Comparison between Kalman filter and Robust filter for vehicle handling dynamics state estimation

This paper explores design methods for a vehicle handling dynamics state estimator based on a linear vehicle model. The state estimator is needed because there are some states of the vehicle that cannot be measured directly, such as sideslip velocity, and also some which are relatively expensive to measure, such as roll and yaw rates. Information about the vehicle states is essential for vehicle handling stability control and is also valuable in chassis design evaluation. The aim of this study is to compare the performance of a Kalman filter with that of a robust filter, under conditions which would be realistic and viable for a production vehicle. Both filters are thus designed and tested with reference to a higher order source model which incorporates nonlinear saturating tyre force characteristics. Also, both filters rely solely on accelerometer sensors, which are simulated with expected noise characteristics in terms of amplitude and spectra. As is widely known, the Kalman filter is a stochastic filter whose design depends on the nominal vehicle model and statistical information of process and measurement noises. By contrast, the robust filter is deterministic, formulated in terms of model parameter uncertainties and the expected gain of process and measurement noises. The objective of both filter designs is to minimise the variance of the estimation error. Both filters are designed to compensate the vehicle model non-linearities, parameter uncertainties and other modeling errors, which are represented in terms of process and measurement noise covariances in Kalman filter design and in terms of additive model uncertainties in robust filter design. The study shows that the robust filter offers higher performance potential. The work concludes with a discussion on the practical realisation of each method, and gives recommendations for further research into a single design methodology which combines the benefits of both approaches

Prototyping a new car semi-active suspension by variational feedback controller

New suspension systems electronically controlled are presented and mounted on board of a real car. The system consists of variable semi-active magneto-rheological dampers that are controlled through an electronic unit that is designed on the basis of a new optimal theoretical control, named VFC-Variational Feedback Controller. The system has been mounted on board of a BMW Series 1 car, and a set of experimental tests have been conducted in real driving conditions. The VFC reveals, because of its design strategy, to be able to enhance simultaneously both the comfort performance as well as the handling capability of the car. Preliminary comparisons with several industrially control methods adopted in the automotive field, among them skyhook and groundhook, show excellent results

Practical aspects of modeling aircraft dynamics from flight data

The purpose of parameter estimation, a subset of system identification, is to estimate the coefficients (such as stability and control derivatives) of the aircraft differential equations of motion from sampled measured dynamic responses. In the past, the primary reason for estimating stability and control derivatives from flight tests was to make comparisons with wind tunnel estimates. As aircraft became more complex, and as flight envelopes were expanded to include flight regimes that were not well understood, new requirements for the derivative estimates evolved. For many years, the flight determined derivatives were used in simulations to aid in flight planning and in pilot training. The simulations were particularly important in research flight test programs in which an envelope expansion into new flight regimes was required. Parameter estimation techniques for estimating stability and control derivatives from flight data became more sophisticated to support the flight test programs. As knowledge of these new flight regimes increased, more complex aircraft were flown. Much of this increased complexity was in sophisticated flight control systems. The design and refinement of the control system required higher fidelity simulations than were previously required