The European Union (EU) has set the goal to have a carbon-neutral economy by 2050. To achieve this, a key sector to focus on is the transportation sector. It will be especially challenging though to decarbonise the larger vehicles from the transportation sector, the trucks, ships and airplanes.

The typical way to respond to this or any other design challenge is by making use of the top-down/sequential design process, where first the larger parts of the design are established, and later the finer details are developed. However such an approach cannot guarantee that the design at the end is optimal or anywhere close to it. This makes the sequential design approach ill-suited to tackle the challenge of decarbonising the EU transportation sector. Co-Design on the other hand, a simultaneous design approach, can guarantee optimality, and would be a much more appropriate approach if it wasn’t for it being limited to only consider around 10-20 components.

The work in this report first investigated where this limitation of 10-20 components comes from. It is described how the source of the problem does not originate from topology optimisation, where most research has gone into, but rather it originates from the vast number of isomorphic topologies that are created during the topology generation process. Adding more data to describe the components only increases the number of isomorphic topologies even further. Since the results of the topology generation process are fed to the topology optimisation process, the entire Co-Design process is impacted by these isomorphic topologies.

The work that follows therefore focused on topology generation for Co-Design by testing a candidate method that allows topologies to be generated without any of the isomorphic topologies while describing components in more detail than what has been done in literature. The approach taken here relied, unlike what has been done so far, on the use of adjacency matrices and the interconnected system model. With this, new insight into topology generation for Co-Design was developed. The use of adjacency matrices were also key to automate the formation of constraints, which allowed to generate topologies for any set of components, also a first in the literature. The program TopoGen was developed to make this possible. The results that were obtained showed how using requirement chains and ordering instances, at least in simple cases, the 10-20 components limitation is fully overcome. This confirmed that the presence of isomorphic topologies the cause was for 10-20 components limitation in Co-Design. Still, for more complex cases, mistakes were present. However it was also shown how these too may be prevented in the future. This means that with further research and development, the candidate method presented here can be adjusted so that Co-Design can be applied even to complex cases, and therefore be used to develop solutions needed to decarbonise the EU transportation sector.

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.

This thesis addresses the Learning-Based Control (LBC) of unknown partially observable systems in the Linear Quadratic (LQ) paradigm. In this setting of learning-based LQ control, the control action influences not only the control performance but also the rate at which the system is being learnt, causing a conflict between learning and control (exploration and exploitation), which is particularly challenging to address. This thesis aims to develop a novel LBC algorithm for unknown partially observable systems in the LQG setting that is computationally efficient and can guarantee an optimal exploration-exploitation trade-off, quantified by a metric called regret. The regret quantifies the cumulative performance gap between the LBC policy and the ideal controller having full knowledge of the true system dynamics. The contributions in this thesis involve a novel LBC algorithm deployed in a two-phase structure. The first phase involves injecting Gaussian input signals to obtain an initial system model. The subsequent second phase deploys the proposed LBC strategy in an episodic setting, where the model is updated for each episode, and the resulting updated LQG controller is applied with additive Gaussian signals for exploration. In addition, the thesis establishes strong theoretical guarantees on optimal regret growth.