[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

Incremental Sheet Forming (ISF) is a progressive metal forming process, where the deformation occurs locally around the point of contact between a tool and the metal sheet. The final work-piece is formed cumulatively by the movements of the tool, which is usually attached to a CNC milling machine. The ISF process is dieless in nature and capable of producing different parts of geometries with a universal tool. The tooling cost of ISF can be as low as 5–10% compared to the conventional sheet metal forming processes. On the laboratory scale, the accuracy of the parts created by ISF is between ±1.5 mm and ±3mm. However, in order for ISF to be competitive with a stamping process, an accuracy of below ±1.0 mm and more realistically below ±0.2 mm would be needed. In this work, we first studied the ISF deformation process by a simplified phenomenal linear model and employed a predictive controller to obtain an optimised tool trajectory in the sense of minimising the geometrical deviations between the targeted shape and the shape made by the ISF process. The algorithm is implemented at a rig in Cambridge University and the experimental results demonstrate the ability of the model predictive controller (MPC) strategy. We can achieve the deviation errors around ±0.2 mm for a number of simple geometrical shapes with our controller. The limitations of the underlying linear model for a highly nonlinear problem lead us to study the ISF process by a physics based model. We use the elastoplastic constitutive relation to model the material law and the contact mechanics with Signorini’s type of boundary conditions to model the process, resulting in an infinite dimensional system described by a partial differential equation. We further developed the computational method to solve the proposed mathematical model by using an augmented Lagrangian method in function space and discretising by finite element method. The preliminary results demonstrate the possibility of using this model for optimal controller design.

This thesis deals with chemical plants constituted by parallel batch-continuous production lines with shared resources. For such plants, it is highly desirable to have optimal operation schedules which determine the starting times of the various batch processes and the flow rates of the continuous processes in order to maximize the average plant productivity and to have a continuous production without interruptions. This optimization problem is constrained by the limitation of the resources that are shared by the reactors and by the capacities of the various devices that constitute the plant. Such plants are "hybrid" by nature because they combine both continuous-time dynamics and discrete-event dynamics. The formalism of "Hybrid Automata" is there fore well suited for the design of plant models. The first contribution of this thesis is the development of a hybrid automaton model of the chemical plant in the Matlab-Simulink-Stateflow environment and its use for the design of an optimal periodic schedule that maximises the plant productivity. Using a sensitivity analysis and the concept of Poincaré; map, it is shown that the optimal schedule is a stable limit cycle of the hybrid system that attracts the system trajectories starting in a wide set of initial conditions. The optimal periodic schedule is valid under the assumption that the hybrid model is an exact description of the plant. Under perturbations on the plant parameters, it is shown that two types of problems may arise. The first problem is a drift of the hybrid system trajectory which can either lead to a convergence to a new stable sub-optimal schedule or to a resource conflict. The second problem is a risk of overflow or underflow of the output buffer tank. The second contribution of the thesis is the analysis of feedback control strategies to avoid these problems. For the first problem, a control policy based on a model predictive control (MPC) approach is proposed to avoid resource conflicts. The feedback control is run on - line with the hybrid Simulink-Stateflow simulator used as an internal model. For the solution of the second problem, a classical PI control is used. The goal is not only to avoid over- or under-filling of the tank but also to reduce the amplitude of outflow rate variations as much as possible. A methodological analysis for the PI controller tuning is presented in order to achieve an acceptable trade-off between these conflicting objectives.

The goal of this dissertation is to evaluate the effectiveness of model predictive control (MPC) for a magnetically suspended flywheel energy storage uninterruptible power supply (FlyUPS). The reason this research topic was selected was to determine if an advanced control technique such as MPC could perform better than a classical control approach such as decentralised Proportional-plus-Differential (PD) control. Based on a literature study of the FlyUPS system and the MPC strategies available, two MPC strategies were used to design two possible MPC controllers were designed for the FlyUPS, namely a classical MPC algorithm that incorporates optimisation techniques and the MPC algorithm used in the MATLAB® MPC toolbox™. In order to take the restrictions of the system into consideration, the model used to derive the controllers was reduced to an order of ten according to the Hankel singular value decomposition of the model. Simulation results indicated that the first controller based on a classical MPC algorithm and optimisation techniques was not verified as a viable control strategy to be implemented on the physical FlyUPS system due to difficulties obtaining the desired response. The second controller derived using the MATLAB® MPC toolbox™ was verified to be a viable control strategy for the FlyUPS by delivering good performance in simulation. The verified MPC controller was then implemented on the FlyUPS. This implementation was then analysed in order to validate that the controller operates as expected through a comparison of the simulation and implementation results. Further analysis was then done by comparing the performance of MPC with decentralised PD control in order to determine the advantages and limitations of using MPC on the FlyUPS. The advantages indicated by the evaluation include the simplicity of the design of the controller that follows directly from the specifications of the system and the dynamics of the system, and the good performance of the controller within the parameters of the controller design. The limitations identified during this evaluation include the high computational load that requires a relatively long execution time, and the inability of the MPC controller to adapt to unmodelled system dynamics. Based on this evaluation MPC can be seen as a viable control strategy for the FlyUPS, however more research is needed to optimise the MPC approach to yield significant advantages over other control techniques such as decentralised PD control. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013

