This thesis introduces two improvements in trajectory planning on uneven terrain with dynamic obstacles: the Dynamic Traversability-based Hybrid A* (DT Hybrid A*) algorithm and the Safety Direction Velocity Obstacle (DSVO) replanning method. The DT Hybrid A* differs from traditional path-planning approaches by adopting a less conservative strategy, incorporating vehicle momentum into terrain traversability in order to more accurately reflect the vehicle’s capability to navigate uneven terrain. Based on dynamic traversability, the DT Hybrid A* is developed by leveraging a kinematic bicycle model for steering, allowing variable travel lengths between adjacent nodes, and incorporating both distance and time heuristics. These enhancements significantly reduce CPU time costs during the planning process, resulting in shorter path lengths and faster planning times. This thesis incorporates a collision detection mechanism within the global path planning framework, enabling the system to replan if a potential collision is predicted in the future. The replanning process initializes the global path to avoid all static obstacles. With the movement of dynamic obstacles, if a potential conflict is recognized, the vehicle discards the current global path and replans from its current location. To ensure that the replanned path is free of dynamic obstacles, the collision detection mechanism incorporates Safety and Direction Elements based on a Velocity Obstacle (VO) cost term into the global path algorithm (DSVO). The DSVO algorithm enhances collision avoidance by integrating Safety and Direction Elements based on a VO cost term, surpassing traditional methods that use Euclidean distance penalties. DSVO avoids collisions by considering obstacle velocity and direction over time. It enhances the rate of successful replans for dynamic obstacle avoidance, producing the shortest collision-free paths with minimal computational overhead and completion time. The effectiveness of these approaches highlights the balance between computational efficiency and dynamic obstacle management, paving the way for more reliable path replanning solutions. DSVO surpasses methods that use Euclidean distance as a penalty for dynamic obstacles because it considers the obstacle’s velocity and direction over time. It also outperforms the Safety Element based on a VO cost term, which only ensures collision avoidance within a short time frame and provides the optimal velocity for that period but does not account for longer-term safety, potentially leading to collisions over extended durations. Additionally, since these are cost terms within global path planning algorithms, there can be conflicts between obstacle avoidance and achieving the goal. The DSVO algorithm, which incorporates Safety and Direction Elements based on a VO cost term, mitigates conflicts between obstacle avoidance and goal achievement. Additionally, DSVO effectively adjusts the path to navigate around dynamic obstacles while maintaining a direct route toward the goal, balancing safety and efficiency.

Automated vehicles represent an exciting advancement in transportation, offering a range of benefits that have the potential to revolutionize how we travel. They can improve safety, efficiency, accessibility, and sustainability, holding promise for transforming our cities and communities. However, generating safe, comfortable, and efficient motion plans, especially in interactive scenarios like forced lane merging, remains a significant challenge in the field.

Lane merging is a pivotal skill for automated vehicles, as it frequently involves changing lanes to reach a destination. For instance, when approaching an intersection, a vehicle might need to merge into a specific lane beforehand to execute a turn later. Traditional pipelines in automated driving typically decouple prediction and planning. They assume perfect upstream prediction and generate robust motion plans to avoid collisions with multi-modal predictions. However, in dense traffic conditions, conservative planning might hinder the ego vehicle from merging effectively, resulting in it becoming stuck. This underscores the necessity of combining prediction and planning, a concept we term interaction-aware planning algorithms.

The first major contribution of this thesis is an efficient game-theoretic behavior planner that captures interactions under different behavior modes. In this approach, we represent the behaviors of the vehicles as actions in a matrix game and select the Nash equilibrium to capture their mutual influence. To generate the cost of the action pairs, we model the merging process as a gap selection process and evaluate the trajectories generated by interactive models. The effectiveness of the proposed planner is validated in the high-fidelity CARLA simulator.

In the real world, human drivers may not always adhere rationally to the equilibrium of a game model. They could choose a behavior mode different from the game theory solution. Therefore, it might be more beneficial to consider different motion modes simultaneously, rather than favoring the "most likely" one while neglecting the others.

