Vibrations and disturbances are becoming more of a concern as lightweight, flexible structures in high-tech systems are pushed towards faster speeds and higher precision. Active Vibration Control (AVC) methods have been effectively used to attenuate vibrations and increase the bandwidth of these systems. With the miniaturisation of electronics, an increasing amount of sensor and actuator pairs can be used for AVC applications. Not only does this allow for higher active damping, it also grants more flexibility in terms of control. This trend has led to the study of robotic metamaterials and meta-structures: large-scale engineered materials build out of a repeating pattern of unit cells, where each unit cell contains a sensor, actuator and sometimes even a computing unit. The optimal control architecture to use for these systems is a difficult dilemma, since decentralised and centralised control schemes both have fundamental trade-offs in terms of performance and scalability. In this paper we study distributed control, a promising middle-ground solution that is hardly used in AVC applications. We show with the use of LQR that a distributed control architecture can achieve optimal performance in the low-frequency range for robotic materials with relative measurements. Additionally, the actuators use lower maximum control forces and a distributed control architecture remains scalable for implementation in large-scale systems. In this paper the robotic cantilever beam is studied as a specific example as it represent many typical high-tech applications. Furthermore, implications on periodic robotic meta-structures are made using LQR in the Spatial Fourier Domain.

Advancements in knowledge and technology have led to an evolution of greenhouse operations: from simple transparent shelters to (one of) the most profitable sectors in agricultural industry. The understanding of the physiological and biological processes of the inside micro-climate and crop, as well as the introduction of climate control, were critical for improving both the quality and production rate. Advanced control strategies like MPC involve the application of horticultural and plant physiological knowledge using mathematical models that describe the dynamical behavior of the greenhouse and crop. An on-line control method balances the benefits of crop yield and costs of the climate control equipment. The increasing production and cultivation rate in response to the growing demand is at a point where the focus and tension is shifted to the energy input and environmental footprint of greenhouse operations. This stimulates the agricultural industry to provide sustainable solutions. An innovative greenhouse is developed by BBBLS Solutions, that uses a soap bubble cavity which improves the thermal properties and energy efficiency. The concept is in development phase and is acknowledged that there is potential for improved climate control and automation of the control of the soap bubble cavity. This research introduces a MPC strategy for controlling the greenhouse climate and the soap bubble cavity. The greenhouse processes are described by a mechanistic model, which simulates the inside climate variables as a function of the greenhouse structure, outside environmental conditions and climate control equipment. The dynamics of the soap bubble cavity and the corresponding thermal varying properties were then augmented in the climate model. The augmented model is used as a predictor for the future dynamics of the greenhouse climate, for which the optimizer generates a control input sequence for the climate control equipment and soap bubble generators inside the cavity. The MPC implementation results demonstrate that the greenhouse temperature can be controlled within the pre-defined bounds and that the filling of the soap bubble cavity is with respect to outside conditions and priorities. Furthermore, the MPC strategy supports real-time control as the CPU time can be less than the time between control instances. The proposed methodology is an initial attempt but proves to be a promising concept for further development.

Ongoing research in autonomous driving currently focuses on creating new applications for autonomous vehicles (AV) and connected autonomous vehicles (CAV). Specifically, motion planning and control solutions are being developed based on the combination of Artificial Potential Functions (APF) with economic Model Predictive Control (eMPC). These two methods are integrated into a new Comprehensive Predictive Control (CPC) strategy. Although preliminary research shows promising results, a performance analysis of this approach, both for AV and CAV, has not yet been published. Therefore this thesis studies the capabilities of this novel APF-eMPC framework by carrying out numerical simulations. Multiple manoeuvres and varying amounts of white noise are utilized to test the controller's limitations. For the AV part, multiple basic driving manoeuvres are simulated: lane-keeping, car-following and lane-changing. The results show that an AV based on this framework can execute these different manoeuvres without precise measurements. The CAV concept is simulated using a platoon scenario. The gap-closing behaviour of the multiple CAVs in a platoon is examined. The state-of-the-art gap-closing APF is compared with an APF based on inter-molecular dynamics and fitted on actual traffic. Various experiments are carried out using a constant time-headway in combination with different time gaps between the vehicles. The results show that the resulting behaviour by the inter-molecular APF better matches human driving behaviour and results in less dangerous gap-closing behaviour than the quadratic platoon APF. The latter has a more considerable change of lateral instability occurring. Therefore the APF based on inter-molecular dynamics and fitted on actual traffic data outperforms the APF based on a quadratic function. Lastly, it was found that the coupling between the longitudinal and lateral dynamics, often neglected in literature, cannot be ignored during platoon stability analysis.

