Modern engineering systems, ranging from autonomous vehicles to energy storage devices, are required to operate reliably under uncertainty while satisfying increasingly complex performance and safety requirements. Ensuring that such systems behave as intended is the domain of verification, while the even more ambitious goal of designing controllers that guarantee correct behaviour by construction is known as controller synthesis. Achieving these objectives is especially difficult when systems are nonlinear or only partially known.
A central paradigm to address this challenge is the use of symbolic abstractions: simplified models that preserve the essential behaviours of the underlying system while improving analytical tractability. Abstractions enable the use of automated methods for verification and controller synthesis, making it possible to reason about safety, reachability, or performance in a mathematically rigorous way. In particular, symbolic control leverages finite-state abstractions to enable automated algorithmic synthesis of controllers that come with formal correctness guarantees. Yet, traditional abstraction techniques require complete system knowledge, limiting their applicability in practical scenarios where model knowledge is scarce, while data is abundant.
This thesis investigates how to overcome this limitation by learning abstractions directly from data and learning abstractions in combination with data when partial knowledge of the dyamics is available; further, we demonstrate how such abstractions can be used for verification and control under uncertainty....

Water scarcity is emerging as one of the most pressing global challenges, particularly in the context of agricultural irrigation, which consumes nearly 70 % of the world’s freshwater, 40 % of which is wasted due to inefficient systems. Water Irrigation Systems (WISs), composed of interconnected open-channel networks, are vital for delivering water to farmland but often suffer from suboptimal performance due to decentralised control and lack of inter-pool communication. While centralised control can enhance efficiency, it is rarely scalable. This thesis investigates overlapping control with neighbour-to-neighbour communication, supported by Networked Control Systems (NCS) and Event-Triggered Control (ETC) strategies, as an approach to optimise water and energy use. The research covers WIS modelling, controller architectures, system identification, and the integration of ETC, culminating in a proposed framework for smarter irrigation control.

Urban search and rescue (USaR) missions operate in volatile environments where time, information, and energy are scarce. This thesis presents a hierarchical decision-making and coordination architecture that elevates heterogeneous robotic autonomy from local actions to mission-level reasoning. Robots are modeled by capabilities of action and perception rather than fixed roles, allowing robot-specific suitability to be evaluated for any tasks that evolve during operation considering mission metrics. Sequential mission planning is formulated as a nonlinear model predictive control (MPC) problem with safe-return constraints over a finite horizon. Task suitability is first estimated by a fuzzy inference system that fuses robot capabilities with task-specific priorities. A multi-criteria decision-making (MCDM) framework assigns mission utilities to candidate actions by weighing urgency, information gain, and inter-task preferences, providing flexibility when task definitions are ambiguous and priorities shift.

The decision process is implemented in a two-tier hierarchial control scheme. Locally, each robot solves a tractable linearization of the MPC by filtering independent actions using utilities and observation overlaps, followed by a mixed-integer linear programming (MILP) scheduler subject to energy-aware safe-return constraints. Globally, a multi-robot extension reconciles local plans to produce a consistent allocation that balances distributed adaptability with centralized coordination.

The architecture is validated in realistic simulations with heterogeneous robots and evolving tasks. Relative to heuristic baselines, it accelerates victim identification and area mapping while achieving higher utility in most of 17 randomized single-robot scenarios. In multi-robot experiments, it allocates high-priority tasks to the most suitable platforms and completes missions faster than non-cooperative approaches, while using fewer computational resources than comparable optimization-based methods.

This thesis introduces a novel SLAM method based on Semidefinite Programming–Gradient Descent (SDP-GD) and compares it to a sonar-based SLAM algorithm and the ORB-SLAM2 visual SLAM algorithm for seabed reconstruction. All three algorithms were tested in Gazebo/ROS simulations using BlueROV2 and Desistek SAGA AUV models. A linear discrete time varying model is built and a PID controller is used for each AUV along straight trajectories while collecting measurement with among others a stereo-camera and sonar sensor. Across the various seabed profiles with increasing complexity, SDP-GD demonstrated reconstruction accuracy comparable to ORB-SLAM2. Furthermore, SDP-GD consistently achieved lower errors and exhibited greater robustness across different levels of seabed complexity when compared to the Sonar-Based SLAM algorithm. These findings suggest that SDP-GD matches the performance of with both sonar-based and visual SLAM methods in terms of performance, with the only downside being its higher computational cost.