The main topic of this thesis is integer quadratic programming with applications to problems arising in the areas of automatic control and communication. One of the most widespread modern control methods is Model Predictive Control (MPC). In each sampling time, MPC requires the solution of a Quadratic Programming (QP) problem. To be able to use MPC for large systems, and at high sampling rates, optimization routines tailored for MPC are used. In recent years, the range of application of MPC has been extended to so-called hybrid systems. Hybrid systems are systems where continuous dynamics interact with logic. When this extension is made, binary variables are introduced in the problem. As a consequence, the QP problem has to be replaced by a far more challenging Mixed Integer Quadratic Programming (MIQP) problem, which is known to have a computational complexity which grows exponentially in the number of binary optimization variables. In modern communication systems, multiple users share a so-called multi-access channel. To estimate the information originally sent, a maximum likelihood problem involving binary variables can be solved. The process of simultaneously estimating the information sent by multiple users is called Multiuser Detection (MUD). In this thesis, the problem to efficiently solve MIQP problems originating from MPC and MUD is addressed. Four different algorithms are presented. First, a polynomial complexity preprocessing algorithm for binary quadratic programming problems is presented. By using the algorithm, some, or all, binary variables can be computed efficiently already in the preprocessing phase. In numerical experiments, the algorithm is applied to unconstrained MPC problems with a mixture of real valued and binary valued control signals, and the result shows that the performance gain can be significant compared to solving the problem using branch and bound. The preprocessing algorithm has also been applied to the MUD problem, where simulations have shown that the bit error rate can be significantly reduced compared to using common suboptimal algorithms. Second, an MIQP algorithm tailored for MPC is presented. The algorithm uses a branch and bound method where the relaxed node problems are solved by a dual active set QP algorithm. In this QP algorithm, the KKT systems are solved using Riccati recursions in order to decrease the computational complexity. Simulation results show that both the proposed QP solver and MIQP solver have lower computational complexity compared to corresponding generic solvers. Third, the dual active set QP algorithm is enhanced using ideas from gradient projection methods. The performance of this enhanced algorithm is shown to be comparable with the existing commercial state-of-the-art QP solver \cplex for some random linear MPC problems. Fourth, an algorithm for efficient computation of the search directions in an SDP solver for a proposed alternative SDP relaxation applicable to MPC problems with binary control signals is presented. The SDP relaxation considered has the potential to give a tighter lower bound on the optimal objective function value compared to the QP relaxation that is traditionally used in branch and bound for these problems, and its computational performance is better than the ordinary SDP relaxation for the problem. Furthermore, the tightness of the different relaxations is investigated both theoretically and in numerical experiments. / This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the Linköping University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this material, you agree to all provisions of the copyright laws protecting it.