Research has shown that GLOSA systems can help reduce the emission of $CO_2$ by motor vehicles and improve flow on urban road networks. However, existing GLOSA systems only work in combination with a limited selection of Traffic Signal Controllers (TSCs) and therefore have not been widely implemented. This research describes and evaluates the design of a system to model the behaviour of the most common types of TSCs using data that is shared by road users, also known as Floating Car Data (FCD). Almost every road user is able to share his location using a smartphone, so this means that a system of this kind can be implemented everywhere in the world where people are willing to participate, without requiring any changes to the infrastructure. It is chosen to model TSC behaviour using Hidden Markov Models (HMMs), because they can be generated from unlabelled data, as opposed to other machine learning techniques. More specifically, Explicit Duration Hidden semi-Markov Models (EDHSMMs) are used, so that timing behaviour can be captured more accurately than with standard HMMS. An adaptation to an existing learning algorithm is made to decrease amount of data required for learning and to make sure the algorithm can cope with the sparse nature of FCD. Additionally, a system is developed to combine generally known intersection data and FCD to generate models that have transition parameters with low entropy and thus high predictive powers. In this way, the system produces models that are accurate enough to be used for the generation of GLOSA from relatively sparse data. In a simulated setting, it is shown that the system can identify the true switching behaviour of the most common types of TSCs and that it produces models whose duration probability distributions converge to the true situation when provided with realistic amounts of data.

Optimizing delivery routes is a well-researched topic, however, most of the classical approaches do not incorporate preferences of drivers, as those approaches focus on minimizing the time or distance of the routes. As a result, the actual driven route of an experienced driver often deviates from the proposed route since the drivers have tacit knowledge about the real-life conditions of the road network. Amazon proposed a challenge to learn a delivery route planning strategy from historically driven routes and thus incorporate this tacit knowledge. In this thesis, we will tackle the challenge using data-driven inverse optimization to learn the zone sequencing patterns of drivers. The zone sequences of expert drivers are assumed to be the solutions to a traveling salesman problem (TSP) in which the weights represent the preference of a driver to use a certain edge. The values of the weights will be learned through inverse optimization. Our final approach achieves a score that ranks 4th out of the 48 models that qualified for the final round of the challenge.

The global market for personal mobility has transformed over the last decade. Traditional taxi services have to compete with the emergence of ride-hailing services such as Uber and Lyft. Rapid developments in available algorithms and real-time inter-connectivity of travellers and vehicles offer mobility-on-demand (MOD) services new possibilities to maximise the efficiency of the ride-hailing process. It is well established that rebalancing can enable the true potential of a ride-hailing fleet. In this context, rebalancing is defined as the redistribution of idle vehicles over the service area of a ride-hailing operator. In the search for the optimal rebalancing strategy, model predictive control (MPC) and approximate dynamic programming (ADP) methodologies are active fields of research. However, the computational burden of MPC limits the length of the predictive horizon in large MOD applications. This thesis proposes a novel algorithm that combines ADP and MPC. More specifically, we integrated a value function into the MPC framework as a terminal cost. We shorten the horizon of MPC and propose to use the value function as a long-term planner. For the terminal cost, we continue research into piece-wise linear value function approximation. We implement a novel multi-period approximation of the value function, where we use the maximum repositioning length as the horizon. In our case study, the value-based repositioning strategy offers comparable service quality to conventional MPC at 5.2% of the computational burden. The hybrid ADP-MPC algorithm offers flexible horizon partitions between the multi-period value function and MPC. It addresses the shortcomings of both ADP and MPC algorithms in repositioning problems. At peak performance, it offers an increase of 4.5% in service rate and a decrease of 5.2% in the average waiting time over conventional MPC, at roughly half the computational burden. Keywords: Ride-hailing, Mobility on demand, Model predictive control, Approximate Dynamic programming

Digital Twins are a key component of Industry 4.0. They are a digital representation of a real-world entity containing both the structure and the dynamics of its real-world counterpart. The use of Digital Twins appears to offer a powerful an compelling application for production processes. A Self-configurable and self-adjustable Digital Twin is developed for the Greenfield production process of FOCUS-ON. FOCUS-ON expects a fast growth in order demand and needs to adjust their production line accordingly. A Digital Twin is developed according to the 5C architecture. The Digital Twin proposes adjustments to the production line to keep up with the increasing order demand.

