In this thesis, a Model Predictive Control (MPC) based re-planning and control system is proposed. The MPC re-planner will generate a collision-free path for the controlled vehicle when obstacles are detected, and the controller will make the vehicle move along the reference or re-planned path by adjusting its velocity and steering angle. The MPC re-planner and controller are built based on different vehicle dynamic models, i.e., the bicycle model and point-mass model, respectively. Simulation results show that the trajectory tracking performance when the velocity and steering are controlled simultaneously are better than using steering MPC alone. Then the effects on computational time of two critical parameters, prediction horizon and control horizon, are studied to find reasonable horizons that can enable real-time control. The robustness of the obstacle avoidance function is tested using obstacles with increasing sizes and the results show that the controlled vehicle is able to avoid a 6 m obstacle so that it can overtake other car-like vehicles in the driving process. Finally, a closed-loop one-lane road with some moving vehicles is built as a test scenario for the MPC-based re-planning and control system. According to the results, the controlled vehicle can successfully follow the centerline of the road and overtake other vehicles. / I detta examensarbete föreslås ett fordonsreglersystem baserat på modellprediktiv reglering (MPC) med en omplaneringsfunktion. Det MPC-baserade omplaneringssystemet ska hitta en kollisionsfri väg åt det reglerade fordonet när hinder upptäcks på vägen och hastighet samt styrvinkel anpassas med hjälp av reglersystemet så att fordonet kan köra längs referensvägen eller den nya vägen efter omplaneringen. Det MPC-baserade fordonsregler- och omplaneringssystemet är uppbyggt baserat på olika fordonsdynamiska modeller, cykelmodellen och en punktmassemodell. Simuleringsresultaten visar att prestandan för trajektorieföljningen är avsevärt bättre i fallet då både fordonshastighet och styrvinkel regleras jämfört med att enbart reglera styrvinkeln. Därefter studerades vilken inverkan två kritiska parametrar, förutsägelsehorisont och reglerhorisont, har på simuleringstiden för att hitta rimliga horisonter som kan möjliggöra realtidsreglering. Omplaneringsfunktionens robusthet utvärderades med hjälp av olika hinder med ökande storlekar. Resultaten visar att det reglerade fordonet har förmåga att undvika hinder upp till sex meters storlek, vilket betyder att fordonet kan passera andra billiknande fordon under körning. Slutligen, för att utvärdera det MPC-baserade regler- och omplaneringssystemet skapas ett testscenario där fordonet kör på en enfilig väg och där det finns andra fordon i rörelse samtidigt. Enligt simuleringsresultaten så kan det reglerade fordonet följa vägens mittlinje samt köra om de andra fordonen som färdas på samma väg.

In the era of Industry 4.0, conventional supply chains are undergoing a transformation into digital supply chains with the wide application of digital technologies such as big data, cloud computing, and Internet of Things. A digital supply chain is an intelligent and value-driven process that has superior features such as speed, flexibility, transparency, and real-time inventory monitoring and management. This concept is further included in the framework of Supply Chain 4.0, which emphasizes the connection between supply chain and Industry 4.0. In this context, data analytics for supply chain management presents a promising research opportunity. This thesis aims to investigate the use of data analytics in supply chain decision-making, including modelling, monitoring, and optimization. First, this thesis investigates supply chain monitoring (SCMo) using data analytics. The goal of SCMo is to raise an alarm when abnormal supply chain events occur and identify the potential reason. We propose a framework of SCMo based on a data-driven method, principal component analysis (PCA). Within this framework, supply chain data such as inventory levels and customer demand are collected, and the normal operating conditions of a supply chain are characterized using PCA. Fault detection and diagnosis are implemented by examining the monitoring statistics and variable contributions. A supply chain simulation model is developed to carry out the case studies. The results show that dynamic PCA (DPCA) successfully detected abnormal behaviour of the supply chain, such as transportation delay, low production rate, and supply shortage. Moreover, the contribution plot is shown to be effective in interpreting the abnormality and identify the fault-related variables. The method of using data-driven methods for SCMo is named data-driven SCMo in this work. Then, a further investigation of data-driven SCMo based on another statistical process monitoring method, canonical variate analysis (CVA), is conducted. CVA utilizes the state-space model of a system and determines the canonical states by maximizing the correlation between the combination of past system outputs and inputs and the combination of future outputs. A state-space model of supply chain is developed, which forms the basis of applying CVA to detect supply chain faults. The performance of CVA and PCA are assessed and compared in terms of dimensionality reduction, false alarm rate, missed detection rate, and detection delay. Case studies show that CVA identifies a smaller system order than PCA and achieves comparable performance to PCA in a lower-dimensional latent space. Next, we investigate data-driven supply chain control under uncertainty with risk taken into account. The method under investigation is reinforcement learning (RL). Within the RL framework, an agent learns an optimal policy that maps the state to action during the process of interacting with the non-deterministic environment, such that a numerical reward is maximized. The current literature regarding supply chain control focuses on conventional RL that maximizes the expected return. However, this may be not the best option for risk-averse decision makers. In this work, we explore the use of safe RL, which takes into account the concept of risk in the learning process. Two safe RL algorithms, Q-hat-learning and Beta-pessimistic Q-learning, are investigated. Case studies are carried out based on the supply chain simulator developed using agent-based modelling. Results show that Q-learning has the best performance under normal scenarios, while safe RL algorithms perform better under abnormal scenarios and are more robust to changes in the environment. Moreover, we find that the benefits of safe RL are more pronounced in a closed-loop supply chain. Finally, we investigate real-time supply chain optimization. The operational optimization problems for supply chains of realistic size are often large and complex, and solving them in real time can be challenging. This work aims to address the problem by using a deep learning-based model predictive control (MPC) technique. The MPC problem for supply chain operation is formulated based on the state space model of a supply chain, and the optimal state-input pairs are precomputed in the offline phase. Then, a deep neural network is built to map the state to input, which is then used in the online phase to reduce solution time. We propose an approach to implement the deep learning-based MPC method when there are delayed terms in the system, and a heuristic approach to feasibility recovery for mixed-integer MPC, with binary decision variables taken into account. Case studies show that compared with solving the nominal MPC problem online, deep learning-based MPC can provide near-optimal solution at a lower computational cost. / Thesis / Doctor of Philosophy (PhD)

