A NOVEL PREDICTIVE FIR FILTER SCHEME FOR SAFETY CRITICAL COMPONENTS IN DRIVE-BY-WIRE SYSTEM

A PREDICTIVE FIR FILTER SCHEME FOR missing data compensation IN DRIVE-BY-WIRE SYSTEMBy Biswajit DebnathM140201EEKey TermsDrive by wire(DbW) technology in the automotive industry is the use of electrical or electro-mechanical systems for performing vehicle functions traditionally achieved by mechanical linkages. Main DriveByWire systems are brake-by-wire, throttle-by-wire and steer-by-wire.Missing data- loss of safety critical data generated from pedal, accelerator or steering sensors.

Credits Reza Hoseinnezhad and Alireza Bab-Hadiashar of Swinburne University of Technology, Hawthorn, Australia.

Research work funded by Research Centre for Advanced By-Wire Technology(RABiT).

Published in IEEE Transactions on Vehicular Technology, 2013.overviewThe Presentation discusses the modelling of a new multistep ahead predictive FIR filter scheme that accounts for data loss in safety critical components in a drive-by-wire car. The constraints on computational time and memory render the scheme an advantage as it requires only one set of finite impulse response(FIR) filter weights to be tuned while it can be used for different numbers of steps ahead predictions.Management system of dbw systemProcessors, including an electronic control unit (ECU) and other local processors.Memory (mainly integrated into the ECU).Sensors .Actuators.Communication Network.

Effects of critical data lossSteering sensor data loss improper turns.

Braking pedal sensor data loss improper braking or brake failure.

Throttle sensor data loss over speeding.

Wheel speed sensor data loss improper Anti-Lock Braking Action leading into skidding.

Causes of data loss and mitigation Permanent failure of sensor or communication link redundancy and fail safe mechanism.

Temporary failure due to sensor , link or due to noise predictive filters.

Predictive filtering methodsHeinonen et al. applied FIR predictive filters combined with median filters to measure systolic blood pressure. Harju et al. introduced a method to design predictive IIR filters via feedback extension of FIR predictive filters.Handel proposed a multistep ahead predictive filter for narrow-band waveforms with multiple distinct spectral peaks.Crassidis et al. used the least mean square method to tune the parameters of a nonlinear predictive filter for attitude estimation.Ferrah et al. applied an adaptive nonlinear predictive filter as a dynamic speed estimator for induction motor drivesANN and Fuzzy logic based predictive FIR filters have also been designed for some Model Predictive Control(MPC) applications.Filter design Constraints for automobile applications Should require SMALL computation time for REAL time implementation. Should use small dynamic memory(DRAMs) so as to achieve competitive cost. Algorithm should be STABLE.Proposed filter has the following characteristic It uses only ONE set of weights for DIFFERENT steps ahead in prediction. Hence, comparatively LESS memory is required to save filter coefficients and data samples. Also multiple nos. of predictive filters are NOT required for different numbers of missing data samples thus requiring LESSER time for processing.Linear prediction filter equationsblock diagram

Filter weight tuning algorithmMethod of steepest descent

Method of steepest descentcontd.

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Operating methodologyExperimental setup for comparative analysis

Brake Pedal and sensors at test rig Initial Considerations- Brake by wire system considered. Test rig comprised a pedal with a displacement and two force sensors. Filter weights tuned for all pedal movements such as standstill pedal, frequent soft pushes, frequent hard pushes, and sudden continuous hard brake. Proposed filter is compared in terms of Mean Square Error(MSE) and Average Execution Time with the following filters First-order hold filter. Newton Polynomial Predictive filter. Linear Smoothed Newton Predictive(LSN) filter. Neural Predictive filter.

Reported experimental results

ConclusionThe results confirm that the proposed FIR multistep ahead predictive filter estimates missing data samples as accurately as the sophisticated neural predictive filter, and more accurately than other methods, with a computational overhead that is lower than other filters. In contrast to commonly used FIR filters, the proposed filter only requires one set of weights to be tuned, memorized, and applied to different numbers of steps ahead prediction. Hence, the proposed predictive filtering technique is an ideal tool for missing data compensation in drive by-wire systems as well as any other time- and memory-critical application.References1. Hoseinnezhad, R.; Bab-Hadiashar, A., "Missing Data Compensation for Safety-Critical Components in a Drive-by-Wire System,"Vehicular Technology, IEEE Transactions on, vol.54, no.4, pp.1304,1311, July 2013.2. P. Handel, "Predictive digital filtering of sinusoidal signals",IEEE Trans. Signal Process., vol. 46, no. 2, pp.364 -374, Sept 2006.3. M. Lubaszewski and B. Courtois, A reliable fail-safe system, IEEE Trans. Comput., vol. 47, no. 2, pp. 236241, 1998.