The ongoing increase in urbanization and traffic congestion creates an urgent need to operate our transportation systems with maximum efficiency. Traffic signal control optimization is considered one of the main ways to solve traffic problems in urban networks. In publications in the field of intelligent transportation systems, a vast amount of different optimal traffic light control methods is described. With new optimization methods being developed, it is important to know their performance compared to similar methods. Such a comparison is only possible if the same performance evaluation methodology is applied to all these methods. Most of the studies in the field of intelligent transportation system consider a self-defined evaluation methodology. A general evaluation methodology is developed to objectively evaluate the performance of these optimization methods. The developed general evaluation methodology is used to evaluate the performance of a dynamic programming and Q-learning method.

Greenhouses allow production of crops that would otherwise be impossible. Permitting more local, fresher and nutrient richer crop production. Eorts are taken to minimize societal harm due to energy and resource consumption by greenhouse production systems. One way to control such systems is by using model predictive control. Optimal crop yield and resource eciency can, in theory, be achieved by model predictive control. Unfortunately, one major drawback of model predictive control is that it is not well equipped to deal with parametric uncertainty. Significant prediction errors can occur when a mismatch between the model and the real system exists, resulting in deteriorated performance of the system. Strategies exist, such as robust MPC, that are designed to handle uncertainty, but those often result in conservative control policies. This thesis proposes to use model predictive control as a function approximator for RL in order to learn values for model and MPC parameters that can deliver optimal performance in the case of model mismatch. In this thesis, data-driven economic nonlinear model predictive control using Q-learning is proposed as a method to alter the model parameters. The performance of the system af- ter learning is compared to approaches using robust and nominal model predictive control. Three dierent goals are determined: maximizing economic profit, minimizing the constraint violations and maximizing the economic performance while minimizing constraint violations. In this work, an ENMPC scheme is used as a function approximator in a Q-learning envi- ronment. The optimization solution from the ENMPC scheme is used as the input to the system, while the Q-learning agent optimizes the parameter values of the ENMPC scheme and model for the environment. The performance of the system after learning is compared to approaches using robust and nominal model predictive control. The simulation results show that the data-driven ENMPC using reinforcement learning is able to decrease constraint vi- olations by up to 94%, but unable to increase economic performance compared to nominal MPC, compared to robust MPC the EPI is increased by almost 10% while keeping constraint violations at a similar level.

Cycling is an increasingly attractive transportation mode, thanks to its health and environmental benefits. Personalized travel assistance services can help make cycling more appealing by providing speed or route advices that can reduce travel time and increase safety while taking into account the personal preferences of cyclists. Due to its ability to learn agents' reward function, Inverse Reinforcement Learning is a suitable algorithm for learning cycling preferences from data. This thesis aims to describe cycling styles as a set of cycling preferences encoded as a reward function composed of a weighted sum of features. The weights associated to the features composing the reward function represent the importance given to each cycling preference and express the trade-off between different goals of a cyclist. Continuous-time Inverse Reinforcement Learning extracts the weights from empirical cyclists' trajectories collected during an experiment performed in Delft. During the experiment, cyclists were asked to cycle according to three different cycling styles: cautious, normal and aggressive. Differences between weight sets extracted for each cycling styles were analyzed by means of the Kruskar-Wallis statistical test and K-Means clustering algorithm, and the averaged weights for each cycling style were used to simulate a set of test trajectories. It is shown by simulations that the reward function identified for a specific cycling style leads to an improvement in terms of similarity to test trajectories with the same cycling style with respect to the reward functions corresponding to other cycling styles. The statistical analysis shows that the weights of cautious and aggressive cycling styles show statistical differences and define separate clusters.