As robots increasingly operate in human environments, their controllers must ensure safe and reliable behavior under real-time constraints. Although optimization-based motion planners can enforce hard safety constraints, their computational demands limit their use on complex robotic platforms. Geometric motion planning offers a real-time alternative through optimization-free, closed-form control laws with reach–avoid guarantees. However, these guarantees rely on assumptions about obstacle representations that are often violated in realistic settings. When such assumptions fail, the planner’s dynamical system may preserve invariance of the safe set but lose global attractivity, jeopardizing goal reachability.

This thesis introduces a runtime verification algorithm, called Scenario-Shield, that adapts the geometric planner’s underlying dynamical system to expand its finite-time region of attraction. The method periodically samples nearby robot configurations and performs forward simulations to approximate this region. To accelerate this process, the approach is extended by incorporating statistical uncertainty quantification: conformal prediction is used to calibrate a fast membership test for candidate states, and the scenario approach provides a principled approximation of an uncountably infinite subset of the region of attraction.

To maintain computational efficiency, the algorithm is implemented using parallel computing and integrated into a geometric motion planning toolbox with ROS. The proposed method is validated in simulation on both a holonomic ground robot and a mobile manipulator, demonstrating improved reliability over baseline geometric fabrics controllers.

Robots have been used to automate repeatable tasks in industries for decades. With the rise of computing power, robots can now be used in a wider range tasks involving finer motion control, even ones which involve sharing a workspace with humans. An example of this is using a dual arm robot for quality control, as was demonstrated by BMW with their RoboCT quality control system. However, such systems require guarantees of safety to demonstrate reliable operation.

This thesis will delve into the question of how to compute and execute a motion plan for a dual robot system which has a guarantee of synchronized and collision-free motion. This thesis proposes using a hierarchical control approach to ensure task execution while satisfying collision avoidance and alignment constraints. The proposed approach is verified through a case study of using two manipulators to pick and place a ball.

Rechargeable Lithium(Li)-Ion Batteries are a ubiquitous element of modern technology, as they pertain to efficient and sustainable energy storage for Electric Vehicles (EVs), as well as wind and solar farms. In the last decades, the production and design of such batteries and their adjacent embedded control, charging, and safety protocols, denoted by Battery Management Systems (BMS), has taken centre stage in the energy transition. A fundamental challenge to be addressed in battery technology, however, is the trade-off between the speed of the charging protocol employed by the BMS and the aging behaviour exhibited by the battery resulting in the loss of capacity in the battery cell, all while maintaining the safe operation of the battery.

This thesis aims to explore electrochemical models describing the charging and aging behaviour of Li-Ion Battery Systems, as well as the current existing charging protocols that aim to maximize charging speed while minimizing the aging effects that result in capacity loss. The proposed approach in this thesis is to adopt a data-driven approach to controller design, implementing improvements on an existent Reinforcement Learning (RL) pipeline to design an aging-aware battery-charging protocol, and extending the work into the field of Formal Methods for Systems and Control. This is done by expressing the closed-loop system resulting from a trained charging policy in the form of a data-driven abstraction capable of verifying the formal system specifications under probabilistic guarantees. Furthermore, a Counterexample-Guided Inductive Synthesis (CEGIS) scheme is proposed to additionally guide the training of the charging policy based on information from the learning results.

The rediscovered interest in space exploration has led to plans to establish outposts on the Moon and beyond. The lunar bases currently planned are to be manned incrementally, with robots performing most work. With new trends in robotics, the use of collaborating swarms has become more abundant. To support lunar operation, a distinct area within swarming, namely foraging, is proposed. Foraging systems have the primary objective of recovering resources. From literature, no foraging framework was found that included system maintenance in its mechanisms. This report aims to answer the question if this is possible while maintaining the benefits of foraging.

