Linear MIMO model identification using an extended Kalman filter

Linear Multi-Input Multi-Output (MIMO) dynamic models can be identified, with no a priori knowledge of model structure or order, using a new Generalised Identifying Filter (GIF). Based on an Extended Kalman Filter, the new filter identifies the model iteratively, in a continuous modal canonical form, using only input and output time histories. The filter’s self-propagating state error covariance matrix allows easy determination of convergence and conditioning, and by progressively increasing model order, the best fitting reduced-order model can be identified. The method is shown to be resistant to noise and can easily be extended to identification of smoothly nonlinear systems

Extending the Kalman filter for structured identification of linear and nonlinear systems

This paper considers a novel approach to system identification which allows accurate models to be created for both linear and nonlinear multi-input / output systems. In addition to conventional system identification applications the method can also be used as a black-box tool for model order reduction. A nonlinear Kalman filter is extended to include slow-varying parameter states in a canonical model structure. Interestingly, in spite of all model parameters being unknown at the start, the filter is able to evolve parameter estimates to achieve 100% accuracy in noise-free test cases, and is also proven to be robust to noise in the measurements. The canonical structure ensures a well-conditioned model which simultaneously provides valuable dynamic information to the engineer. After extensive testing of a linear example, the model structure is extended to a generalised nonlinear form, which is shown to accurately identify the handling response of a full vehicle model

Development of a dynamic model of a ducted fan VTOL UAV

The technology of UAV (Unmanned Aerial Vehicle) has developed since its conception many years ago. UAVs have several features such as, computerised and autonomous control without the need for an on-board pilot. Therefore, there is no risk of loss of life and they are easier to maintain than manned aircraft. In addition, UAVs have an extended range/endurance capability, sometimes for several days. This makes UAVs attractive for missions that are typically "dull, dirty and dangerous". With the development of technology, the application of UAVs is becoming commonplace for both military and civil missions. Examples of this are reconnaissance, surveillance, environmental monitoring, disaster observation, etc. The School of Aerospace, Mechanical and Manufacturing Engineering (SAMME) at RMIT University has designed a novel concept for a ducted-fan UAV with vertical takeoff and landing capability and the option to transition to horizontal flight. The aerodynamic analysis, preliminary and detailed design, of this ducted-fan VTOL UAV, is the first and most important step. To optimize the aerodynamic characteristics, evaluating aerodynamic coefficients and analyzing the flow patterns around the vehicle at different speeds and angles of attack is necessary. In this project, CFD plays an important role in predicting the longitudinal and lateral stability and control characteristics of a full-scale model of ducted fan VTOL UAV at both vertical and horizontal flight without any prior knowledge of existing wind tunnel or flight test data. Prior to carrying out experiments in the wind tunnel, the manufacture of ducted fan VTOL UAV was focused on. Particular attention was paid to the propulsion system as the key point. The full-scale model of UAV was produced using the Rapid Prototyping Facility at SAMME to ensure its accurate geometric shape for testing in the wind tunnel. The experiments of the full-scale UAV model with engines was conducted in RMIT's Industrial Wind Tunnel where its aerodynamic characteristics and its properties of counter-rotating propulsion system were tested. In addition, the correlation between experimental data and CFD results was evaluated and the accuracy of the dynamic model of ducted fan VTOL UAV was improved. Flight dynamics is concerned with the motion of an aircraft due to internally or externally generated forces. The ducted fan VTOL UAV stability and control derivatives are determined and used as a basis in a flight simulation environment. This simulation showed that the vehicle is stable and controllable for a range of flight speeds. Finally, a MIMO linear control system was designed to control the vehicle in hovering and low-speed slide flight. The real-time simulation and modeling in MATLAB combined with a flight-simulator showed several animations and trajectories of UAV missions with or without crosswind effect during flight. These simulations were very helpful in determining the dynamic behaviour of the vehicle under various flight conditions

Reduced order modelling through system identification using stochastic filtering

This thesis presents a novel approach to model order reduction, through system identification and using stochastic filtering. Order reduction is a particularly relevant application in the automotive context, as the generation of simplified simulation models for the whole vehicle and its subsystems is an increasingly important aspect of vehicle design. First, grey-box parameter identification of vehicle handling dynamics is explored, including identification of a combined-slip tyre model. This introductory study serves as an intermediate step to review three alternative stochastic filters: identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are here compared for effectiveness, complexity and computational efficiency. Despite being initially merely considered as a stepping stone towards black-box identification, this phase of the PhD generated its own and independent outcomes and might be viewed as a spin-off of the main research topic. All three filters appear suited to system identification and could operate in on-line model predictive controllers or estimators, with varying levels of practicability at different sampling rates. Work on black-box system identification then starts through a non-linear Kalman filter, extended to identify all the parameters of a canonical linear state-space structure. In spite of all model parameters being unknown at the start, the filter is able to evolve parameter estimates to achieve 100$\%$ accuracy in noise-free test cases, and is also proven to be robust to noise in the measurements. The canonical form ensures that a minimal number of parameters need to be identified and produces additional information in terms of eigenvalues and dominant modes. After extensive testing in the linear domain, state-space is extended into a non-linear framework, with each parameter becoming a non-linear function of system inputs or states. Parameter variation is first constrained by cubic spline polynomials, to provide continuity and maintain relatively small extended state-parameter vectors. This early approach is later simplified, with each element of state-space generated through unconstrained, generic non-linear functions and defined through a number of equally spaced, fixed nodes. Conditioning and convergence are maintained through the definition of additional system outputs, based on specific functions of the non-linear node ordinates. Unlike other methods published in the literature, this new approach does not focus on a specific non-linear structure, but consists in the prescription of a generic and yet simple non-linear state-space model structure, that allows various non-linearities to be identified and approximated solely based on inputs and outputs. The method is illustrated in practice through simple non-linear examples and test cases, which include the identification of a full vehicle model, a highly non-linear brake model and CFD data. These applications show that it is possible to easily expand the order of the system and the complexity of the non-linearities, to achieve higher accuracy while ensuring good parameter conditioning. The approach is completely black-box and requires no physical understanding of the process for successful identification, making it an ideally suited mechanism for order reduction of high order simulation models. In addition to high order simulation data, the developed approach can be used as a tool for conventional system identification and applied to experimental test data as well.</div