Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part

A solution for improved simulation efficiency of a multi-domain marine power system model

Integrated Full Electric Propulsion (IFEP) marine power systems offer increased design flexibility and operational economy by supplying ship propulsion and service loads from a common electrical system. Predicting the behaviour of IFEP systems through simulation is important in reducing the design risk. However, the prevalence of power electronics and the potential for interaction between large electrical and mechanical systems introduce significant simulation challenges. This paper presents an integrated simulation tool, which brings together electrical, mechanical, thermal and hydrodynamic models, facilitating a holistic simulation capability. Approaches adopted for model validation and computational efficiency together with two case studies are discussed

REGULATION OF BLOOD GLUCOSE IN TYPE I DIABETIC PATIENTS

Ph.DDOCTOR OF PHILOSOPH

An Offline-Sampling SMPC Framework with Application to Automated Space Maneuvers

In this paper, a sampling-based Stochastic Model Predictive Control algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive disturbances. One of the main drivers for the development of the proposed control strategy is the need of real-time implementability of guidance and control strategies for automated rendezvous and proximity operations between spacecraft. The paper presents considers the validation of the proposed control algorithm on an experimental testbed, showing how it may indeed be implemented in a realistic framework. Parametric uncertainties due to the mass variations during operations, linearization errors, and disturbances due to external space environment are simultaneously considered. The approach enables to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided, and under mild assumptions, asymptotic stability in probability of the origin can be established. The offline sampling approach in the control design phase is shown to reduce the computational cost, which usually constitutes the main limit for the adoption of Stochastic Model Predictive Control schemes, especially for low-cost on-board hardware. These characteristics are demonstrated both through simulations and by means of experimental results

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems

Sliding mode control of constrained nonlinear systems

This technical note introduces the design of sliding mode control algorithms for nonlinear systems in the presence of hard inequality constraints on both control and state variables. Relying on general results on minimum-time higher-order sliding mode for unconstrained systems, a general order control law is formulated to robustly steer the state to the origin, while satisfying all the imposed constraints. Results on minimum-time convergence to the sliding manifold, as well as on the maximization of the domain of attraction, are analytically proved for the first-order and second-order sliding mode cases. A general result is presented regarding the domain of attraction in the general order case, while numerical results on the estimation of the domain of attraction and on minimum-time convergence are discussed for the third-order case, following a procedure applicable to a sliding mode of any order

De-risking Integrated Full Electric Propulsion (IFEP) vessels using advanced modelling and simulation techniques

Complex multi-domain engineering systems, where for example mechanical and thermal (sub)systems are connected to each other in some way, have increasingly become a vital part of our society. An example of such a system is the Integrated Full Electric Propulsion (IFEP) concept for the marine shipping industry. With this IFEP concept, as opposed to the more conventional marine power system, the power for the ship's propulsion and ship's services is provided by a common power plant. This offers advantages including fuel efficiency and design flexibility. However, due to its system complexity and capital costs, it is important that the overall dynamic behaviour of these systems can be predicted in the early stages of the design. Predicting the overall system behaviour can be obtained by employing an integrated end-to-end model, which combines detailed models of for example the mechanical and electrical (sub)systems. This allows for example ship designers to investigate disturbances and the primary and higher order responses across the system. However, present existing simulation tools do not easily facilitate such employment of a holistic approach. In this thesis the focus is on how advanced modelling and simulation techniques can be used to de-risk the design and in-service of complex IFEP systems. The state-of-the-art modelling and simulation techniques as well as the IFEP application area are considered. An integrated-model of an IFEP vessel was developed under the EPSRC collaborative AMEPS (Advanced Marine Electric Propulsion System) research project, which forms a major part of this thesis. In order to reduce the computational burden, due to a wide variety of time constants in the IFEP system, a multi-rate simulation technique was proposed. It was demonstrated that a reduction in simulation execution time between 10-15 times can be achieved. However, it was conceptually argued that multi-rate simulation could introduce errors, which propagates itself across the system thereby provoking potential unrealistic responses from other subsystems. Several case studies were conducted based on this model, which shows that such an integrated end-to-end model may be a valuable decision-support tool for de-risking the design and in-service phases of IFEP vessels. For example, it was demonstrated that a disturbance on the propeller could provoke a saturation of the gas turbine governor. Different power system architectures were proposed for IFEP power systems such as radial and hybrid AC/DC. For this thesis, an initial study was conducted to assess the relationship between the type of power system architecture and the vessel survivability. For this assessment an existing vessel survivability theory was further developed into a quantitative method. It was concluded that based on a comparative short circuit study and the proposed survivability method that the IFEP-hybrid AC/DC architecture offers the best vessel survivability.Complex multi-domain engineering systems, where for example mechanical and thermal (sub)systems are connected to each other in some way, have increasingly become a vital part of our society. An example of such a system is the Integrated Full Electric Propulsion (IFEP) concept for the marine shipping industry. With this IFEP concept, as opposed to the more conventional marine power system, the power for the ship's propulsion and ship's services is provided by a common power plant. This offers advantages including fuel efficiency and design flexibility. However, due to its system complexity and capital costs, it is important that the overall dynamic behaviour of these systems can be predicted in the early stages of the design. Predicting the overall system behaviour can be obtained by employing an integrated end-to-end model, which combines detailed models of for example the mechanical and electrical (sub)systems. This allows for example ship designers to investigate disturbances and the primary and higher order responses across the system. However, present existing simulation tools do not easily facilitate such employment of a holistic approach. In this thesis the focus is on how advanced modelling and simulation techniques can be used to de-risk the design and in-service of complex IFEP systems. The state-of-the-art modelling and simulation techniques as well as the IFEP application area are considered. An integrated-model of an IFEP vessel was developed under the EPSRC collaborative AMEPS (Advanced Marine Electric Propulsion System) research project, which forms a major part of this thesis. In order to reduce the computational burden, due to a wide variety of time constants in the IFEP system, a multi-rate simulation technique was proposed. It was demonstrated that a reduction in simulation execution time between 10-15 times can be achieved. However, it was conceptually argued that multi-rate simulation could introduce errors, which propagates itself across the system thereby provoking potential unrealistic responses from other subsystems. Several case studies were conducted based on this model, which shows that such an integrated end-to-end model may be a valuable decision-support tool for de-risking the design and in-service phases of IFEP vessels. For example, it was demonstrated that a disturbance on the propeller could provoke a saturation of the gas turbine governor. Different power system architectures were proposed for IFEP power systems such as radial and hybrid AC/DC. For this thesis, an initial study was conducted to assess the relationship between the type of power system architecture and the vessel survivability. For this assessment an existing vessel survivability theory was further developed into a quantitative method. It was concluded that based on a comparative short circuit study and the proposed survivability method that the IFEP-hybrid AC/DC architecture offers the best vessel survivability