästa vägobjekt bestämmas och den förväntade retardationsdistansen beräknas. Dessa värden användes sedan för att bestämma när föraren ska varnas. Framkopplingsreglering användes för att reglera retardationsförloppet och baserades på en longitudinell fordonsmodell. Den utvecklade funktionen testades både i en Lynk &amp; Co 01 och CEVT:s dynamiska simulator och resultaten visade att funktionen kan implementeras i till exempel en Lynk &amp; Co 01 utan någon extra hårdvara. Dock kräver en implementation av funktionen mjukvara som kan bistå funktionen med kartdata och mest trolig väg för fordonet. Under de utförda testerna i bil samlades kartdata och bilens position in med en frekvens av 1 Hz, men för att funktionens tillförlitlighet ska öka krävs en högre uppdateringsfrekvens.

A method to adapt the Generalized Predictive Control parameters to improve broadband disturbance rejection was developed and tested. The effect of the parameters on disturbance rejection has previously been poorly understood and a trial and error method was used to achieve adequate results. This dissertation provides insight on the effect of the parameters, as well as an adaptive tuning method to adjust them. The study begins by showing the effect of the four GPC parameters, the control and prediction horizons, control weighting &lambda , and order, on the disturbance rejection and control effort of a vibrating plate. It is shown that the effect of increases in the control and prediction horizon becomes negligible after a certain point. This occurs at nearly the same point for a variety of &lambda 's and orders, and hence they can be eliminated from the tuning space. The control effort and closed-loop disturbance rejection are shown to be highly dependant on &lambda and order, thereby becoming the parameters that need to be tuned. The behavior is categorized into various groups and further investigated. The pole and zero locations of the closed-loop system are examined to reveal how GPC gains control and how it can fail for non-minimum phase plants. A set of fuzzy logic modules is developed to adapt &lambda with order fixed, and conversely to adapt order with &lambda fixed. The effectiveness of the method is demonstrated in both numerical simulations and laboratory experiments. / Ph. D.

This work proposes a series of driver assistance technologies that enable blind persons to safely and independently operate an automobile on standard public roads. Such technology could additionally benefit sighted drivers by augmenting vision with suggestive cues during normal and low-visibility driving conditions. This work presents a non-visual human-computer interface system with passive and adaptive controlling software to realize this type of driver assistance technology. The research and development behind this work was made possible through the Blind Driver Challenge® initiative taken by the National Federation of the Blind. The instructional technologies proposed in this work enable blind drivers to operate an automobile through the provision of steering wheel angle and speed cues to the driver in a non-visual method. This paradigm imposes four principal functionality requirements: Perception, Motion Planning, Reference Transformations, and Communication. The Reference Transformation and Communication requirements are the focus of this work and convert motion planning trajectories into a series of non-visual stimuli that can be communicated to the human driver. This work proposes two separate algorithms to perform the necessary reference transformations described above. The first algorithm, called the Passive Non-Visual Interface Driver, converts the planned trajectory data into a form that can be understood and reliably interacted with by the blind driver. This passive algorithm performs the transformations through a method that is independent of the driver. The second algorithm, called the Adaptive Non-Visual Interface Driver, performs similar trajectory data conversions through methods that adapt to each particular driver. This algorithm uses Model Predictive Control supplemented with Artificial Neural Network driver models to generate non-visual stimuli that are predicted to induce optimal performance from the driver. The driver models are trained online and in real-time with a rapid training approach to continually adapt to changes in the driver's dynamics over time. The communication of calculated non-visual stimuli is subsequently performed through a Non-Visual Interface System proposed by this work. This system is comprised of two non-visual human computer interfaces that communicate driving information through haptic stimuli. The DriveGrip interface is pair of vibro-tactile gloves that communicate steering information through the driver's hands and fingers. The SpeedStrip interface is a vibro-tactile cushion fitted on the driver's seat that communicates speed information through the driver's legs and back. The two interfaces work simultaneously to provide a continuous stream of directions to the driver as he or she navigates the vehicle. / Ph. D.

