Filter and control performance bounds in the presence of model uncertainties with aerospace applications

This thesis establishes performance properties for approximate filters and controllers that are designed on the basis of approximate dynamic system representations. These performance properties provide a theoretical justification for the widespread application of approximate filters and controllers in the common situation where system models are not known with complete certainty. This research also provides useful tools for approximate filter designs, which are applied to hybrid filtering of uncertain nonlinear systems. As a contribution towards applications, this thesis also investigates air traffic separation control in the presence of measurement uncertainties.

Locally weighted learning model predictive control for nonlinear and time varying dynamics

This paper proposes an online learning control system that uses the strategy of Model Predictive Control (MPC) in a model based locally weighted learning framework. The new approach, named Locally Weighted Learning Model Predictive Control (LWL-MPC), is proposed as a solution to learn to control robotic systems with nonlinear and time varying dynamics. This paper demonstrates the capability of LWL-MPC to perform online learning while controlling the joint trajectories of a low cost, three degree of freedom elastic joint robot. The learning performance is investigated in both an initial learning phase, and when the system dynamics change due to a heavy object added to the tool point. The experiment on the real elastic joint robot is presented and LWL-MPC is shown to successfully learn to control the system with and without the object. The results highlight the capability of the learning control system to accommodate the lack of mechanical consistency and linearity in a low cost robot arm.

Model-predictive control and closed-loop stability considerations for nonlinear plants described by local ARX-type models

In this paper, a model-predictive control (MPC) method is detailed for the control of nonlinear systems with stability considerations. It will be assumed that the plant is described by a local input/output ARX-type model, with the control potentially included in the premise variables, which enables the control of systems that are nonlinear in both the state and control input. Additionally, for the case of set point regulation, a suboptimal controller is derived which has the dual purpose of ensuring stability and enabling finite-iteration termination of the iterative procedure used to solve the nonlinear optimization problem that is used to determine the control signal.

Power buffer with model predictive control for stability of vehicular power systems with constant power loads

Electrification of vehicular systems has gained increased momentum in recent years with particular attention to constant power loads (CPLs). Since a CPL potentially threatens system stability, stability analysis of hybrid electric vehicle with CPLs becomes necessary. A new power buffer configuration with battery is introduced to mitigate the effect of instability caused by CPLs. Model predictive control (MPC) is applied to regulate the power buffer to decouple source and load dynamics. Moreover, MPC provides an optimal tradeoff between modification of load impedance, variation of dc-link voltage and battery current ripples. This is particularly important during transients or starting of system faults, since battery response is not very fast. Optimal tradeoff becomes even more significant when considering low-cost power buffer without battery. This paper analyzes system models for both voltage swell and voltage dip faults. Furthermore, a dual mode MPC algorithm is implemented in real time offering improved stability. A comprehensive set of experimental results is included to verify the efficacy of the proposed power buffer.

Non-linear model predictive control for UAVs with slung/swung load

Suspended loads on UAVs can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present software and flight system architecture to test controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions.

Control of ion density distribution by magnetic traps for plasma electrons

The effect of a magnetic field of two magnetic coils on the ion current density distribution in the setup for low-temperature plasma deposition is investigated. The substrate of 400 mm diameter is placed at a distance of 325 mm from the plasma duct exit, with the two magnetic coils mounted symmetrically under the substrate at a distance of 140 mm relative to the substrate centre. A planar probe is used to measure the ion current density distribution along the plasma flux cross-sections at distances of 150, 230, and 325 mm from the plasma duct exit. It is shown that the magnetic field strongly affects the ion current density distribution. Transparent plastic films are used to investigate qualitatively the ion density distribution profiles and the effect of the magnetic field. A theoretical model is developed to describe the interaction of the ion fluxes with the negative space charge regions associated with the magnetic trapping of the plasmaelectrons. Theoretical results are compared with the experimental measurements, and a reasonable agreement is demonstrated.

Nonlinear Model Predictive Control for a multi-rotor with heavy slung load

In this paper a novel controller for stable and precise operation of multi-rotors with heavy slung loads is introduced. First, simplified equations of motions for the multi-rotor and slung load are derived. The model is then used to design a Nonlinear Model Predictive Controller (NMPC) that can manage the highly nonlinear dynamics whilst accounting for system constraints. The controller is shown to simultaneously track specified waypoints whilst actively damping large slung load oscillations. A Linear-quadratic regulator (LQR) controller is also derived, and control performance is compared in simulation. Results show the improved performance of the Nonlinear Model Predictive Control (NMPC) controller over a larger flight envelope, including aggressive maneuvers and large slung load displacements. Computational cost remains relatively small, amenable to practical implementation. Such systems for small Unmanned Aerial Vehicles (UAVs) may provide significant benefit to several applications in agriculture, law enforcement and construction.

