System modelling and control

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Review of advanced guidance and control algorithms for space/aerospace vehicles

The design of advanced guidance and control (G&C) systems for space/aerospace vehicles has received a large amount of attention worldwide during the last few decades and will continue to be a main focus of the aerospace industry. Not surprisingly, due to the existence of various model uncertainties and environmental disturbances, robust and stochastic control-based methods have played a key role in G&C system design, and numerous effective algorithms have been successfully constructed to guide and steer the motion of space/aerospace vehicles. Apart from these stability theory-oriented techniques, in recent years, we have witnessed a growing trend of designing optimisation theory-based and artificial intelligence (AI)-based controllers for space/aerospace vehicles to meet the growing demand for better system performance. Related studies have shown that these newly developed strategies can bring many benefits from an application point of view, and they may be considered to drive the onboard decision-making system. In this paper, we provide a systematic survey of state-of-the-art algorithms that are capable of generating reliable guidance and control commands for space/aerospace vehicles. The paper first provides a brief overview of space/aerospace vehicle guidance and control problems. Following that, a broad collection of academic works concerning stability theory-based G&C methods is discussed. Some potential issues and challenges inherent in these methods are reviewed and discussed. Then, an overview is given of various recently developed optimisation theory-based methods that have the ability to produce optimal guidance and control commands, including dynamic programming-based methods, model predictive control-based methods, and other enhanced versions. The key aspects of applying these approaches, such as their main advantages and inherent challenges, are also discussed. Subsequently, a particular focus is given to recent attempts to explore the possible uses of AI techniques in connection with the optimal control of the vehicle systems. The highlights of the discussion illustrate how space/aerospace vehicle control problems may benefit from these AI models. Finally, some practical implementation considerations, together with a number of future research topics, are summarised

Control oriented modelling of an integrated attitude and vibration suppression architecture for large space structures

This thesis is divided into two parts. The main focus of the research, namely active vibration control for large flexible spacecraft, is exposed in Part I and, in parallel, the topic of machine learning techniques for modern space applications is described in Part II. In particular, this thesis aims at proposing an end-to-end general architecture for an integrated attitude-vibration control system, starting from the design of structural models to the synthesis of the control laws. To this purpose, large space structures based on realistic missions are investigated as study cases, in accordance with the tendency of increasing the size of the scientific instruments to improve their sensitivity, being the drawback an increase of its overall flexibility. An active control method is therefore investigated to guarantee satisfactory pointing and maximum deformation by avoiding classical stiffening methods. Therefore, the instrument is designed to be supported by an active deployable frame hosting an optimal minimum set of collocated smart actuators and sensors. Different spatial configurations for the placement of the distributed network of active devices are investigated, both at closed-loop and open-loop levels. Concerning closed-loop techniques, a method to optimally place the poles of the system via a Direct Velocity Feedback (DVF) controller is proposed to identify simultaneously the location and number of active devices for vibration control with an in-cascade optimization technique. Then, two general and computationally efficient open-loop placement techniques, namely Gramian and Modal Strain Energy (MSE)-based methods, are adopted as opposed to heuristic algorithms, which imply high computational costs and are generally not suitable for high-dimensional systems, to propose a placement architecture for generically shaped tridimensional space structures. Then, an integrated robust control architecture for the spacecraft is presented as composed of both an attitude control scheme and a vibration control system. To conclude the study, attitude manoeuvres are performed to excite main flexible modes and prove the efficacy of both attitude and vibration control architectures. Moreover, Part II is dedicated to address the problem of improving autonomy and self-awareness of modern spacecraft, by using machine-learning based techniques to carry out Failure Identification for large space structures and improving the pointing performance of spacecraft (both flexible satellite with sloshing models and small rigid platforms) when performing repetitive Earth Observation manoeuvres

