On-line estimation approaches to fault-tolerant control of uncertain systems

This thesis is concerned with fault estimation in Fault-Tolerant Control (FTC) and as such involves the joint problem of on-line estimation within an adaptive control system. The faults that are considered are significant uncertainties affecting the control variables of the process and their estimates are used in an adaptive control compensation mechanism. The approach taken involves the active FTC, as the faults can be considered as uncertainties affecting the control system. The engineering (application domain) challenges that are addressed are: (1) On-line model-based fault estimation and compensation as an FTC problem, for systems with large but bounded fault magnitudes and for which the faults can be considered as a special form of dynamic uncertainty. (2) Fault-tolerance in the distributed control of uncertain inter-connected systems The thesis also describes how challenge (1) can be used in the distributed control problem of challenge (2). The basic principle adopted throughout the work is that the controller has two components, one involving the nominal control action and the second acting as an adaptive compensation for significant uncertainties and fault effects. The fault effects are a form of uncertainty which is considered too large for the application of passive FTC methods. The thesis considers several approaches to robust control and estimation: augmented state observer (ASO); sliding mode control (SMC); sliding mode fault estimation via Sliding Mode Observer (SMO); linear parameter-varying (LPV) control; two-level distributed control with learning coordination

Cooperation of unmanned systems for agricultural applications: A theoretical framework

Agriculture 4.0 comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management with the objective of optimising production by accounting for variabilities and uncertainties within agricultural systems. Autonomous ground and aerial vehicles can lead to favourable improvements in management by performing in-field tasks in a time-effective way. In particular, greater benefits can be achieved by allowing cooperation and collaborative action among unmanned vehicles, both aerial and ground, to perform in-field operations in precise and time-effective ways. In this work, the preliminary and crucial step of analysing and understanding the technical and methodological challenges concerning the main problems involved is performed. An overview of the agricultural scenarios that can benefit from using collaborative machines and the corresponding cooperative schemes typically adopted in this framework are presented. A collection of kinematic and dynamic models for different categories of autonomous aerial and ground vehicles is provided, which represents a crucial step in understanding the vehicles behaviour when full autonomy is desired. Last, a collection of the state-of-the-art technologies for the autonomous guidance of drones is provided, summarising their peculiar characteristics, and highlighting their advantages and shortcomings with a specific focus on the Agriculture 4.0 framework. A companion paper reports the application of some of these techniques in a complete case study in sloped vineyards, applying the proposed multi-phase collaborative scheme introduced here

Damping controller design for FACTS devices in power systems using novel control techniques

Power systems are under increasing stress as deregulation introduces several new economic objectives for operation. Since power systems are being operated close to their limits, weak connections, unexpected events, hidden failures in protection system, human errors, and a host of other factors may cause a system to lose stability and even lead to catastrophic failure. Therefore, the need for improved system damping in a wider operating range is gaining more attention. Among the available damping control methods, each approach has advantages and disadvantages in different systems. The effectiveness of damping control depends on the devices chosen, the system modal feature, and the applied controller design method;In the literature, many approaches have been proposed to undertake this task. However, some of these approaches only take a fixed operating point into consideration without describing the changing uncertainty in varying system conditions; computational effort. Furthermore, no systematic comparison of controller design methods has been conducted with regard to different system profiles. Attention has been drawn to the enhanced susceptibility to inter-area oscillations between groups of machines under large others require a great deal of variation of system operating conditions. The linear parameter varying (LPV) approach, which has been widely studied in the literature, provides a potential method for capturing the varying system condition precisely without formulation of system uncertainty. However, in some cases no solution can be achieved if the system variation is too large using the traditional LPV approach. Also, sometimes the system structure imposes limitations in the achievable damping performance. In general, there is a critical need for a cost-effective control strategy applicable to different systems from an economic point of view;In this dissertation, a comprehensive comparison among controller design methods has been conducted to study the damping effectiveness of different FACTS devices. Based on these, a robust regional pole-placement method is applied in a TCSC damping controller design in a 4-machine system; an interpolated LPV approach is proposed and applied to designing a SVC damping controller in the IEEE 50-machine system; finally with the advantage of an additional feedback signal, limitations in achieving satisfactory damping performance can be relieved using a two-input single-output (TISO) damping controller for a TCSC in the IEEE 50-machine system