Control of solar powered micro-grids using electric vehicles

Large scale solar plants are gaining recognition as potential energy sources for future. In this paper, the feasibility of using electric vehicles (EVs) to control a solar powered micro-grid is investigated in detail. The paper presents a PSCAD/EMTDC based model for the solar powered micro-grid with EVs. EVs are expected to have both the vehicle-to-grid (V2G) and grid-to-vehicle (G2V) capability, through which energy can either be injected into or extracted from the solar powered micro-grid to control its energy imbalance. Using the model, the behaviour of the micro-grid is investigated under a given load profile, and the results indicate that a minimum number of EVs are required to meet the energy imbalance and it is time dependent and influenced by various factors such as depth of charge, commuting profiles, reliability etc...

A policy model for access control using building information models

Building information models have created a paradigm shift in how buildings are built and managed by providing a dynamic repository for building data that is useful in many new operational scenarios. This change has also created an opportunity to use building information models as an integral part of security operations and especially as a tool to facilitate fine-grained access control to building spaces in smart buildings and critical infrastructure environments. In this paper, we identify the requirements for a security policy model for such an access control system and discuss why the existing policy models are not suitable for this application. We propose a new policy language extension to XACML, with BIM specific data types and functions based on the IFC specification, which we call BIM-XACML.

Stochastic output feedback model predictive control

This paper addresses the issue of output feedback model predictive control for linear systems with input constraints and stochastic disturbances. We show that the optimal policy uses the Kalman filter for state estimation, but the resultant state estimates are not utilized in a certainty equivalence control law

A model predictive approach for community battery energy storage system optimization

This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.

A model to estimate the durability performance of both normal and light-weight concrete

There has been an increasing focus on the development of test methods to evaluate the durability performance of concrete. This paper contributes to this focus by presenting a study that evaluates the effect of water accessible porosity and oven-dry unit weight on the resistance of both normal and light-weight concrete to chloride-ion penetration. Based on the experimental results and regression analyses, empirical models are established to correlate the total charge passed and the chloride migration coefficient with the basic properties of concrete such as water accessible porosity, oven dry unit weight, and compressive strength. These equations can be broadly applied to both normal and lightweight aggregate concretes. The model was also validated by an independent set of experimental results from two different concrete mixtures. The model provides a very good estimate on the concrete’s durability performance in respect to the resistance to chloride ion penetration.

Time-independent charge carrier mobility in a model polymer:fullerene organic solar cell

The technique of photo-CELIV (charge extraction by linearly increasing voltage) is one of the more straightforward and popular approaches to measure the faster carrier mobility in measurement geometries that are relevant for operational solar cells and other optoelectronic devices. It has been used to demonstrate a time-dependent photocarrier mobility in pristine polymers, attributed to energetic relaxation within the density of states. Conversely, in solar cell blends, the presence or absence of such energetic relaxation on transport timescales remains under debate. We developed a complete numerical model and performed photo-CELIV experiments on the model high efficiency organic solar cell blend poly[3,6-dithiophene-2-yl-2,5-di(2-octyldodecyl)-pyrrolo[3,4-c]pyrrole-1,4-dione-alt-naphthalene] (PDPP-TNT):[6,6]-phenyl-C71-butyric-acid-methyl-ester (PC70BM). In the studied solar cells a constant, time-independent mobility on the scale relevant to charge extraction was observed, where thermalisation of photocarriers occurs on time scales much shorter than the transit time. Therefore, photocarrier relaxation effects are insignificant for charge transport in these efficient photovoltaic devices.

The effects of prediction, understanding, and control: A test of the stress antidote model

A study was undertaken to examine further the effects of perceived work control on employee adjustment. On the basis of the stress antidote model, it was proposed that high levels of prediction, understanding, and control of work-related events would have direct, indirect, and interactive effects on levels of employee adjustment. These hypotheses were tested in a short-term longitudinal study of 137 employees of a large retail organization. The stress antidote measures appeared to be indirectly related to employee adjustment, via their effects on perceptions of work stress. There was weak evidence for the proposal that prediction, understanding, and control would buffer the negative effects of work stress. Additional analyses indicated that the observed effects of prediction, understanding, and control were independent of employees' generalized control beliefs. However, there was no support for the proposal that the effects of the stress antidote measures would be dependent on employees' generalized control beliefs.