Numerical algorithms for efficiently solving optimal control problems are important for commonly used advanced control strategies, such as model predictive control (MPC), but can also be useful for advanced estimation techniques, such as moving horizon estimation (MHE). In MPC, the control input is computed by solving a constrained finite-time optimal control (CFTOC) problem on-line, and in MHE the estimated states are obtained by solving an optimization problem that often can be formulated as a CFTOC problem. Common types of optimization methods for solving CFTOC problems are interior-point (IP) methods, sequential quadratic programming (SQP) methods and active-set (AS) methods. In these types of methods, the main computational effort is often the computation of the second-order search directions. This boils down to solving a sequence of systems of equations that correspond to unconstrained finite-time optimal control (UFTOC) problems. Hence, high-performing second-order methods for CFTOC problems rely on efficient numerical algorithms for solving UFTOC problems. Developing such algorithms is one of the main focuses in this thesis. When the solution to a CFTOC problem is computed using an AS type method, the aforementioned system of equations is only changed by a low-rank modification between two AS iterations. In this thesis, it is shown how to exploit these structured modifications while still exploiting structure in the UFTOC problem using the Riccati recursion. Furthermore, direct (non-iterative) parallel algorithms for computing the search directions in IP, SQP and AS methods are proposed in the thesis. These algorithms exploit, and retain, the sparse structure of the UFTOC problem such that no dense system of equations needs to be solved serially as in many other algorithms. The proposed algorithms can be applied recursively to obtain logarithmic computational complexity growth in the prediction horizon length. For the case with linear MPC problems, an alternative approach to solving the CFTOC problem on-line is to use multiparametric quadratic programming (mp-QP), where the corresponding CFTOC problem can be solved explicitly off-line. This is referred to as explicit MPC. One of the main limitations with mp-QP is the amount of memory that is required to store the parametric solution. In this thesis, an algorithm for decreasing the required amount of memory is proposed. The aim is to make mp-QP and explicit MPC more useful in practical applications, such as embedded systems with limited memory resources. The proposed algorithm exploits the structure from the QP problem in the parametric solution in order to reduce the memory footprint of general mp-QP solutions, and in particular, of explicit MPC solutions. The algorithm can be used directly in mp-QP solvers, or as a post-processing step to an existing solution. / Numeriska algoritmer för att effektivt lösa optimala styrningsproblem är en viktig komponent i avancerade regler- och estimeringsstrategier som exempelvis modellprediktiv reglering (eng. model predictive control (MPC)) och glidande horisont estimering (eng. moving horizon estimation (MHE)). MPC är en reglerstrategi som kan användas för att styra system med flera styrsignaler och/eller utsignaler samt ta hänsyn till exempelvis begränsningar i styrdon. Den grundläggande principen för MPC och MHE är att styrsignalen och de estimerade variablerna kan beräknas genom att lösa ett optimalt styrningsproblem. Detta optimeringsproblem måste lösas inom en kort tidsram varje gång som en styrsignal ska beräknas eller som variabler ska estimeras, och således är det viktigt att det finns effektiva algoritmer för att lösa denna typ av problem. Två vanliga sådana är inrepunkts-metoder (eng. interior-point (IP)) och aktivmängd-metoder (eng. active-set (AS)), där optimeringsproblemet löses genom att lösa ett antal enklare delproblem. Ett av huvudfokusen i denna avhandling är att beräkna lösningen till dessa delproblem på ett tidseffektivt sätt genom att utnyttja strukturen i delproblemen. Lösningen till ett delproblem beräknas genom att lösa ett linjärt ekvationssystem. Detta ekvationssystem kan man exempelvis lösa med generella metoder eller med så kallade Riccatirekursioner som utnyttjar strukturen i problemet. När man använder en AS-metod för att lösa MPC-problemet så görs endast små strukturerade ändringar av ekvationssystemet mellan varje delproblem, vilket inte har utnyttjats tidigare tillsammans med Riccatirekursionen. I denna avhandling presenteras ett sätt att utnyttja detta genom att bara göra små förändringar av Riccatirekursionen för att minska beräkningstiden för att lösa delproblemet. Idag har behovet av  parallella algoritmer för att lösa MPC och MHE problem ökat. Att algoritmerna är parallella innebär att beräkningar kan ske på olika delar av problemet samtidigt med syftet att minska den totala verkliga beräkningstiden för att lösa optimeringsproblemet. I denna avhandling presenteras parallella algoritmer som kan användas i både IP- och AS-metoder. Algoritmerna beräknar lösningen till delproblemen parallellt med ett förutbestämt antal steg, till skillnad från många andra parallella algoritmer där ett okänt (ofta stort) antal steg krävs. De parallella algoritmerna utnyttjar problemstrukturen för att lösa delproblemen effektivt, och en av dem har utvärderats på parallell hårdvara. Linjära MPC problem kan också lösas genom att utnyttja teori från multiparametrisk kvadratisk programmering (eng. multiparametric quadratic programming (mp-QP)) där den optimala lösningen beräknas i förhand och lagras i en tabell, vilket benämns explicit MPC. I detta fall behöver inte MPC problemet lösas varje gång en styrsignal beräknas, utan istället kan den förberäknade optimala styrsignalen slås upp. En nackdel med mp-QP är att det krävs mycket plats i minnet för att spara lösningen. I denna avhandling presenteras en strukturutnyttjande algoritm som kan minska behovet av minne för att spara lösningen, vilket kan öka det praktiska användningsområdet för mp-QP och explicit MPC.

A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.

An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency.

This master’s thesis examines how a small MIMO lighting system can be identified and controlled. Two approaches are examined and compared; the first approach is a dynamic model using state space representation, where the system identification technique is Recursive Least Square, RLS, and the controller is an LQG controller; the second approach is a static model derived from the physical properties of light and a feedback feed-forward controller consisting of a PI controller coupled with a Control Allocation, CA, technique. For the studied system, the CA-PI approach significantly outperforms the LQG-RLS approach, which leads to the conclusion that the system’s static properties are predominant compared to the dynamic properties.