Doctoral DissertationsDoctoral Dissertations (FEAS)https://hdl.handle.net/10155/4012024-03-29T08:30:14Z2024-03-29T08:30:14ZCategories in control systems software: toward a unified theory of programming & controlTeatro, Timothy A.V.https://hdl.handle.net/10155/17542024-02-27T20:07:09Z2023-12-01T00:00:00ZCategories in control systems software: toward a unified theory of programming & control
Teatro, Timothy A.V.
Category theory is applied to the design and modeling of control systems application software with emphasis on feedback control. The language of application is iso standard C++17, though the design is abstract and can be gainfully applied in any language expressive enough to embed domain specific languages for event stream processing with sufficient structure. The design is derived in a category, Cpp, of a subset of C++ programs where types are modelled as sets and programs/routines are modelled as functions. This gives a forgetful functor from Cpp to 𝕊𝗲𝘁, the category of sets which, in theory, facilitates broader compatibility with theories of dynamical systems in concrete categories.
A library of abstract datatypes (struct templates) and natural transformations (parametrically polymorphic function templates) is developed to demonstrate that (1) Cpp carries a bicartesian closed structure and (2) this structure has representation as standard compliant code. The axioms of this structure are encoded as unit-tests. And from this structure we specialize “machines” in the sense of Goguen (or more generally, Arbib & Manes), which actualise in Cpp as Moore machines. These Moore machines are then used as a basic model for the I/S/O structure of a control program.
Categorical Moore machines can be cast in terms of algebra and coalgebra which give natural mechanism to the input-driven evolution of internal state of the control programs, and infinite records of behaviour. The internal language of that model is consonant with sufficiently structured domain specific event-stream processing languages. The core examples and a case study use Rx, but FRP is a stated ideal and avenue for future work for modeling of interconnected and hybrid systems with computer controlled components.
The architecture is applied in two examples: (1) a simulated spring-mass- damper system with PID-force control, where comparison is made to analytical results, and (2) NMPC path tracking of a mobile robot with obstacle avoidance through soft constraint.
2023-12-01T00:00:00ZDevelopment and analysis of thermal management strategies to improve Lithium-ion battery performanceShahid, Sehamhttps://hdl.handle.net/10155/17492024-02-27T17:30:27Z2024-01-01T00:00:00ZDevelopment and analysis of thermal management strategies to improve Lithium-ion battery performance
Shahid, Seham
The transportation industry contributes more than a quarter of the global greenhouse gas emissions and transportation electrification was introduced as a means to decarbonize the industry. One of the major challenges related to the electrification of technologies are the thermal challenges associated with Lithium-ion batteries which are the leading candidate for electric batteries. In this research, these thermal challenges have been investigated with the objective of effective cooling and increased thermal uniformity within cylindrical Lithium-ion batteries. To achieve this, novel hybrid thermal management strategies have been proposed that combine air, liquid, and phase change material cooling systems. Several configurations of the proposed strategies are designed and analyzed through both experimental and numerical studies. The proposed hybrid strategies were able to limit the maximum temperature of the battery module to below ~29 °C. The developed battery module also achieved the desired temperature uniformity to less than 5 °C. Furthermore, the proposed hybrid strategies eliminate the requirement of a pump and reservoir system since there is no flow of liquid coolant within the battery module. This reduces the energy required for the operation of the thermal management system, thereby increasing the available energy for propulsion. Therefore, the proposed hybrid strategies and battery modules are capable of maintaining the thermal environment required by the Lithium-ion batteries for effective performance and can also be scaled to an entire battery pack for a range of applications.
2024-01-01T00:00:00ZSpectrum sharing for multi-user massive MIMO networksSaif, Rosahttps://hdl.handle.net/10155/17352024-01-29T20:50:07Z2023-12-01T00:00:00ZSpectrum sharing for multi-user massive MIMO networks
Saif, Rosa
In this dissertation, we propose a creative approach to advance 5G network technologies by investigating the impact of employing an underlay spectrum sharing (USS) scheme on the performance of two massive multi-input-multi-output (MIMO) networks. We explore an USS approach where a multi-user massive MIMO network (primary network (PN)), i.e., the owner of the frequency spectrum, allows another multi-user massive MIMO network, the secondary network (SN), to utilize its allocated spectrum to serve secondary users (SUs). Within this context, we devise joint power allocation and beamforming techniques at the SN for both conventional time-division duplexing (C-TDD) and reverse time-division duplexing (R-TDD) protocols. In the C-TDD approach, both the PN and SN operate concurrently in either the uplink (UL) or downlink (DL) modes. In the R-TDD protocol, the PN and SN do not simultaneously operate in the UL or DL modes. It is worth noting that, during the training phase of the PN (learning phase of the SN), all the SN’s nodes remain silent and listen to the PN to acquire as much information as possible about the PN. The optimization problems aim to maximize the SN’s achievable sum-rate in both UL and DL, while guaranteeing the minimum acceptable individual rate for each primary user (PU) and satisfying the SN’s power constraints. Effective solutions are proposed for both the C-TDD and R-TDD protocols, including novel methods to mitigate interference caused by the SN’s nodes to the PN’s nodes during UL and DL phases. We assume that the PN parameters are set by the PN independently, without considering the presence of the SN, to minimize the SN’s potential impact on the PN’s frame structure and system design. Our simulation results demonstrate that for a moderate-scale SN coverage area, both C-TDD and R-TDD approaches reveal almost comparable performance. Additionally, changes in the SN’s settings have a small effect on the total sum-rate of the PN when our proposed method is employed. Finally, for all tested values of the number of antennas at the secondary base stations (SBS), the R-TDD approach outperforms the C-TDD approach when the SN coverage area is large, and vice versa.
2023-12-01T00:00:00ZDesign and development of a context-aware collaborative autonomous real-time vehicle systems frameworkPereira Peixoto, Maria Joelmahttps://hdl.handle.net/10155/17312024-01-23T21:29:16Z2023-11-01T00:00:00ZDesign and development of a context-aware collaborative autonomous real-time vehicle systems framework
Pereira Peixoto, Maria Joelma
The increasing autonomy of intelligent systems, with applications extending from self-driving vehicles to home-based robots, has emerged as a critical area of focus in modern research. Yet, to acknowledge the full potential of these systems, numerous challenges must be addressed. This thesis encapsulates rigorous research resulting in eight scientific papers investigating autonomous systems’ efficacy and efficiency. Our study proposes the Context-Aware Collaborative Autonomous Real-Time Vehicle Systems (CARVS) Framework and focuses on improving context awareness, simplifying remote task processing, and quantifying prediction uncertainty in Machine Learning (ML) algorithms. Our intention is to move forward the state-of-the-art in autonomous systems based on our findings as we investigate the employment of noise as a stimulus to boost agent exploration. We also address the development of mapping and task management systems for connected autonomous vehicles (CAVs) using edge, fog, and cloud computing. Furthermore, we study the quantification of uncertainty in ML algorithm predictions to describe their behaviours and decision-making mechanisms. This research provides valuable insights for the continuous improvement of autonomous learning and the ability to deal with uncertainties in dynamic and unpredictable environments, which could lead to greater acceptance of such systems.
2023-11-01T00:00:00Z