The research considers the fundamental act of recharging as its required maintenance task. To evaluate it dynamically, a rudimentary energetics model is included. For the framework of foraging, the work of [Adams] is used as a baseline. The newly proposed system implements an additional recharging region and role to perform recharging activities, both having major implications for role selection and agent operation. Furthermore, to enable navigation based on energy considerations, the experience communicated by mobile agents is amended to include the energy cost of a travelled path. In doing so, additional quality indicators of paths are available making path optimization a more dynamic process resulting in finer population behaviour. Finally, the decaying of beacons is updated and a fallback feature is introduced to maximize agent utilization.

The newly developed foraging system is evaluated using data collected through simulation in Webots. Simulation scenarios included obstacles with impenetrable boundaries and surfaces with increased rolling friction to emulate cost-expensive regions. Qualitative analysis identified all features of the foraging system as expected, both in the exploration and exploitation phase. Quantitative results proved that the system is able to function with the added requirements of recharging, perform path optimization with the additional path quality indicator, and can do so in various types of scenarios. With this, the research statement that foraging functionality is achievable with the practical considerations of robotics is confirmed to hold.

This thesis addresses the detection and prevention of adversarial attacks on self-triggered control (STC) systems and demonstrates how manipulating the sampling times can be used to provide a secure control framework. Secure control is vital to guarantee both the safety and security of critical infrastructure, which has been the target of malicious attacks over the past decade.

First, a novel watermarking scheme is proposed based on the early triggering of a well-designed STC policy, such that stability is guaranteed to be preserved. We show that this watermarking scheme, together with an event-triggered χ² detector we design, is able to detect replay attacks. An online heuristic for obtaining an optimal early triggering policy is provided. If certain assumptions hold we show that the early triggering policy is a discrete uniform one. Through an illustrative example, both a quantitative and qualitative comparison between two other watermarking schemes are provided. We conclude that none of the watermarking schemes can claim absolute superiority, and trade-offs between all considered schemes exist.

Next, we propose a new type of attack called a switched zero dynamic attack (ZDA), and provide an algorithm on how to construct these switched ZDA. We show that certain STC systems are susceptible to such attacks, and demonstrate that by tuning the triggering parameters there exist sufficient conditions such that these attacks are no longer disruptive. The effect of additive perturbations and a non-zero initial condition, as well as the proposed tuning method, are shown in a numerical example. We provide a qualitative comparison between several other countermeasures in the literature, which we extend for aperiodic sampling when needed. Finally, shortcomings and future directions are discussed.

Modern control systems require control strategies that can handle multiple levels of abstraction while providing formal guarantees of system behavior. Traditional hierarchical control approaches often lack formal interfaces between different layers of abstraction, making it challenging to ensure system-wide properties and extend implementations across different platforms.

This thesis presents a contract-based hierarchical control framework that provides formal guarantees across different layers of abstraction. Building upon recent developments in vertical contracts with heterogeneous refinements, we develop a three-layer control architecture for robot surveillance tasks, decomposing the problem into motion planning, trajectory generation, and tracking control. Each layer operates at its own time scale and uses appropriate model abstractions, with contracts ensuring system-wide guarantees while vertical system relationships enable proper interfacing between layers.

We validate our framework through both simulation studies and physical implementation on Elisa3 robots. The simulation results demonstrate the framework's ability to satisfy high-level temporal logic specifications while maintaining guarantees at each layer. The physical implementation reveals practical challenges in transitioning from simulation to hardware, leading to adaptations in the control architecture that maintain theoretical guarantees while accommodating hardware constraints.

With the introduction of autonomous vehicles on public roads, their performance in emergency situations has become a strong focus. Collision Imminent Control (CIC) concerns the planning and control of aggressive evasive maneuvers for collision avoidance of automated vehicles. CIC is implemented using adaptive Nonlinear Model Predictive Control (NMPC), which considers obstacles and road barriers for combined trajectory re-planning and control. To achieve real-time performance, the prediction model complexity is often reduced, which can lead to an under-utilization of the control potential. The aim of CIC is to use as much of the control potential of the vehicle as possible while remaining real-time viable.