This dissertation focuses on constrained control of two applications: helicopter and ocean current turbines (OCT). A major contribution in the helicopter application is a novel model predictive control (MPC) framework for helicopter shipboard operations in high demanding sea-based conditions. A complex helicopter-ship dynamics interface has been developed as a system of implicit nonlinear ordinary differential equations to capture essential characteristics of the nonlinear helicopter dynamics, the ship dynamics, and the ship airwake interactions. Various airwake models such as Control Equivalent Turbulence Inputs (CETI) model and Computation Fluid Dynamics (CFD) data of the airwake are incorporated in the interface to describe a realistic model of the shipborne helicopter. The feasibility of the MPC design is investigated using two case studies: automatic deck landing during the ship quiescent period in sea state 5, and lateral reposition toward the ship in different wind-over-deck conditions. To improve the overall MPC performance, an updating scheme for the internal model of the MPC is proposed using linearization around operating points. A mixed-integer MPC algorithm is also developed for helicopter precision landing on moving decks. The performance of this control structure is evaluated via numerical simulations of the automatic deck landing in adverse conditions such as landing on up-stroke, and down-stroke moving decks with high energy indices. Kino-dynamic motion planning for coordinated maneuvers to satisfy the helicopter-ship rendezvous conditions is implemented via mixed integer quadratic programming. In the OCT application, the major contribution is that a new idea is leveraged from helicopter blade control by introducing cyclic blade pitch control in OCT. A minimum energy, constrained control method, namely Output Variance Constrained (OVC) control is studied for OCT flight control in the presence of external disturbances. The minimum achievable output variance bounds are also computed and a parametric study of design parameters is conducted to evaluate their influence on the OVC performance. The performance of the OVC control method is evaluated both on the linear and nonlinear OCT models. Furthermore, control design for the OCT with sensor failures is also examined. Lastly, the MPC strategy is also investigated to improve the OCT flight control performance in simultaneous satisfaction of multiple constraints and to avoid blade stall. / Ph. D.

Advanced Driver Assistance Systems (ADAS) and Autonomous Ground Vehicles (AGV) have the potential to increase road transportation safety, environmental gains, and passenger comfort. The advent of Electric Vehicles has also facilitated greater flexibility in powertrain architectures and control capabilities. Path Tracking controllers that provide steering input are used to execute lateral maneuvers or model the response of a vehicle during cornering. Direct Yaw Control using Torque Vectoring has the potential to improve vehicle's transient cornering stability and modify its steady state handling characteristics during lateral maneuvers. In the first part of this thesis, the transient dynamics of an existing baseline Path Tracking controller is improved using a transient Torque Vectoring algorithm. The existing baseline Path Tracking controller is evaluated, using a linearized system, for a range of vehicle and controller parameters. The effect of implementing transient Torque Vectoring along with the baseline Path Tracking controller is then studied for the same parameter range. The linear analysis shows, in both time and frequency domain, that the transient Torque Vectoring improves vehicle response and stability during cornering. A Torque Vectoring controller is developed in Linear Adaptive Model Predictive Control framework and it's performance is verified in simulation using Simulink and CarSim. The second part of the thesis analyzes the tradeoff enabled by steady state Torque Vectoring between improved limit handling capability through optimal tire force allocation and drivability demonstrated by understeer gradient. Optimal tire force allocation prescribes equal usage in all four tires during maneuvers. This is enabled using steering and Torque Vectoring. An analytical proof is presented which demonstrates that implementation of this optimal tire force allocation results in neutralsteering handling characteristics for the vehicle. The optimal tire force allocation strategy is formulated as a minimax optimization problem. A two-track vehicle model is simulated for this strategy, and it verified the analytical proof by displaying neutralsteering behavior. / Master of Science / Advanced Driver Assistance Systems (ADAS) and Autonomous Ground Vehicles (AGVs) have the potential to increase road transportation safety, environmental gains, passenger comfort and passenger productivity. The advent of Electric Vehicles (EVs) has also facilitated greater flexibility in powertrain configurations and capabilities that facilitate the implementation of Torque Vectoring (TV), which is a method of applying differential torques to laterally opposite wheels to enhance the cornering performance of ground vehicles. Path Tracking (PT) controllers that provide steering input to the vehicles are traditionally used for lateral control in AGVs and ADAS features. The goal of this thesis is to develop Torque Vectoring algorithms to improve a vehicle's stability and shape its steady state behaviour through a corner during low lateral acceleration maneuvers. An existing baseline Path Tracking controller is selected and evaluated. The effect of implementing Torque Vectoring along with this Path Tracking controller is studied and it is found to improve the stability of the vehicle during cornering. This is verified in simulation by designing and implementing the Torque Vectoring algorithm. Finally, a Torque Vectoring strategy is proposed to manage the handling of the vehicle during low acceleration cornering.

