Laura R. Ray

Nonlinear tire force estimation and road friction identification: simulation and experiments

Abstract This paper applies extended Kalman-Bucy filtering (EKBF) and Bayesian hypothesis selection to estimate motion, tire forces, and road coefficient of friction (μ) of vehicles on asphalt surfaces. The EKBF estimates the state and tire forces of an eight-degree-of-freedom vehicle from vehicle-mounted sensors. The filter requires no a priori knowledge of μ and does not require a tire force model. Resulting force, slip, and slip angle estimates are compared statistically with those that result from a nominal analytic tire model to select the most likely μ from a set of hypothesized values. The methods have application to both off-line construction of tire models and development of vehicle control systems that require μ. Both simulation results and results of applying estimation methods to field test data are presented. Simulation results show excellent convergence and accuracy of μ estimates, and results of processing field test data demonstrate the ability to construct useful tire models. Computation and sensor requirements, and robustness of the μ identification algorithm are considered.

Adaptive friction compensation using extended Kalman–Bucy filter friction estimation

Abstract An extended Kalman–Bucy filter (EKBF)-based friction compensation method is presented and validated. The method relies on an accurate model of system rigid-body dynamics and measured motion, rather than a structured nonlinear friction model, to estimate external friction torque. The estimate is used in a traditional friction compensator to cancel friction effects. The EKBF compensator is compared with other model-based and non-model-based friction compensation strategies through position tracking experiments. Results show that when motion is dominated by static and Stribeck friction, non-model-based friction estimation and compensation using the EKBF consistently provides equal or superior performance over model-based adaptive friction compensation methods.

Hybrid feedforward-feedback active noise reduction for hearing protection and communication.

A hybrid active noise reduction (ANR) architecture is presented and validated for a circumaural earcup and a communication earplug. The hybrid system combines source-independent feedback ANR with a Lyapunov-tuned leaky LMS filter (LyLMS) improving gain stability margins over feedforward ANR alone. In flat plate testing, the earcup demonstrates an overall C-weighted total noise reduction of 40 dB and 30-32 dB, respectively, for 50-800 Hz sum-of-tones noise and for aircraft or helicopter cockpit noise, improving low frequency (<100 Hz) performance by up to 15 dB over either control component acting individually. For the earplug, a filtered-X implementation of the LyLMS accommodates its nonconstant cancellation path gain. A fast time-domain identification method provides a high-fidelity, computationally efficient, infinite impulse response cancellation path model, which is used for both the filtered-X implementation and communication feedthrough. Insertion loss measurements made with a manikin show overall C-weighted total noise reduction provided by the ANR earplug of 46-48 dB for sum-of-tones 80-2000 Hz and 40-41 dB from 63 to 3000 Hz for UH-60 helicopter noise, with negligible degradation in attenuation during speech communication. For both hearing protectors, a stability metric improves by a factor of 2 to several orders of magnitude through hybrid ANR.

All-wheel driving using independent torque control of each wheel

Electric motors offer tremendous potential to create an advanced all-wheel drive system. By driving each wheel with a separate motor, the torque delivered to individual wheels can be precisely controlled. This paper reports control algorithms, simulation results, and initial results of scale model testing of an all-wheel drive system. The control algorithms seek to keep all four wheel speeds within a certain percentage of one another to limit slip, while a torque distribution system sets the torque command for each wheel based on its loading and the need to produce a yaw moment. Torque control algorithms are evaluated using an eight degree-of-freedom vehicle model. A unique road surface simulator is developed to evaluate controller performance on surfaces with realistic transitions between high and low friction patches. A microprocessor-controlled physical model based on a one-eighth scale radio-controlled car driven by four brushless DC motors is constructed as an experimental test platform. The resulting braking, traction, and anti-skid controllers result in a better-handling vehicle with improved safety and performance.

Experimental determination of tire forces and road friction

This paper presents experimental results of using extended Kalman-Bucy filtering (EKBF) and Bayesian model selection to extract tire force characteristics and road friction coefficient from measured motion of ground vehicles on smooth surfaces. The filter estimates wheel slip and slip angles, along with longitudinal and per axle lateral tire forces. Force estimates are based on vehicle-mounted sensors and are derived without knowing road conditions and without a tire force model. Force estimates are compared statistically with those that result from a nominal tire model to select the most likely friction coefficient from hypothesized values. This paper verifies tire force estimation and road friction identification using off-line processing of field test data. Results confirm applicability of the EKBF and Bayesian selection approaches.

