4,735 research outputs found
Robust observer design under measurement noise with gain adaptation and saturated estimates
We use incremental homogeneity, gain adaptation and incremental observability for proving new results on robust observer design for systems with noisy measurement and bounded trajectories. A state observer is designed by dominating the incrementally homogeneous nonlinearities of the observation error system with its linear approximation, while gain adaptation and incremental observability guarantee an asymptotic upper bound for the estimation error depending on the limsup of the norm of the measurement noise. A characteristic and innovative feature of this observer is the mixed low/high-gain structure in combination with saturated state estimates and dynamically tuned gains and saturation levels. The gain adaptation is implemented as the output of a stable filter using the squared norm of the measured output estimation error and the mismatch between each estimate and its saturated value
Robust observer design under measurement noise
We prove new results on robust observer design for systems with noisy measurement and bounded trajectories. A state observer is designed by dominating the incrementally homogeneous nonlinearities of the observation error system with its linear approximation, while gain adaptation and incremental observability guarantee an asymptotic upper bound for the estimation error depending on the limsup of the norm of the measuremen noise. The gain adaptation is implemented as the output of a stable filter using the squared norm of the measured output estimation error and the mismatch between each estimate and its saturated value
Experimental comparison of parameter estimation methods in adaptive robot control
In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications
Sequential processing and performance optimization in nonlinear state estimation
We propose a framework for designing observers for noisy nonlinear systems
with global convergence properties and performing robustness and noise sensitivity. Our state
observer is the result of the combination of a state norm estimator with a bank of Kalman-type
lters, parametrized by the state norm estimator. The state estimate is sequentially processed
through the bank of lters. In general, existing nonlinear state observers are responsible for
estimation errors which are sensitive to model uncertainties and measurement noise, depending
on the initial state conditions. Each Kalman-type lter of the bank contributes to improve the
estimation error performances to a certain degree in terms of sensitivity with respect to noise
and initial state conditions. A sequential processing algorithm for performance optimization is
given and simulations show the eectiveness of these sequential lters
An Adaptive Observer-based Robust Estimator of Multi-sinusoidal Signals
This paper presents an adaptive observer-based
robust estimation methodology of the amplitudes, frequencies
and phases of biased multi-sinusoidal signals in presence of
bounded perturbations on the measurement. The parameters of
the sinusoidal components are estimated on-line and the update
laws are individually controlled by an excitation-based switching
logic enabling the update of a parameter only when the measured
signal is sufficiently informative. This way doing, the algorithm
is able to tackle the problem of over-parametrization (i.e., when
the internal model accounts for a number of sinusoids that is
larger than the true spectral content) or temporarily fading
sinusoidal components. The stability analysis proves the existence
of a tuning parameter set for which the estimator\u2019s dynamics are
input-to-state stable with respect to bounded measurement disturbances.
The performance of the proposed estimation approach
is evaluated and compared with other existing tools by extensive
simulation trials and real-time experiments
Real-Time Vehicle Parameter Estimation and Adaptive Stability Control
This dissertation presents a novel Electronic Stability Control (ESC) strategy that is capable of adapting to changing vehicle mass, tire condition and road surface conditions. The benefits of ESC are well understood with regard to assisting drivers to maintain vehicle control during extreme handling maneuvers or when extreme road conditions such as ice are encountered. However state of the art ESC strategies rely on known and invariable vehicle parameters such as vehicle mass, yaw moment of inertia and tire cornering stiffness coefficients. Such vehicle parameters may change over time, especially in the case of heavy trucks which encounter widely varying load conditions. The objective of this research is to develop an ESC control strategy capable of identifying changes in these critical parameters and adapting the control strategy accordingly. An ESC strategy that is capable of identifying and adapting to changes in vehicle parameters is presented. The ESC system utilizes the same sensors and actuators used on commercially-available ESC systems. A nonlinear reduced-order observer is used to estimate vehicle sideslip and tire slip angles. In addition, lateral forces are estimated providing a real-time estimate of lateral force capability of the tires with respect to slip angle. A recursive least squares estimation algorithm is used to automatically identify tire cornering stiffness coefficients, which in turn provides a real-time indication of axle lateral force saturation and estimation of road/tire coefficient of friction. In addition, the recursive least squares estimation is shown to identify changes in yaw moment of inertia that may occur due to changes in vehicle loading conditions. An algorithm calculates the reduction in yaw moment due to axle saturation and determines an equivalent moment to be generated by differential braking on the opposite axle. A second algorithm uses the slip angle estimates and vehicle states to predict a Time to Saturation (TTS) value of the rear axle and takes appropriate action to prevent vehicle loss of control. Simulation results using a high fidelity vehicle modeled in CarSim show that the ESC strategy provides improved vehicle performance with regard to handling stability and is capable of adapting to the identified changes in vehicle parameters
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