The need for smart traffic control has grown over the last years. Initiated by an increased amount of traffic. Network-wide traffic control is becoming a more interesting field for traffic control. Mainly because computer power has increased and optimisation techniques improved. Network-wide traffic aims to improve the overall traffic state by looking at the entire problem instead of sub-problems. Besides improving traffic conditions, network-wide traffic control could support road operators in simplifying their work by taking over some tasks and keeping track of the situation. For this research, we compare a pragmatic, user-friendly and transparent control method versus a Model Predictive Control (MPC) approach. For the MPC controller, the second-order macroscopic METANET model is chosen. The METANET model describes a network as a directed graph. We test both controllers on two small scale freeway traffic networks. The control measures that are implemented are ramp-metering and rerouting. The simulations are done in a SUMO environment. The key performance index (KPI) used for comparison is Total Time Spend (TTS). The resulting optimisation problem is a Mixed Non-linear Integer Problem (MINLP). This problem is solved with a heuristic method by a Genetic Algorithm (GA). Simulations for the two networks in a demand scenario around critical density are analysed over 20 iterations. The results prove the potential of both algorithms since both improved the TTS significantly. The NM excels in ease of implementation and ease of understanding for non-experts. While the MPC outperforms the NM in TTS reduction, it is harder to configure and understand for non-experts. The MPC is successfully tested on its capability to prevent undesired behaviour from happening by adding penalties to the objective function. For future research, larger networks need to be investigated, with a focus on simplifying the resulting optimisation problem. It is expected that a piecewise affine approximation is a promising method.

Speed trajectories considerably influence vehicular fuel consumption, particularly on signalized roads. To minimize fuel consumption, sharp acceleration/deceleration maneuvers and idling events at signalized intersections should be prevented. By taking advantage of the technological developments in infrastructure-to-vehicle communication, the possibility of receiving traffic signal phase and timing information in advance is enabled. Although a vast amount of research has been dedicated to optimal speed trajectory planning, existing methods may not be adequate in identifying the optimal solution for vehicles driving on signalized roads. Most studies do not involve queue estimation in the algorithm, which makes it challenging to deploy these methods in practice. Moreover, research efforts focus on undersaturated traffic conditions where queues can completely dissolve in a single cycle. Once the network is oversaturated, residual queues are formed generating traffic fluctuations and complete stops, significantly reducing the effectiveness of the application. In this thesis, an optimal control problem is formulated to obtain the optimal speed trajectory, where traffic induced constraints are taken into account and queue estimation is explicitly integrated into the control framework. Based on kinematic wave theory, an efficient and accurate procedure to formulate the queue constraints in various traffic conditions is developed. To facilitate real-time control actions, the constrained optimization problem is solved using model predictive control. The simulation case studies show the proposed algorithm achieves vehicular fuel consumption savings as high as 29.15% compared to an existing approach in the literature. However, the fuel consumption savings are at the expense of an increase in travel time up to 1.65% compared to the literature approach. The results also indicate the benefits grow with increasing market penetration rates (MPRs) of controlled vehicles until it levels off at about 80% MPR. Furthermore, the results demonstrate the proposed algorithm can deal with stochasticity in traffic behavior. Finally, the thesis highlights the need for future research to further improve the proposed algorithm.

In decision making problems, the ability to compute the optimal solution can pose a serious challenge. Dynamic Programming (DP) aims to provide a framework to deal with a category of such problems, namely ones that involve sequential decision making. By dividing the original control problem into sub-problems and solving it backwards in time, from the end of the time horizon to the start, the method can compute a map of optimal solutions with respect to the initial condition. In order to divide the original problem into subproblems the DP method takes advantage of the principle of optimality, which states that a sub-solution of the optimal solution should be the optimal solution for the equivalent subproblem. In control systems, where the state and decision spaces are continuous, the original DP framework can be intractable due to the size of the discretization needed to simulate the continuous space. Therefore, efficient approximations are needed to solve such problems. One promising method is called Conjugate Dynamic Programming (CDP). The CDP algorithm is able to transform the original framework and solve problems in the conjugate domain providing a computational advantage over the standard method. In this work, we aim to improve and extend the setting under which the CDP algorithm operates, thus providing a more concrete advantage over standard method . In that regard, we will extract the optimal control actions from within the CDP algorithm, eliminating the need for solving an extra optimization problem for their computation. In addition, we will introduce a different interpolation technique that can outperform the current one, in certain scenarios, thus granting the user more choice when solving a decision making problem.