Developments in predictive displays for discrete and continuous tasks

The plan of the thesis is as follows: The introductory chapters review the literature pertaining to human prediction and predictive control models (Chapter 1), and to engineering aspects of predictive displays (Chapter 2). Chapter 3 describes a fundamental study of predictive display parameters in a laboratory scheduling task, Chapter 4 attempts to verify these findings using test data from an actual job shop scheduling problem. Chapter 5 branches into the area of continuous control with a pilot study of predictive displays in a laboratory simulated continuous stirred-tank chemical reactor. Chapter 6 uses the experience gained in the pilot study as the basis for a comprehensive study of predictive display parameters in a further laboratory study of a simplified dual-meter monitoring and control task, and Chapter 7 attempts to test the optimal design in a part-simulated semi-batch chemical reactor using real plant and experienced operators in an industrial setting. The results of the experimental programme are summarized for convenience in Chapter 8. Chapter 9 draws together the threads from the various experiments and discusses the findings in terms of a general hierarchical model of an operator's control and monitoring behaviour. Finally, Chapter 10 presents conclusions and recommendations from the programme of research, together with suggestions for further work

Advanced control for miniature helicopters : modelling, design and flight test

Unmanned aerial vehicles (UAV) have been receiving unprecedented development during the past two decades. Among different types of UAVs, unmanned helicopters exhibit promising features gained from vertical-takeoff-and-landing, which make them as a versatile platform for both military and civil applications. The work reported in this thesis aims to apply advanced control techniques, in particular model predictive control (MPC), to an autonomous helicopter in order to enhance its performance and capability. First, a rapid prototyping testbed is developed to enable indoor flight testing for miniature helicopters. This testbed is able to simultaneously observe the flight state, carry out complicated algorithms and realtime control of helicopters all in a Matlab/Simulink environment, which provides a streamline process from algorithm development, simulation to flight tests. Next, the modelling and system identification for small-scale helicopters are studied. A parametric model is developed and the unknown parameters are estimated through the designed identification process. After a mathematical model of the selected helicopter is available, three MPC based control algorithms are developed focusing on different aspects in the operation of autonomous helicopters. The first algorithm is a nonlinear MPC framework. A piecewise constant scheme is used in the MPC formulation to reduce the intensive computation load. A two-level framework is suggested where the nonlinear MPC is combined with a low-level linear controller to allow its application on the systems with fast dynamics. The second algorithm solves the local path planning and the successive tracking control by using nonlinear and linear MPC, respectively. The kinematics and obstacle information are incorporated in the path planning, and the linear dynamics are used to design a flight controller. A guidance compensator dynamically links the path planner and flight controller. The third algorithm focuses on the further reduction of computational load in a MPC scheme and the trajectory tracking control in the presence of uncertainties and disturbances. An explicit nonlinear MPC is developed for helicopters to avoid online optimisation, which is then integrated with a nonlinear disturbance observer to significantly improve its robustness and disturbance attenuation. All these algorithms have been verified by flight tests for autonomous helicopters in the dedicated rapid prototyping testbed developed in this thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations

Manipulation of uncooperative rotating objects in space with a modular self-reconfigurable robot

The following thesis is a feasibility study for the controlled deployment of robotic scaffolding structures on randomly tumbling objects with low-magnitude gravitational field for use in space applications such as space debris removal, spacecraft maintenance and asteroids capture and mining. The proposed solution is based on the novel use of self-reconfigurable modular robots performing deployments on randomly tumbling objects as a task-driven reconfiguration or manipulation through reconfiguration. The robot design focused on its control strategy which used a decentralised modular controller with two levels. One high-level behaviour-based component and one low-level component generating commands via a constrained optimisation using either a linear or a non-linear model predictive control approach and constituting a novel control method for rotating objects via angular momentum exchanges and mass distribution changes. The controller design relied on modelling the robot modules and the object as a rotating discretised deformable continuum whose rigid part, the object, was an ellipsoid. All parameters were normalised when possible and disturbances, sensors and actuator errors were modelled respectively as biased white noises and coloured noises. The correctness of the overall control algorithm was ensured. The main objective of the MPC controllers was to control the deployment of a module from the tip of the spinning axis to the plane containing the object’s centre of mass while coiling around the spinning axis and ensuring the object’s rotational state tracked a reference state. Simulations showed that the nonlinear MPC controller should be preferred over a linear one and that, for a mass ratio of the object’s to the module’s equal to 10000, the nonlinear MPC controller is best suited to stability maintenance and meets the deployment requirement, suggesting that the proposed solution would be acceptable for medium-size objects such as asteroids