[ES] La gran mayoría de procesos del mundo real tienen incertidumbres inherentes, las cuales, al ser consideradas en el proceso de modelado, se puede obtener una representación que describa con la mayor precisión posible el comportamiento del proceso real. En la mayoría de casos prácticos, se considera que éstas tienen un comportamiento estocástico y sus descripciones como distribuciones de probabilidades son conocidas. Las estrategias de MPC estocástico están desarrolladas para el control de procesos con incertidumbres de naturaleza estocástica, donde el conocimiento de las propiedades estadísticas de las incertidumbres es aprovechado al incluirlo en el planteamiento de un problema de control óptimo (OCP). En éste, y contrario a otros esquemas de MPC, las restricciones duras son relajadas al reformularlas como restricciones de tipo probabilísticas con el fin de reducir el conservadurismo. Esto es, se permiten las violaciones de las restricciones duras originales, pero tales violaciones no deben exceder un nivel de riesgo permitido. La no-convexidad de tales restricciones probabilísticas hacen que el problema de optimización sea prohibitivo, por lo que la mayoría de las estrategias de MPC estocástico en la literatura se diferencian en la forma en que abordan tales restricciones y las incertidumbres, para volver el problema computacionalmente manejable. Por un lado, están las estrategias deterministas que, fuera de línea, convierten las restricciones probabilísticas en unas nuevas de tipo deterministas, usando la propagación de las incertidumbres a lo largo del horizonte de predicción para ajustar las restricciones duras originales. Por otra parte, las estrategias basadas en escenarios usan la información de las incertidumbres para, en cada instante de muestreo, generar de forma aleatoria un conjunto de posibles evoluciones de éstas a lo largo del horizonte de predicción. De esta manera, convierten las restricciones probabilísticas en un conjunto de restricciones deterministas que deben cumplirse para todos los escenarios generados. Estas estrategias se destacan por su capacidad de incluir en tiempo real información actualizada de las incertidumbres. No obstante, esta ventaja genera inconvenientes como su gasto computacional, el cual aumenta conforme lo hace el número de escenarios y; por otra parte, el efecto no deseado en el problema de optimización, causado por los escenarios con baja probabilidad de ocurrencia, cuando se usa un conjunto de escenarios pequeño. Los retos mencionados anteriormente orientaron esta tesis hacia los enfoques de MPC estocástico basado en escenarios, produciendo tres contribuciones principales. La primera consiste en un estudio comparativo de un algoritmo del grupo determinista con otro del grupo basado en escenarios; se hace un especial énfasis en cómo cada uno de estos aborda las incertidumbres, transforma las restricciones probabilísticas y en la estructura de su OCP, además de señalar sus aspectos más destacados y desafíos. La segunda contribución es una nueva propuesta de algoritmo MPC, el cual se basa en escenarios condicionales, diseñado para sistemas lineales con incertidumbres correlacionadas. Este esquema aprovecha la existencia de tal correlación para convertir un conjunto de escenarios inicial de gran tamaño en un conjunto de escenarios más pequeño con sus probabilidades de ocurrencia, el cual conserva las características del conjunto inicial. El conjunto reducido es usado en un OCP en el que las predicciones de los estados y entradas del sistema son penalizadas de acuerdo con las probabilidades de los escenarios que las componen, dando menor importancia a los escenarios con menores probabilidades de ocurrencia. La tercera contribución consiste en un procedimiento para la implementación del nuevo algoritmo MPC como gestor de la energía en una microrred en la que las previsiones de las energías renovables y las cargas están correlacionadas. / [CA] La gran majoria de processos del món real tenen incerteses inherents, les quals, en ser considerades en el procés de modelatge, es pot obtenir una representació que descriga amb la major precisió possible el comportament del procés real. En la majoria de casos pràctics, es considera que aquestes tenen un comportament estocàstic i les seues descripcions com a distribucions de probabilitats són conegudes. Les estratègies de MPC estocàstic estan desenvolupades per al control de processos amb incerteses de naturalesa estocàstica, on el coneixement de les propietats estadístiques de les incerteses és aprofitat en incloure'l en el plantejament d'un problema de control òptim (OCP). En aquest, i