In this research, CIC is implemented using objective-based collision avoidance based on the distance to obstacles and road boundaries. Different collision avoidance formulations were derived and compared on accuracy and real-time performance. The control potential of the vehicle was further exploited by a computationally efficient vehicle model that employs differential braking. The NMPC problem is solved using Sequential Quadratic Programming (SQP) with Real Time Iterations (RTI). Different techniques that reduce the computation time were compared. Sparse solvers and variable timesteps were found to be most significant.

The robustness of the controller was improved by friction estimation. The controller is furthermore demonstrated to work on highly curved roads and in scenarios with dynamic obstacles. The controller is implemented on the hardware of a real autonomous vehicle and simulated on a closed-loop embedded system. Combining all these elements results in a CIC controller that can apply more control potential and reach control frequencies upwards of 100 Hz, increasing the level of safety in vehicle collision avoidance.

In recent years, the deployment of ground-based mobile robots has gained more and more interest in various domains. In contrast to other types of mobile robots, legged robots can traverse irregular terrains, climb stairs, and step over obstacles. However, these unique properties intensify the energy demand and require highly advanced perception methods, actuator designs, and motion control algorithms. The most significant challenges in legged robotics lie in robustness, energy efficiency, and agility.

In recent years, the integration of an articulated torso or active spine, inspired by the body motion of high-performance mammals like the cheetah, has shown promising results. Various studies observed higher maximum velocities and lower energy consumption compared to a rigid torso. However, in these studies, the compliant systems were typically controlled using basic control strategies. In recent years, the development of highly dynamic model-based motion optimization strategies has greatly enhanced the overall performance of various legged robots. Therefore, a model-based motion optimization scheme is developed specifically for articulated quadruped robots. This scheme fully exploits the additional degrees of freedom of the torso to enhance the dynamic performance of the legged robot further.

Traditionally, Event-Triggered Control (ETC) methods are sample-and-hold control schemes that implement a triggering condition in order to reduce the number of control updates. Given a decay rate of the Lyapunov function, they focus on minimizing the (average) Inter-Sample Time (IST). In this thesis, we focused on the scheduling of Periodic Event-Triggered Control (PETC) controllers. By dynamically switching between triggering conditions, we
are maximizing the average rate of decay of the Common Lyapunov Function (CLF) given a minimum Average Inter Sample Time (AIST) or burst condition.

Given the physical system, we construct a switched system which captures all possible
scheduling behaviors. The l-complete abstraction of the switched system is constructed by solving a conjunction of quadratic equations. By setting a minimum AIST or burst condition, a set of states in the abstraction is marked and a safety game is played to construct the Maximal Permissive Controller (MPC).

On the safe behaviors inside the MPC, the guaranteed minimum control performance is maximized for the infinite horizon problem, i.e. by maximizing the minimum weighted time average of the primitive cycles in the MPC. First, several energy games are played to estimate
the maximum control performance. Thereafter, a mean-payoff game is played to generate the strategy securing this maximum control performance, which is used to construct the infinite horizon controller.

Reinforcement learning, especially deep reinforcement learning, has made many advances in the last decade. Similarly, great strides have been made in multi-agent reinforcement learning. Systems of cooperative autonomous robots are increasingly being used, for which multi-agent reinforcement learning can be used as a training method. However, the curse of dimensionality remains a problem for the computational speed of the learning algorithm and the bandwidth of communication channels. This research will focus mainly on reducing the problem of overloading communication channels by trying to reduce the number of communications. This is possible since it is usually unnecessary for every agent to communicate with every other agent constantly.

To do this, we use some ideas by Daniel Jarne Ornia. The first is to reduce communications in a multi-agent reinforcement learning system by treating it as an event-triggered control problem. This method uses so-called robustness surrogates as an equivalent to a Lyapunov function to determine if a communication can be skipped without decreasing the performance more than some tolerance. The second is a method to increase the observational robustness of a policy by using lexicographic reinforcement learning.