Robust Nonlinear Model Predictive Control using Polynomial Chaos Expansions

The performance of model predictive controllers (MPCs) is largely dependent on the accuracy of the model predictions as compared to the actual plant outputs. Irrespective of the model used, first-principles (FP) or empirical, plant-model mismatch is unavoidable. Consequently, model based controllers must be robust to mismatch between the model predictions and the actual process behavior. Controllers that are not robust may result in poor closed loop response and even instability. Model uncertainty can generally be formulated into two broader forms, parametric uncertainty and unstructured uncertainty. Most of the current robust nonlinear MPC have been based on FP-model where only robustness to bounded disturbances rather than parametric uncertainty has been addressed. Systematically accounting for parametric uncertainty in the robust design has been difficult in FP-models due to varying forms in which uncertain parameters occur in the models. To address parametric uncertainty robustness tests based on Structured Singular Value (SSV) and Linear Matrix Inequalities (LMI) have been proposed previously, however these algorithms tend to be conservative because they consider worst-case scenarios and they are also computationally expensive. For instance the SSV calculation is NP-hard and as a result it is not suitable for fast computations. This provides motivation to work on robust control algorithms addressing both parametric and unstructured uncertainty with fast computation times. To facilitate the design of robust controllers which can be computed fast, empirical models are used in which parametric uncertainty is propagated using Polynomial Chaos Expansion (PCE) of parameters. PCE assists in speeding up the computations by providing an analytical expression for the L^2-norm of model predictions while also eliminating the need to design for the worst-case scenario which results in conservatism. Another way of speeding up computations in MPC algorithms is by grouping subsets of available the inputs and outputs into subsystems and by controlling each of the subsystems by MPC controllers of lower dimensions. This latter approach, referred in the literature as Distributed MPC, has been tackled by different strategies involving different degrees of coordination between subsystems but it has not been studied in terms of robustness to model error. Based on the above considerations the current work investigates different robustness aspects of predictive control algorithms for nonlinear processes with special emphasis on the following three situations, i) a nonlinear predictive control based on a Volterra series model where the uncertain parameters are formulated as PCE’s, ii) The application of a PCE-based approach to control and optimization of bioreactors where the model is based on dynamic flux metabolic models, and iii) A Robust Distributed MPC with a robust estimator that is needed to account for the interactions between sub-systems in distributed control

A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared

The optimal control of power electronic embedded networks in More Electric Aircraft

With the advancement of power electronic technologies over recent decades, there has been an overall increase in the utilisation of distributed generation and power electronic embedded networks in a large sphere of applications. Probably one of the most prominent areas of utilisation of new power electronics embedded systems is the use in power networks onboard military and civilian aircraft. With environmental concerns and increased competition in the civil aviation sector, more aircraft manufactures are replacing and interfacing electrical alternatives over heavier, less efficient and costly pneumatic, hydraulic and mechanical systems. In these modern power systems, the increased proliferation of power electronic converters and distributed generation raises important issues in regards to the performance, stability and robustness between interfaced switching units. These phenomena, such as power electronic sub-system interactions, become even more prominent in micro-grid applications or other low voltage distribution systems where interfaced converters are in close proximity to one another. In More Electric Aircraft (MEA), these interfaced power electronic converters are connected to the same non-stiff low power AC grid, which further increases the interactive effects between converter sub-systems. If these effects are not properly taken into account, then external disturbances to the system at given operating conditions can result in degradation of the system performance, failure in meeting the operating requirements of the grid, or in the worst case, instability of the whole grid. With much research in the area of decreasing the size and weight of systems, there is much literature proposing optimisation methods which decrease the size of filters between interfacing converters. Whilst effectively decreasing the size of these systems, interactions between interfaced converters gets worse, and is often improperly accounted for. The work presented in this thesis proposes a novel approach to the decentralisation and optimisation of converter controls on a power electronics embedded power network. In order to account for the interactive dynamics between sub-systems in the environment of reduced passive filter networks, all the system dynamics including the interactive terms are modelled globally. An optimal controller design approach based on the H2 optimisation is proposed to synthesise and generate automatically the controller gains for each power electronic sub-system. H2 optimisation is a powerful tool, which not only allows the submission, optimisation and development of closed loop controls for large dynamic systems, but offers the ability to the user to construct the controller for given structures. This enables the development of decentralised controllers for every sub-system with intrinsic knowledge of the closed loop dynamics of every other interconnect sub-system. It is shown through simulation and by experimental validation that this novel approach to grid control optimisation not only can improve overall dynamic performance of all sub-systems over 15traditional methods of design, but can also intrinsically reduce or better yet mitigate against the interactive effects between all converters. In addition, this method of controller design will be shown to not only be scalable to expanding sizes of grids, but the Phase-locked loops (PLLs) integrated to grid connected devices can also be considered in the optimisation procedure. PLLs are widely known to further cause interactive behaviours between grid interfaced devices. Including this into the optimisation also has been validated experimentally to prevent interactions on the grid, and improve performance over traditional design methods. Adaptations to the controller are performed to ensure operation in variable frequency environments (as is common in MEA), as well as methods of single converter optimisation when interfacing to an unknown grid. Additionally some initial research towards an adaption of the H2 controller to incorporate robustness as well as performance into the optimisation procedure is presented with mathematical concepts shown through simulation