contrari a altres esquemes de MPC, les restriccions dures són relaxades en reformulades com a restriccions de tipus probabilístiques amb la finalitat de reduir el conservadorisme. Això és, es permeten les violacions de les restriccions dures originals, però tals violacions no han d'excedir un nivell de risc permès. La no-convexitat de tals restriccions probabilístiques fan que el problema d'optimització siga computacionalment immanejable, per la qual cosa la majoria de les estratègies de MPC estocàstic en la literatura es diferencien en la forma en què aborden tals restriccions i les incerteses, per a tornar el problema computacionalment manejable. D'una banda, estan les estratègies deterministes que, fora de línia, converteixen les restriccions probabilístiques en unes noves de tipus deterministes, usant la propagació de les incerteses al llarg de l'horitzó de predicció per a ajustar les restriccions dures originals. D'altra banda, les estratègies basades en escenaris usen la informació de les incerteses per a, en cada instant de mostreig, generar de manera aleatòria un conjunt de possibles evolucions d'aquestes al llarg de l'horitzó de predicció. D'aquesta manera, converteixen les restriccions probabilístiques en un conjunt de restriccions deterministes que s'han de complir per a tots els escenaris generats. Aquestes estratègies es destaquen per la seua capacitat d'incloure en temps real informació actualitzada de les incerteses. No obstant això, aquest avantatge genera inconvenients com la seua despesa computacional, el qual augmenta conforme ho fa el nombre d'escenaris i; d'altra banda, l'efecte no desitjat en el problema d'optimització, causat pels escenaris amb baixa probabilitat d'ocurrència, quan s'usa un conjunt d'escenaris xicotet. Els reptes esmentats anteriorment van orientar aquesta tesi cap als enfocaments de MPC estocàstic basat en escenaris, produint tres contribucions principals. La primera consisteix en un estudi comparatiu d'un algorisme del grup determinista amb un altre del grup basat en escenaris; on es fa un especial èmfasi en com cadascun d'aquests aborda les incerteses, transforma les restriccions probabilístiques i en l'estructura del seu problema d'optimització, a més d'assenyalar els seus aspectes més destacats i desafiaments. La segona contribució és una nova proposta d'algorisme MPC, el qual es basa en escenaris condicionals, dissenyat per a sistemes lineals amb incerteses correlacionades. Aquest esquema aprofita l'existència de tal correlació per a convertir un conjunt d'escenaris inicial de gran grandària en un conjunt d'escenaris més xicotet amb les seues probabilitats d'ocurrència, el qual conserva les característiques del conjunt inicial. El conjunt reduït és usat en un OCP en el qual les prediccions dels estats i entrades del sistema són penalitzades d'acord amb les probabilitats dels escenaris que les componen, donant menor importància als escenaris amb menors probabilitats d'ocurrència. La tercera contribució consisteix en un procediment per a la implementació del nou algorisme MPC com a gestor de l'energia en una microxarxa en la qual les previsions de les energies renovables i les càrregues estan correlacionades. / [EN] The vast majority of real-world processes have inherent uncertainties, which, when considered in the modelling process, can provide a representation that most accurately describes the behaviour of the real process. In most practical cases, these are considered to have stochastic behaviour and their descriptions as probability distributions are known. Stochastic model predictive control algorithms are developed to control processes with uncertainties of a stochastic nature, where the knowledge of the statistical properties of the uncertainties is exploited by including it in the optimal control problem (OCP) statement. Contrary to other model predictive control (MPC) schemes, hard constraints are relaxed by reformulating them as probabilistic constraints to reduce conservatism. That is, violations of the original hard constraints are allowed, but such violations must not exceed a permitted level of risk. The non-convexity of such probabilistic constraints renders the optimisation problem computationally unmanageable, thus most stochastic MPC strategies in the literature differ in how they deal with such constraints and uncertainties to turn the problem computationally tractable. On the one hand, there are deterministic strategies that, offline, convert probabilistic constraints into new deterministic ones, using the propagation of uncertainties along the prediction horizon to tighten the original hard