We aim to combine these ideas and trade the additional observational robustness for decreased communications. We also want to test whether additional observational robustness can help mitigate the sim-to-real gap. We implement this method for the multi-agent deep deterministic policy gradient algorithm and perform tests on a variant of the predator-prey domain in increasingly more realistic simulations.

We found that the combination of this robust policy and the robustness surrogates method does enable the agents to achieve the same return while communicating less. Unfortunately, our research shows that the observational robustness obtained using lexicographic reinforcement learning, does not help mitigate the sim-to-real gap.

Loop Robots develops and operates the next generation of fully autonomous disinfection robots in hospitals and healthcare settings. Accurate localization is essential in order to navigate reliably and effectively disinfect the tight hallways and corners of a patient room, operating room, or intensive care unit in a hospital. Odometry, or dead-reckoning, is the process of estimating the robots pose with respect to a known initial pose. It forms the backbone of the robots localization strategy, as well as being critical to many other processes that run on the robot, such as control and mapping.
The current strategy of generating odometry using the wheel encoders is prone to drift due to the integration of errors into the estimate. Moreover, the estimation takes place on the horizontal plane due to the nature of the wheel encoders. To improve the odometry and extend it to the full 3D space, a solution that makes use of all of the onboard sensors in a tightly-coupled manner is required.
In this Thesis, we make the first steps towards this larger goal. The contributions of this thesis include: (1) an Extended Kalman Filter (EKF) algorithm to fuse data from the Inertial Measurement Unit (IMU) and wheel encoders to estimate position and orientation of the robot in 6-DoF; (2) a line feature extraction and tracking methodology to extract primitives from LIDAR data and (3) A Moving Horizon Estimation (MHE) scheme based on a factor-graph formulation to perform pose estimation on the horizontal plane using LIDAR data and wheel encoders.
We test the three modules individually using a combination of simulations and real-world data wherever possible. We found that the MHE scheme was able to reduce drift over the long term, but is sensitive to the effects of outliers in feature matching, motion distortion of LIDAR scans, and wheel slip. The EKF scheme is able to reduce the overall drift and correct for wheel slips.
Based on these results, promising avenues for the improvement of all the proposed modules are given, along with recommendations on how to combine them all in a tightly-coupled fashion.

Water scarcity is a persistent global issue that requires effective solutions. One increasingly popular approach to address the distribution of freshwater in different regions is the utilization of Water Irrigation Systems (WIS). Due to their vital role in ensuring people's well-being, it is crucial to design a reliable infrastructure that can maintain water levels in channels as close as possible to predefined reference points.
Extensive research papers have been dedicated to discussing various aspects of WIS control, modeling, and identification. Although simulations can yield valuable insights, it is essential to validate the results in real-life systems due to certain factors that cannot be precisely estimated, such as leakages along the channels. To address this need, a scaled-down version of a WIS at TU Delft has been developed, which effectively emulates the behavior of a real water irrigation system. Through continuous refinement and design enhancements, the simulator has proven to be a valuable tool.
Working with the scaled-down WIS has presented significant challenges, but a dedicated controller has been developed, yielding satisfactory results. The approach to controlling the WIS is simple yet effective, employing a decentralized controller that is applied to both the nonlinear simulator and the real setup. Remarkably, this approach successfully meets the predefined reference points, even in the face of applied disturbances to the system.

Localization is one of the most fundamental competencies required by an autonomous robot, providing crucial information about its position for decision-making in indoor environments. In the current literature, an indoor localization system utilizes exteroceptive sensors such as GNSS(Global Navigation Satellite System), a camera, or an ultrasonic sensor and onboard sensors such as odometry or accelerometer to observe its environment. These sensor data are typically fused using Extended Kalman Filter (EKF) techniques. To solve the challenges posed by multiple sensors, different EKF variants such as Multi-Rate EKF and Single-Rate EKF have been proposed. Additionally, localization architectures like Cascade and OWA (Ordered Weighted Averaging) have been introduced to enhance the fusion process.