The optimal control of power electronic embedded networks in More Electric Aircraft

With the advancement of power electronic technologies over recent decades, there has been an overall increase in the utilisation of distributed generation and power electronic embedded networks in a large sphere of applications. Probably one of the most prominent areas of utilisation of new power electronics embedded systems is the use in power networks onboard military and civilian aircraft. With environmental concerns and increased competition in the civil aviation sector, more aircraft manufactures are replacing and interfacing electrical alternatives over heavier, less efficient and costly pneumatic, hydraulic and mechanical systems. In these modern power systems, the increased proliferation of power electronic converters and distributed generation raises important issues in regards to the performance, stability and robustness between interfaced switching units. These phenomena, such as power electronic sub-system interactions, become even more prominent in micro-grid applications or other low voltage distribution systems where interfaced converters are in close proximity to one another. In More Electric Aircraft (MEA), these interfaced power electronic converters are connected to the same non-stiff low power AC grid, which further increases the interactive effects between converter sub-systems. If these effects are not properly taken into account, then external disturbances to the system at given operating conditions can result in degradation of the system performance, failure in meeting the operating requirements of the grid, or in the worst case, instability of the whole grid. With much research in the area of decreasing the size and weight of systems, there is much literature proposing optimisation methods which decrease the size of filters between interfacing converters. Whilst effectively decreasing the size of these systems, interactions between interfaced converters gets worse, and is often improperly accounted for. The work presented in this thesis proposes a novel approach to the decentralisation and optimisation of converter controls on a power electronics embedded power network. In order to account for the interactive dynamics between sub-systems in the environment of reduced passive filter networks, all the system dynamics including the interactive terms are modelled globally. An optimal controller design approach based on the H2 optimisation is proposed to synthesise and generate automatically the controller gains for each power electronic sub-system. H2 optimisation is a powerful tool, which not only allows the submission, optimisation and development of closed loop controls for large dynamic systems, but offers the ability to the user to construct the controller for given structures. This enables the development of decentralised controllers for every sub-system with intrinsic knowledge of the closed loop dynamics of every other interconnect sub-system. It is shown through simulation and by experimental validation that this novel approach to grid control optimisation not only can improve overall dynamic performance of all sub-systems over 15traditional methods of design, but can also intrinsically reduce or better yet mitigate against the interactive effects between all converters. In addition, this method of controller design will be shown to not only be scalable to expanding sizes of grids, but the Phase-locked loops (PLLs) integrated to grid connected devices can also be considered in the optimisation procedure. PLLs are widely known to further cause interactive behaviours between grid interfaced devices. Including this into the optimisation also has been validated experimentally to prevent interactions on the grid, and improve performance over traditional design methods. Adaptations to the controller are performed to ensure operation in variable frequency environments (as is common in MEA), as well as methods of single converter optimisation when interfacing to an unknown grid. Additionally some initial research towards an adaption of the H2 controller to incorporate robustness as well as performance into the optimisation procedure is presented with mathematical concepts shown through simulation