Damage detection and vibration control in smart plates: Towards multifunctional smart structures

This paper demonstrates the concept of sensitivity enhancing control (SEC) to aid in damage detection in smart structures through both experimental and simulation evaluation. Methods of implementing state estimate feedback using point measurements of strain along the structure are described, and an initial proof-of-concept laboratory experiment demonstrating enhancement of modal frequency shifts due to tip mass damage in a cantilevered beam is reported. Simulation results focus on applying state feedback control to finite-element models of a cantilevered structure with slot, through-surface crack, or surface crack damage. The simulation analysis ascertains the ability to enhance sensitivity of modal frequency shifts due to realistic damage cases that are difficult to evaluate experimentally. The simulation also ascertains the potential for using the same sensors and actuators for implementing both sensitivity enhancing control laws and vibration damping control laws that are insensitive to damage. In the control model with which SEC laws are designed, damage consists of simple reductions in thickness over a small area of the structure. Finite-element models to which control laws are applied are developed using commercial software (ABAQUS‘) that more accurately models stiffness damage by releasing element connections or by using line spring elements to model fatigue cracks. Experimental results show that enhancement in sensitivity of modal frequencies to damage can be achieved using a single piezoceramic actuator and multiple piezoelectric strain sensors along the beam. Simulation results indicate that feedback control laws can be designed for either sensitivity enhancement or vibration suppression using identical hardware, providing for multifunctional smart structures. In addition, analysis demonstrates that commercial finite-element software is useful for model-based simulation of damaged controlled structures.

Optimal filtering and Bayesian detection for friction-based diagnostics in machines.

Non-model-based diagnostic methods typically rely on measured signals that must be empirically related to process behavior or incipient faults. The difficulty in interpreting a signal that is indirectly related to the fundamental process behavior is significant. This paper presents an integrated non-model and model-based approach to detecting when process behavior varies from a proposed model. The method, which is based on nonlinear filtering combined with maximum likelihood hypothesis testing, is applicable to dynamic systems whose constitutive model is well known, and whose process inputs are poorly known. Here, the method is applied to friction estimation and diagnosis during motion control in a rotating machine. A nonlinear observer estimates friction torque in a machine from shaft angular position measurements and the known input voltage to the motor. The resulting friction torque estimate can be analyzed directly for statistical abnormalities, or it can be directly compared to friction torque outputs of an applicable friction process model in order to diagnose faults or model variations. Nonlinear estimation of friction torque provides a variable on which to apply diagnostic methods that is directly related to model variations or faults. The method is evaluated experimentally by its ability to detect normal load variations in a closed-loop controlled motor driven inertia with bearing friction and an artificially-induced external line contact. Results show an ability to detect statistically significant changes in friction characteristics induced by normal load variations over a wide range of underlying friction behaviors.

Comparison of EKBF-based and Classical Friction Compensation

In servo control, traditionally, models that attempt to capture the friction-velocity curve and interactions at contacting surfaces have been used to compensate for friction-introduced tracking errors. Recently, however, extended Kalman-Bucy filter (EKBF)-based approaches that do not use a phenomenological or structured model for friction have been proposed. In addition to being cast as a friction estimator, the EKBF can also be used to provide parameter adaptation for simple friction models. In this paper, a traditional motor-driven inertia experiment is used to demonstrate the usefulness of EKBF in friction compensation. In addition, a numerical simulation is used to test the robustness of the new methods to normal force variations. Using root mean square position tracking error as the performance metric, comparisons to traditional model-based approaches are provided.

Damage Identification Using Sensitivity-Enhancing Control and Identified Models

This paper investigates a coherence approach for locating structural damage using modal frequencies and transfer function parameters identified from input-output data using Observer/Kalman filter identification (OKID). Autonomous damage identification using such forward methods generally require (i) a structural model by which to relate measured and predicted modal properties induced by damage, and (ii) good sensitivity of modal parameter changes to damage states. Using the coherence approach, a damage parameter vector comprised of a finite set of modal frequencies and transfer function parameters is hypothesized for each damage case using either identified or analytic structural models. Measured parameter vectors are extracted from experimental input-output data for a damaged structure using OKID and are compared to hypotheses to determine the most likely damage state. The richness of the parameter vector set, which is comprised of high-quality frequency measurements and lower-quality transfer function parameters, is evaluated in order to determine the ability to uniquely localize damage. The method is evaluated experimentally using a three-degree-of-freedom torsional system and a space-frame truss. Damage parameter hypotheses are generated from a model of the healthy structure developed by system identification in the torsional system, and an analytic model is used to generate damage hypotheses for the truss structure. Feedback control laws enhance the parameter vectors by including closed-loop modal frequencies in order to reduce noise sensitivity and improve uniqueness of parameter vector hypotheses to each damage case. Results show improvements in damage identification using damage parameter vectors comprised of open- and closed-loop modal frequencies, even when model error exists in structural models used to form damage parameter vector hypotheses.

Optimization of control laws for damage detection in smart structures

A prevalent method of damage detection is based on identifying changes in modal characteristics due to damage induced variations in stiffness or mass along a structure. It is known that modal frequencies can be insensitive to damage, and the open-loop sensitivity itself depends on modal properties and damage location. Here, we develop methods of designing control laws that enhance the sensitivity of modal characteristics to damage. Sensitivity enhancing control exploits the relationship between control gains and closed-loop dynamics in order to increase the observability of damage. The design methods are based on optimization of cost functions that involve the dependence of classic measures of sensitivity on design variables, which include placement of sensors and actuators and state feedback control gains. Due to the size of the design space and the unknown nature of the cost surface, genetic algorithms are used to find control laws that maximize sensitivity to specific damage types subject to control effort and stability constraints. Optimized control laws designed for sensitivity enhancement of stiffness damage in a cantilevered beam are demonstrated by numerical simulation.