For this purpose, we also explore the usage of Branch Model Predictive Control (B-MPC) in this thesis. By predicting the motion of the surrounding vehicle as a scenario tree, the B-MPC approach can generate a trajectory tree as a motion plan. By executing only the root node, the ego vehicle can consider different future scenarios simultaneously and plan contingency motions. We further extend the B-MPC approach by incorporating interactive policies, using different solving schemes, and including collision avoidance constraints that consider the orientations of the vehicles. The effectiveness of these proposed methods was validated through experiments conducted in a handcrafted lightweight simulator.

Overall, this thesis focuses on developing interaction-aware planning methods to facilitate safe, successful, and comfortable lane merging scenarios. Nevertheless, the major limitations lie in the modeling error and the potential long-tail issues. To address these challenges and further improve motion planning, future work could explore data-driven (learning-based) approaches that leverage real-world driver behavior to generate more informed and adaptable motion plans.

Ambitions to limit climate change are incentivizing the expansion of renewable energy. In particular, offshore wind energy is expected to grow rapidly. To harness the full potential of existing as well as prospected offshore wind farms, the limited capacity of the internal cable network of the offshore wind farm, called the collector system, should be efficiently used.

The operation of the collector system during cable outages presents significant potential in this regard. Currently, during these outages, a conservative approach is taken that under-utilizes the capacity of the collector system and consequently limits power production excessively. The available headroom of the system can be unlocked by optimizing the power routing and turbine setpoints. This optimization problem is the topic of the MSc Thesis, carried out
within Vattenfall.

Two novel optimization-based rerouting and setpoint decision frameworks are developed for collector systems with arbitrary topologies: an open-loop control strategy and a receding horizon control strategy.

The open-loop control strategy assumes that the network can only be reconfigured at the beginning of the outage. It is formulated as a mixed-integer linear programming problem, in which the cables are modeled as binary control variables and the setpoints as continuous control variables.

The receding horizon control strategy is deployed in real time, leveraging cable temperature measurements and power forecasts to derive optimal control actions dynamically. Dynamic thermal rating is applied, which entails that the power flows are constrained based on the cables’ temperatures rather than on a static rating. The resulting control strategy is formulated as a mixed-integer quadratically constrained programming problem.

A case study is performed to compare the performance of the developed strategies to existing strategies. Simulations concerning seven occurred cable outages at an offshore wind farm show an average increase in power production with respect to the industry control strategy of 0.82% for the open-loop control strategy and 4.2% for the receding horizon control strategy.

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.

Climate change is one of the biggest challenges of this time. One of the necessary changes to combat this challenge is a shift from conventional power resources, such as coal and natural gas, to large scale deployment of renewable resources, such as wind and solar power. However, renewable energy resources are volatile because they depend on the weather, which poses a problem because supply and demand should always be exactly matched. Along with a shift in energy production, also appliances that use non renewable energy are becoming electric, e.g., electric heating and cooling in buildings and electric vehicles. This causes an increase in electricity demand and is a burden on the main grid. To reduce the demand on the grid, it is important that a part of the energy is produced locally by distributed renewable resources, such as residential solar panels. To maintain a stable operation of the grid, the smart grid paradigm has to be adopted. Advancements in information and communication technologies provide the means for an efficient management of the grid using various services, such as distributed generation and storage. Because of the conflicting nature of the agents in an electricity network, game theory is a widely used framework to tackle this problem. In a realistic model of a power grid coupling constraints must be taken into account, because the agents have shared resources. Therefore, the economic dispatch problem is modeled as a noncooperative game with coupling constraints, called a generalized game. Three distributed algorithms are derived to reach a generalized Nash equilibrium. It is shown through simulations that these algorithms have the potential to ensure a safe and efficient operation of the grid, while minimizing the cost of power usage. To reduce the cost even further, a method is presented to make more optimal use of the storage units.

In this thesis, we study Online Convex Optimization algorithms that exploit predictive and/or dynamical information about a problem instance. These features are inspired by recent developments in the Online Mirror Decent literature. When the Player's performance is compared with the best fixed decision in hindsight, we show that it is possible to achieve constant regret bounds under perfect gradient predictions and optimal minimax bounds in the worst-case, generalizing previous results from the literature. For dynamic environments, we propose a new algorithm, and show that it achieves dynamic regret bounds that exploit both gradient predictions and knowledge about the dynamics of the action sequence that the Player's performance is being compared with. We present results for both convex and strongly convex costs. Finally, we provide numerical experiments that corroborate our theoretical results.

