Multidisciplinary design and flight testing of a remote gas/particle airborne sensor system

The main objective of this paper is to describe the development of a remote sensing airborne air sampling system for Unmanned Aerial Systems (UAS) and provide the capability for the detection of particle and gas concentrations in real time over remote locations. The design of the air sampling methodology started by defining system architecture, and then by selecting and integrating each subsystem. A multifunctional air sampling instrument, with capability for simultaneous measurement of particle and gas concentrations was modified and integrated with ARCAA’s Flamingo UAS platform and communications protocols. As result of the integration process, a system capable of both real time geo-location monitoring and indexed-link sampling was obtained. Wind tunnel tests were conducted in order to evaluate the performance of the air sampling instrument in controlled nonstationary conditions at the typical operational velocities of the UAS platform. Once the remote fully operative air sampling system was obtained, the problem of mission design was analyzed through the simulation of different scenarios. Furthermore, flight tests of the complete air sampling system were then conducted to check the dynamic characteristics of the UAS with the air sampling system and to prove its capability to perform an air sampling mission following a specific flight path.

A UKF-based estimation strategy for actuator fault detection of UASs

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating correspond- ing velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

Nonlinear actuator fault detection for small-scale UAS

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating corresponding velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

On parallel hybrid-electric propulsion system for unmanned aerial vehicles

This paper presents a review of existing and current developments and the analysis of Hybrid-Electric Propulsion Systems (HEPS) for small fixed-wing Unmanned Aerial Vehicles (UAVs). Efficient energy utilisation on an UAV is essential to its functioning, often to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and achieving the operational goals. One technology with potential in this area is with the use of HEPS. In this paper, information on the state-of-art technology in this field of research is provided. A description and simulation of a parallel HEPS for a small fixed-wing UAV by incorporating an Ideal Operating Line (IOL) control strategy is described. Simulation models of the components in a HEPS were designed in the MATLAB Simulink environment. An IOL analysis of an UAV piston engine was used to determine the most efficient points of operation for this engine. The results show that an UAV equipped with this HEPS configuration is capable of achieving a fuel saving of 6.5%, compared to the engine-only configuration.

Design and flight testing of an integrated solar powered UAV and WSN for remote gas sensing

This paper describes a generic and integrated solar powered remote Unmanned Air Vehicles (UAV) and Wireless Sensor Network (WSN) gas sensing system. The system uses a generic gas sensing system for CH4 and CO2 concentrations using metal oxide (MoX) and non-dispersive infrared sensors, and a new solar cell encapsulation method to power the UASs as well as a data management platform to store, analyse and share the information with operators and external users. The system was successfully field tested at ground and low altitudes, collecting, storing and transmitting data in real time to a central node for analysis and 3D mapping. The system can be used in a wide range of outdoor applications, especially in agriculture, bushfires, mining studies, opening the way to a ubiquitous low cost environmental monitoring. A video of the bench and flight test performed can be seen in the following link https://www.youtube.com/watch?v=Bwas7stYIxQ.

Enabling aircraft emergency landings using active visual site detection

The ability to automate forced landings in an emergency such as engine failure is an essential ability to improve the safety of Unmanned Aerial Vehicles operating in General Aviation airspace. By using active vision to detect safe landing zones below the aircraft, the reliability and safety of such systems is vastly improved by gathering up-to-the-minute information about the ground environment. This paper presents the Site Detection System, a methodology utilising a downward facing camera to analyse the ground environment in both 2D and 3D, detect safe landing sites and characterise them according to size, shape, slope and nearby obstacles. A methodology is presented showing the fusion of landing site detection from 2D imagery with a coarse Digital Elevation Map and dense 3D reconstructions using INS-aided Structure-from-Motion to improve accuracy. Results are presented from an experimental flight showing the precision/recall of landing sites in comparison to a hand-classified ground truth, and improved performance with the integration of 3D analysis from visual Structure-from-Motion.

Part 1 MOO methods for multidisciplinary design using parallel evolutionary algorithms, game theory and hierarchical topology. (Part 1.) Theoretical aspects

Two lecture notes describe recent developments of evolutionary multi objective optimization (MO) techniques in detail and their advantages and drawbacks compared to traditional deterministic optimisers. The role of Game Strategies (GS), such as Pareto, Nash or Stackelberg games as companions or pre-conditioners of Multi objective Optimizers is presented and discussed on simple mathematical functions in Part I , as well as their implementations on simple aeronautical model optimisation problems on the computer using a friendly design framework in Part II. Real life (robust) design applications dealing with UAVs systems or Civil Aircraft and using the EAs and Game Strategies combined material of Part I & Part II are solved and discussed in Part III providing the designer new compromised solutions useful to digital aircraft design and manufacturing. Many details related to Lectures notes Part I, Part II and Part III can be found by the reader in [68].