constraints. Scenario-based approaches, on the other hand, use the uncertainty information to randomly generate, at each sampling instant, a set of possible evolutions of uncertainties over the prediction horizon. In this fashion, they convert the probabilistic constraints into a set of deterministic constraints that must be fulfilled for all the scenarios generated. These strategies stand out for their ability to include real-time updated uncertainty information. However, this advantage comes with inconveniences such as computational effort, which grows as the number of scenarios does, and the undesired effect on the optimisation problem caused by scenarios with a low probability of occurrence when a small set of scenarios is used. The aforementioned challenges steered this thesis toward stochastic scenario-based MPC approaches, and yielded three main contributions. The first one consists of a comparative study of an algorithm from the deterministic group with another one from the scenario-based group, where a special emphasis is made on how each of them deals with uncertainties, transforms the probabilistic constraints and on the structure of the optimisation problem, as well as pointing out their most outstanding aspects and challenges. The second contribution is a new proposal for a MPC algorithm, which is based on conditional scenarios, developed for linear systems with correlated uncertainties. This scheme exploits the existence of such correlation to convert a large initial set of scenarios into a smaller one with their probabilities of occurrence, which preserves the characteristics of the initial set. The reduced set is used in an OCP in which the predictions of the system states and inputs are penalised according to the probabilities of the scenarios that compose them, giving less importance to the scenarios with lower probabilities of occurrence. The third contribution consists of a procedure for the implementation of the new MPC algorithm as an energy manager in a microgrid in which the forecasts of renewables and loads are correlated. / González Querubín, EA. (2024). Scenario-Based Model Predictive Control for Systems with Correlated Uncertainties [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203887

Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: A mineral concentrate drying process consisting of a hot gas generator, a flash dryer and a feeding section is found to be the bottleneck in the platinum concentrate smelting process. This operation is used as a case study for system identification and model-based control of dryers. Based on the availability of a month's worth of dryer data obtained from a historian, a third party modelling and control software vendor is interested in the use of this data for data driven model construction and options for dryer control. The aimed contribution of this research is to use only data driven techniques and attempt an SID experiment and use of this model in a controller found in literature to be applicable to the dryer process. No first principle model was available for simulation or interpretation of results. Data were obtained for the operation from the plant historian, reduced, cleaned and investigated for deterministic information through surrogate data comparison – resulting in usable timeseries from the plant data. The best datasets were used for modelling of the flash dryer and hot gas generator operations individually, with the hot gas generator providing usable results. The dynamic, nonlinear autoregressive models with exogenous inputs were identified by means of a genetic programming with orthogonal least squares toolbox. The timeseries were reconstructed as a latent variable set, or “pseudo-embedding”, using the delay parameters as identified by average mutual information, autocorrelation and false nearest neighbours. The latent variable reconstruction resulted in a large solution space, which need to be investigated for an unknown model structure. Genetic Programming is capable of identifying unknown structures. Freerun prediction stability and sensitivity analysis were used to assess the identified best models for use in model based control. The best two models for the hot gas generator were used in a basic model predictive controller in an attempt to only track set point changes. One step ahead modelling of the flash dryer outlet air temperature was unsuccessful with the best model obtaining a validation R2 = 43%. The lack of process information contained in the available process variables are to blame for the poor model identification. One-step ahead prediction of the hot gas generator resulted