This report presents the results of the graduation thesis from the TU Delft, performed at TNO unit Defence, Safety and Security, expertise group Weapon Systems. The goal of this graduation project is to develop a controller for a projectile equipped with the Precision Guidance Kit (PGK). The PGK is a Course Correction Fuze which can be used to retrofit 'dumb' artillery projectiles. It has canards to provide course correction and contains a GPS sensor to determine its position and velocity.

The dynamics of the projectile are described by a nonlinear 6 Degrees of Freedom (DoF) non-rolling body frame model. A static model is made of the PGK where its aerodynamics are calculated using computational fluid dynamics.

A Jacobian based guidance algorithm is developed for the 6 DoF model which calculates the changes in velocity needed along the perturbed trajectory to set the projectile back on the ideal path towards the target. These guidance signals function as a reference to be tracked by the controller.

A switching mode controller is designed, tuned and tested empirically in simulation. A nonlinear controller is designed to set the PGK actuator at an angle based on the guidance signals. Three switching laws are designed, the first to set the starting time of the controller, the second to switch the actuator off when the projectile is on a new ideal trajectory, and a third to avoid chattering, excessive switching, between the control modes.

The combination of the PGK actuator, the Jacobian guidance and the switching controller is shown to provide course correction successfully for four different trajectories with random offsets in initial velocity and pitch and yaw angle. The average miss distance and Circular Error Probable are decreased by 90%, to less than 50 m, without destabilizing the projectile.

Traditional centralized power plants have limited ability to adapt to the varying power demands caused due to the increasing deployment of renewable energy sources. For power grids, willing to increase the use of renewable energy and thereby decrease the energy bills, demand side energy management could act as an effective solution. Demand side energy management of the power grid refers to the process of regulating the power demands of the devices it serves. A large fraction of this power demand on the grid lines is caused due to Thermostatically Controlled Loads such as residential refrigerators, electric water heaters, air conditioners, industrial heaters, ovens, etc. Traditionally, the energy management of these devices is achieved using model predictive control and linear quadratic regulation. To better handle the system heterogeneity and computation costs, model-free adaptive control algorithms are explored.

ZF Friedrichshafen AG is developing its own active suspension system designed as a single unit per corner to optimize the dynamic behavior of a vehicle. The innovative active damper design integrates all the essential components into one damper unit, with the aim to facilitate implementation. This master thesis in cooperation with ZF Friedrichshafen AG presents the development of a disturbance compensation controller for the active suspension unit. The goal is to suppress forces induced by road irregularities on the passenger cabin. The emphasis throughout this work lies on the implementation and applicability of the control algorithm.

Kinodynamic motion planning for a robot involves generating a trajectory from a given robot state to goal state while satisfying kinematic and dynamic constraints. Rapidly-exploring Random Trees (RRT) is a sampling-based algorithm that has been widely adopted for this. However, RRT is not fast enough to enable its use in industrial applications. Recently, supervised learning has been used to pre-learn time consuming steps of RRT which resulted in improvement in planning times. The supervised learning models require cost and control input of the system as training data which are generated using optimal control.

The training data can be obtained either by indirect optimal control or direct optimal control techniques. In this thesis, both the techniques are each used to generate cost and control inputs for a two-link manipulator using random initial-final state pairs. Then each dataset is used to train a model and the datasets are compared based on certain training metrics. K-nearest neighbours regression and multi-layer perceptron neural network are the supervised learning models used in this thesis. It is observed that both the datasets result in similar convergence of the models, but indirect optimal control approach allows upto 24-fold faster data generation and upto 3-fold reduction in dimensionality of training data compared to the direct optimal approach.

Real-world robots have torque limits based on actuator configuration. The torque limits are modeled as control constraints in both the optimal control techniques and the effect of
this restriction on data generation and supervised learning is studied in this thesis. Direct
optimal control is found to be better for data generation in this case due to the ease of
applying control bounds as inequality constraints on the function approximations. Indirect
optimal control is very tedious as active constraints should be known a priori to determine
the switching points. An alternate method is explored instead where samples are generated similar to the unconstrained case but samples violating the constraints are removed. Poor control input learning is observed in both approaches and the models struggled to extrapolate. It is hypothesised that this is due to inability of the constrained data to fully capture the system dynamics. However, good cost prediction is achieved using neural networks.