Integrated series active filter for aerospace flight control surface actuation

The paper investigates integrated series active filters to satisfy aircraft power quality benchmarks and underlying design compromises. Advantages include reduced component count and retrofitting capability. Further insights into the merits of the proposed solution are included, along with representative results from a prototype system

Nonlinear state-observer techniques for sensorless control of automotive PMSM's, including load-torque estimation and saliency

The paper investigates various non-linear observer-based rotor position estimation schemes for sensorless control of permanent magnet synchronous motors (PMSMs). Attributes of particular importance to the application of brushless motors in the automotive sector, are considered e.g. implementation cost, accuracy of predictions during load transients, the impact of motor saliency and algorithm complexity. Emphasis is given to techniques based on model linearisation during each sampling period (EKF); feedback-linearisation followed by Luenberger observer design based on the resulting �linear� motor characteristics; and direct design of non-linear observers. Although the benefits of sensorless commutation of PMSMs have been well expounded in the literature, an integrated approach to their design for application to salient machines subject to load torque transients remains outstanding. Furthermore, this paper shows that the inherent characteristics of some non-linear observer structures are particularly attractive since they provide a phase-locked-loop (PLL)-type of configuration that can encourage stable rotor position estimation, thereby enhancing the overall sensorless scheme. Moreover, experimental results show how operation through, and from, zero speed, is readily obtainable. Experimental results are also employed to demonstrate the attributes of each methodology, and provide dynamic and computational performance comparisons

GA-based tuning of nonlinear observers for sensorless control of IPMSMs

The paper considers two observer-based rotor position estimation schemes for sensorless control of interior permanent magnet synchronous machines (IPMSMs). Emphasis is given to techniques based on feedback linearisation followed by Luenberger observer design, and direct design of nonlinear observers. Genetic algorithms (GAs) based on the principles of evolution, natural selection and genetic mutation are employed to address difficulties in selecting correction gains for the observers, since no analytical tuning mechanisms yet exist, with results included to demonstrate the enhanced performance attributes offered by observers tuned in this way

Observer-based tuning of two-inertia servo-drive systems with integrated SAW torque transducers

This paper proposes controller design and tuning methodologies that facilitate the rejection of periodic load-side disturbances applied to a torsional mechanical system while simultaneously compensating for the observer’s inherent phase delay. This facilitates the use of lower-bandwidth practically realizable disturbance observers. The merits of implementing full- and reduced-order observers are investigated, with the latter being implemented with a new low-cost servo-machine-integrated highband width torque-sensing device based on surface acoustic wave (SAW) technology. Specifically, the authors’ previous work based on proportional–integral–derivative (PID) and resonance ratio control (RRC) controllers (IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1226–1237, Aug. 2006) is augmented with observer disturbance feedback. It is shown that higher-bandwidth disturbance observers are required to maximize disturbance attenuation over the low-frequency band (as well as the desired rejection frequency), thereby attenuating a wide range of possible frequencies. In such cases, therefore, it is shown that the RRC controller is the preferred solution since it can employ significantly higher observer bandwidth, when compared to PID counterparts, by virtue of reduced noise sensitivity. Furthermore, it is demonstrated that the prototype servo-machine-integrated 20-N · mSAWtorque transducer is not unduly affected by machine-generated electromagnetic noise and exhibits similar dynamic behavior as a conventional instrument inline torque transducer

Applied Sensor Fault Detection, Identification and Data Reconstruction

Sensor fault detection and identification (SFD/I) has attracted considerable attention in military applications, especially when safety- or mission-critical issues are of paramount importance. Here, two readily implementable approaches for SFD/I are proposed through hierarchical clustering and self-organizing map neural networks. The proposed methodologies are capable of detecting sensor faults from a large group of sensors measuring different physical quantities and achieve SFD/I in a single stage. Furthermore, it is possible to reconstruct the measurements expected from the faulted sensor and thereby facilitate improved unit availability. The efficacy of the proposed approaches is demonstrated through the use of measurements from experimental trials on a gas turbine. Ultimately, the underlying principles are readily transferable to other complex industrial and military systems

Single-walled carbon nanotube modelling based on Cosserat surface theory

This paper studies the mechanical properties of single-walled carbon nanotubes (SWCNTs). In order to overcome the difficulty of spanning multi-scales from atomistic field to macroscopic space, the Cauchy-Born rule is applied to link the deformation of atom lattice vectors at the atomic level with the material deformation at a macro continuum level. SWCNTs are modelled as Cosserat surfaces, and the modified shell theory is adopted where a displacement field-independent rotation tensor is introduced, which describes the rotation of the inner structure of the surface, i.e. the micro-rotation. Empirical interatomic potentials are employed, so that the force and modulus fields can be computed by the derivations of potential forms from the displacement and rotation fields. A finite element approach is implemented. The Young’s modulus is predicted for SWCNTs in the paper

Fault detection and diagnosis based on extensions of PCA

The paper presents two approaches for fault detection and discrimination based on principal component analysis (PCA). The first approach proposes the concept of y-indices through a transposed formulation of the data matrices utilized in traditional PCA. Residual errors (REs) and faulty sensor identification indices (FSIIs) are introduced in the second approach, where REs are generated from the residual sub-space of PCA, and FSIIs are introduced to classify sensor- or component-faults. Through field data from gas turbines during commissioning, it is shown that in-operation sensor faults can be detected, and sensor- and component-faults can be discriminated through the proposed methods. The techniques are generic, and will find use in many military systems with complex, safety critical control and sensor arrangements

Hybrid energy sources for electric and fuel cell vehicle propulsion

Given the energy (and hence range) and performance limitations of electro-chemical batteries, hybrid systems combining energy and power dense storage technologies have been proposed for electric vehicle propulsion. The paper will discuss the application of electro-chemical batteries, supercapacitors and fuel cells in single and hybrid source configurations for electric vehicle drive-train applications. Simulation models of energy sources are presented and used to investigate the design optimisation of electric vehicle on-board energy source in terms of energy efficiency and storage mass/volume. Results from a case study considering a typical small urban electric vehicle are presented, illustrating the benefits of hybrid energy sources in terms of system mass and vehicle range. The models and approach can be applied to other vehicles and driving regimes

Power/energy storage technologies and energy management

Power/Energy Storage Technologies and Energy Managemen

Predictive control for energy management in all/more electric vehicles with multiple energy storage units

The paper describes the application of Model Predictive Control (MPC) methodologies for application to electric and hybrid-electric vehicle drive-train formats incorporating multiple energy/power sources. Particular emphasis is given to the co-ordinated management of energy flow from the multiple sources to address issues of extended vehicle range and battery life-time for all-electric drive-trains, and emissions reduction and drive-train torsional oscillations, for hybrid-electric counterparts, whilst accommodating operational constraints and, ultimately, generic non-standard driving cycles