Recursive actuator fault detection and diagnosis for emergency landing of UASs

This paper presents a practical recursive fault detection and diagnosis (FDD) scheme for online identification of actuator faults for unmanned aerial systems (UASs) based on the unscented Kalman filtering (UKF) method. The proposed FDD algorithm aims to monitor health status of actuators and provide indication of actuator faults with reliability, offering necessary information for the design of fault-tolerant flight control systems to compensate for side-effects and improve fail-safe capability when actuator faults occur. The fault detection is conducted by designing separate UKFs to detect aileron and elevator faults using a nonlinear six degree-of-freedom (DOF) UAS model. The fault diagnosis is achieved by isolating true faults by using the Bayesian Classifier (BC) method together with a decision criterion to avoid false alarms. High-fidelity simulations with and without measurement noise are conducted with practical constraints considered for typical actuator fault scenarios, and the proposed FDD exhibits consistent effectiveness in identifying occurrence of actuator faults, verifying its suitability for integration into the design of fault-tolerant flight control systems for emergency landing of UASs.

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.

Part 2. MOO methods for multidisciplinary design using parallel evolutionary algorithms, game theory and hierarchical topology. (Part 2.) Numerical aspects and implementation of model test cases

These lecture notes describe the use and implementation of a framework in which mathematical as well as engineering optimisation problems can be analysed. The foundations of the framework and algorithms described -Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEAs) - lie upon traditional evolution strategies and incorporate the concepts of a multi-objective optimisation, hierarchical topology, asynchronous evaluation of candidate solutions , parallel computing and game strategies. In a step by step approach, the numerical implementation of EAs and HAPEAs for solving multi criteria optimisation problems is conducted providing the reader with the knowledge to reproduce these hand on training in his – her- academic or industrial environment.

Part 3. MOO methods for multidisciplinary design using parallel evolutionary algorithms, game theory and hierarchical topology. (Part 3.) Practical application to the design and optimisation of UAV systems

These lecture notes highlight some of the recent applications of multi-objective and multidisciplinary design optimisation in aeronautical design using the framework and methodology described in References 8, 23, 24 and in Part 1 and 2 of the notes. A summary of the methodology is described and the treatment of uncertainties in flight conditions parameters by the HAPEAs software and game strategies is introduced. Several test cases dealing with detailed design and computed with the software are presented and results discussed in section 4 of these notes.

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.

A framework for people capability enhancement to support sustainable facility management practices

Spanning over a considerable length of time, facility management is a key phase in the development cycle of built assets. Therefore facility managers are in a commanding position to maximise the potential of sustainability through the operation, maintenance and upgrade of built facilities leading to decommission and deconstruction. Sustainability endeavours in facility management practices will not only contribute to reducing energy consumption, waste and running costs, but also help improve organisational productivity, financial returns and community standing of the organisation. At the forefront facing sustainability challenge, facility manager should be empowered with the necessary knowledge and capabilities. However, literature studies show a gap between the current level of awareness and the specific knowledge and necessary skills required to pursue sustainability in the profession. People capability is considered as the key enabler in managing the sustainability agenda as well as being central to the improvement of competency and innovation in an organization. This paper aims to identify the critical factors for enhancing people capabilities in promoting the sustainability agenda in facility management practices. Starting with a total of 60 factors identified through literature review, the authors conducted a questionnaire survey to assess the perceived importance of these factors. The findings reveal 23 critical factors as significantly important. They form the basis of a mechanism framework developed to equip facility managers with the right knowledge, to continue education and training and to develop new mind-sets to enhance the implementation of sustainability measures in FM practices.

Green urbanism and diffusion issues

This paper addresses the research question, ‘What are the diffusion determinants for green urbanism innovations in Australia?’ This is a significant topic given the global movement towards green urbanism. The study reported here is based on desktop research that provides new insights through (1) synthesis of the latest research findings on green urbanism innovations and (2) interpretation of diffusion issues through our innovation system model. Although innovation determinants have been studied extensively overseas and in Australia, there is presently a gap in the literature when it comes to these determinants for green urbanism in Australia. The current paper fills this gap. Using a conceptual framework drawn from the innovation systems literature, this paper synthesises and interprets the literature to map the current state of green urbanism innovations in Australia and to analyse the drivers for, and obstacles to, their optimal diffusion. The results point to the importance of collaboration between project-based actors in the implementation of green urbanism. Education, training and regulation across the product system is also required to improve the cultural and technical context for implementation. The results are limited by their exploratory nature and future research is planned to quantify barriers to green urbanism.