in a top model with validation R2 = 77.1%. The best two hot gas generator models were implemented in a model predictive controller constructed in a real time plant data flow simulation. This controller's performance was measured against set point tracking ability. The MPC implementation was unsuccessful due to the poor freerun prediction ability of the models. The controller was found to be unable to optimise the control moves using the model. This is assigned to poor model freerun prediction ability in one of the models and a too complex freerun model structure required. It is expected that the number of degrees of freedom in the freerun model is too much for the optimiser to handle. A successful real time simulation architecture for the plant dataflow could however be constructed in the supplied software. It is recommended that further process measurements, specifically feed moisture content, feed temperature and air humidity, be included for the flash dryer; closed loop system identification be investigated for the hot gas generator; and a simpler model structure with smaller reconstructed latent variable regressor set be used for the model predictive controller. / AFRIKAANSE OPSOMMING: 'n Drogings proses vir mineraal konsentraat bestaan uit drie eenhede: 'n lug verwarmer-, 'n blitsdroeër- en konsentraat toevoer eenheid. Hierdie droeër is geïdentifiseer as die bottelnek in die platinum konsentraat smeltingsproses. Die droeër word gebruik as 'n gevallestudie vir sisteem identifikasie asook model-gebasseerder beheer van droeërs. 'n Maand se data verkry vanaf die proses databasis, het gelei tot 'n derde party industriële sagteware en beheerstelsel maatskappy se belangstelling in data gedrewe modelering en beheer opsies vir die drogings proses. Die doelwit van hierdie studie is om data gedrewe modeleringstegnieke te gebruik en die model in 'n droeër-literatuur relevante beheerder te gebruik. Geen eerste beginsel model is beskikbaar vir simulasie of interpretasie van resultate nie. Die verkrygde data is gereduseer, skoon gemaak en bestudeer om te identifiseer of die tydreeks deterministiese inligting bevat. Dit is gedoen deur die tydreeks met stochastiese surrogaat data te vergelyk. Die mees gepaste datastelle is gebruik vir modellering van die blitsdroeër en lugverwarmer afsonderlik. Die nie-liniêre, dinamiese nie-linieêre outeregressie modelle met eksogene insette was deur 'n genetiese programmering algoritme, met ortogonale minimum kwadrate, identifiseer. Die betrokke tydreeks is omskep in 'n hulp-veranderlike stel deur gebruik te maak van vertragings-parameters wat deur gemiddelde gemeenskaplike inligting, outokorrelasie en vals naaste buurman metodes verkry is. Die GP algoritme is daartoe in staat om the groot oplossings ruimte wat deur hierdie hulp-veranderlike rekonstruksie geskep word, te bestudeer vir 'n onbekende model struktuur. Die vrye vooruitskattings vermoë, asook die model sensitiwiteit is inag geneem tydens die analiese van die resultate. Die beste modelle se gepastheid tot model voorspellende beheer is gemeet deur die uitkomste van 'n sensitiwiteits analise, asook 'n vrylopende voorspelling, in oënskou te neem. Die een-stap vooruit voorspellende model van die droeër was onsusksesvol met die beste model wat slegs 'n validasie R2 = 43% kon behaal. Die gebrekkige meet instrumente in die droeër is te blameer vir die swak resultate. Die een-stap vooruit voorspellende model van die lug verwarmer wat die beste gevaar het, het 'n validasie R2 = 77.1% gehad. 'n Basiese model voorspellende beheerder is gebou deur die 2 beste modelle van slegs die lugverwarmer te gebruik in 'n intydse simulasie van die raffinadery data vloei struktuur. Hierdie beheerder se vermoë om toepaslike beheer uit te oefen, is gemeet deur die slegs die stelpunt te verander. Die beheerder was egter nie daartoe in staat om die insette te optimeer, en so die stelpunt te volg nie. Hierdie onvermoë is as gevolg van die kompleks vrylopende model struktuur wat oor die voorspellingsvenster optimeer moet word, asook die onstabiele vryvooruitspellings vermoë van die modelle. Die vermoede is dat die loslopende voorspelling te veel vryheids grade het om die insette maklik genoeg te optimeer. Die intydse simulasie van die raffinadery se datavloei struktuur was egter suksesvol. Beter meting van noodsaaklike veranderlikes vir die droër, o.a. voginhoud van die voer, voer temperatuur, asook lug humiditeit; geslotelus sisteem identifikasie vir die lugverwarmer; asook meer eenvoudige model struktuur vir gebruik in voorspellende beheer moontlik vermag deur 'n kleiner hulp veranderlike rekonstruksie te gebruik.