This thesis aims to develop an indoor localization system for Elisa-3 robots. The proposed localization system is evaluated through simulations and real-world experiments conducted using the Elisa-3 robots platform. Furthermore, the effectiveness of the localization system is assessed in a control task involving the formation of a consensus within a robot swarm.

The simulated and experimental results indicate that using a Single-Rate Extended Kalman Filter under an OWA architecture can generate an accurate and precise trajectory across a wide range of scenarios.

This thesis investigates the potential of state-dependent sampling strategies (SDSS) for the control of heavy-haul trains. Event-triggered control (ETC) is a control approach in which data is only sent when some state-dependent condition, the triggering condition, is satisfied. In this way, the number of communications required to stabilise a system can be drastically reduced. Periodic event-triggered control (PETC) is a variant of ETC in which the triggering condition is checked periodically. By sometimes sampling earlier than a PETC-generated deadline, long-term pay-offs in the average inter-sample times are possible. As searching for formal models of early-triggering controllers quickly becomes computationally infeasible as system dimensions grow, a sample-based approach using reinforcement learning was utilised to find the SDSS controller, which takes the form of a neural network mapping states to the time until the following sample, trained to hopefully yield long-term payoffs in sample efficiency. It was found that, by choosing suitable control parameters, the SDSS controller can outperform the PETC baseline in terms of inter-sample times (IST). Next, a hardware-in-the-loop (HIL) setup was made to evaluate the optimised controller’s performance in a real-time context controlling a non-linear train system subject to noise, disturbances, and tasked with attaining different speed setpoints while minimising the inter-wagon forces. It was found that the optimised controller did not perform well during these scenarios. Since the controller’s robustness was not considered during the training process, performance suffered under these conditions. To improve the robustness of the controller, the system must be trained on realistic (noisy) data. Simulations with more wagons must be performed to assess whether improvements can still be attained when using larger models. Finally, the accuracy of the HIL setup needs to be improved by using an appropriate network stack that would allow each wagon to send its own data and turn off the sensor node radios when otherwise idly listening for incoming data.

In multi-agent systems reaching consensus has been a long-standing problem. A considerable amount of research has been focused on how event-triggered consensus can be used to limit the energy consumption of the communication system while still ensuring convergence to the neighbourhood of a point or a formation. These algorithms rely on having knowledge of the global state information of each agent in the network. Given this information, it is possible to consider using distributed algorithms from the field of Computer Science to achieve the same.

This thesis presents a study on average consensus algorithms for single-integrator multi-agent systems. The focus is on comparing the time required to reach convergence and the energy required by the communication system. To perform this study, three methods are used to compare the algorithms. The first method involves deriving mathematical bounds on the convergence time and the number of transmissions. The second involves a simple simulation to verify the mathematical bounds. Finally, the algorithms are compared in a practical application where robots are subjected to noisy measurements and need to assemble in a formation.

The results of the comparisons show that distributed algorithms are capable of fast convergence and require less energy for communication. Therefore, it is worth considering the use of distributed algorithms when agents have enough memory to temporarily store the global state of all agents in the network. There might still be situations in which the ETC algorithm converges slightly faster but this benefit is overshadowed by the increase in energy consumption.

Event-Triggered Control (ETC) is a control method where the controller is only updated when necessary. The control inputs are kept fixed until a state-dependent event triggers their re-computation. The triggering condition is designed to guarantee the stability and desired performance of the control system. This prevents the excessive use of scarce communication and energy resources. To fully take advantage of these savings when dealing with a (physically) distributed system, it is vital to decentralise the triggering condition. A decentralised triggering condition can be checked locally at the nodes of the system using the locally available states, eliminating the need to send updated sensor measurements to a central location constantly. However, the decentralisation of the triggering condition needs to be optimised for the state of the system to fully take advantage of the possible resource savings. This thesis provides a framework to optimally decentralise quadratic triggering conditions for Periodic Event-Triggered Control (PETC) systems in the absence and presence of bounded disturbances. In addition, a method of constructing a region-based map of such optimisations is provided, making it feasible to implement the optimisation approach on larger systems without needing more powerful hardware.