Distributed generators (DGs) with integration of renewable resources (RRs) such as photovoltaic (PV) and wind turbine have been widely considered to reduce the dependency on conventional power generation systems along with enhancement of the quality and sustainability of the power system. Recently, DC microgrid has gained popularity in many real-world applications such as rural electrification due to its simplicity and low power losses. However, the power variability of renewable resources and continuous change in load demand imposes risks of power mismatch in standalone DC systems that increase the chances of stability and reliability issues. Therefore, complementary generation and/or storage systems are coupled with standalone DC microgrid to mitigate the power fluctuations and maintain a power balance in the system. This dissertation presents a power management strategy (PMS) based on model predictive control (MPC) for a standalone DC microgrid. A control scheme for a standalone DC microgrid system with RRs, storage, and load is desired to have the capability of effective power management that maximizes the extraction of energy from renewable generators, minimizes the transients in the system during disturbances, and protects the storage from over/under charging conditions. As a part of the proposed MPC, an optimization problem is formulated to meet the voltage performance in the system with respect to operating conditions and constraints. The proposed PMS uses the ARIMA prediction method to forecast the load and environmental parameters. The predicted parameters are utilized to estimate the future performance of the system by solving the dynamic model of the system, and a cost function is optimized to generate suitable control sequences. This research also presents detailed mathematical models of the considered systems. This dissertation presents an extensive simulation-based analysis of the proposed approach. With the proposed control, maximum utilization of the renewable generators has been achieved, and the DC bus voltage is regulated at nominal value with minimum transients under various load/environmental disturbances. Moreover, the research investigates the proposed MPC based on ARIMA prediction by comparing the performance of different types of prediction methods. The dissertation also measures the effectiveness of the proposed MPC by comparing its performance with a conventional PI controller.

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When multiple autonomous vehicles drive closelytogether behind each other, it is called a platoon. Platooningprovides several benefits, such as decreased congestion andreduced fuel consumption. In order for more vehicles to takeadvantage of these benefits, the platoon should be able to openup a space for other vehicles to merge into. Thus, our goal withthe project was to develop a system that can split a platoon.To achieve this, we are using model predictive control (MPC) tocontrol the system because it can handle constraints and controlsystems with multiple variables. To test the implemented system,we created a simulation environment in Python. We createdseveral plots to analyze and show the results of the simulations.To make the simulation more realistic, we introduced air drag tothe system. To counteract this effect, we added linearized air dragto the MPC. We showed that the constructed system could splitbetween any two adjacent vehicles in a platoon up to 50 meters.Another significant result was that the MPC could compensatefor the air drag without adding linearized air drag to the MPC. / När flera autonoma fordon kör nära varandra kallas det för en platoon. Det finns flera fördelar med platooning som minskad trafik samt minskad bränsleförbrukning. För att fler fordon ska kunna dra nytta av dessa fördelar bör nya fordon kunna sammansluta till en platoon och på grund av detta bör fordonen i platoonen kunna öppna ett utrymme för det nya fordonet. Därför är vårt mål med detta projekt att utveckla ett system som kan styra och dela på en platoon. För att åstadkomma detta använder vi model prediktiv reglering (MPC) eftersom den är bra på att hanterar bivilkor och styra system med många variabler. Vi implementerade systemet i Python, där en simuleringsmiljö skapades. För att se och analysera resultaten av simuleringen skapades grafer som visade hur fordonen hade färdats under simuleringen. Vi lade till luftmotstånd i simuleringen för att göra den mer realistisk. För att motverka luftmotståndet lade vi även till ett linjäriserat luftmotstånd till i MPC:n. I slutet av projektet kunde systemet dela platoonen mellan två fordon med ett avstånd upp till 50 meter. Vi observerade att MPC:n kunde kompensera motståndet utan implementationen av det linjäriserade luftmotståndet. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

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