Raymond A. de Callafon

Identification of Normalised Coprime Plant Factors from Closed-loop Experimental Data

Recently introduced methods of iterative identification and control design are directed towards the design of high performing and robust control systems. These methods show the necessity of identifying approximate models from closed loop plant experiments. In this paper a method is proposed to approximately identify normalized coprime plant factors from closed loop data. The fact that normalized plant factors are estimated gives specific advantages both from an identification and from a robust control design point of view. It will be shown that the proposed method leads to identified models that are specifically accurate around the bandwidth of the closed loop system. The identification procedure fits very naturally into a recently developed the iterative identification/control design scheme based on H∞ robustness optimization.

Damage Identification of a Composite Beam Using Finite Element Model Updating

The damage identification study presented in this paper leveraged a full-scale sub-component experiment conducted in the Charles Lee Powell Structural Research Laboratories at the University of California, San Diego. As payload project attached to a quasi-static test of a full-scale composite beam, a high-quality set of low-amplitude vibration response data was acquired from the beam at various damage levels. The Eigensystem Realization Algorithm was applied to identify the modal parameters (natural frequencies, damping ratios, displacement and macro-strain mode shapes) of the composite beam based on its impulse responses recorded in its undamaged and various damaged states using accelerometers and long-gage fiber Bragg grating strain sensors. These identified modal parameters are then used to identify the damage in the beam through a finite element model updating procedure. The identified damage is consistent with the observed damage in the composite beam.

Technical communique: On the asymptotic stability of minimum-variance unbiased input and state estimation

In this note, we investigate the asymptotic stability of the filter for minimum-variance unbiased input and state estimation developed by Gillijns and De Moor. Sufficient conditions for the stability are proposed and proven, with inspiration from the Kalman filter stability analysis.

Simultaneous input and state estimation for nonlinear systems with applications to flow field estimation

This paper studies the problem of simultaneous input and state estimation (SISE) for nonlinear dynamical systems with and without direct input-output feedthrough. We take a Bayesian perspective to develop a sequential joint input and state estimation approach. Our scheme gives rise to a nonlinear Maximum a Posteriori optimization problem, which we solve using the classical Gauss-Newton method. The proposed approach generalizes a number of SISE methods presented in the literature. We illustrate the effectiveness of the proposed scheme for nonlinear systems with direct feedthrough in an oceanographic flow field estimation problem involving submersible drogues that measure position intermittently and acceleration continuously.

Hammerstein system identification using nuclear norm minimization

This paper presents a new method for the identification of Hammerstein systems. The parameter estimation problem is formulated as a rank minimization problem by constraining a finite dimensional time dependency between signals. Due to the unknown intermediate signal, the rank minimization problem cannot be solved directly. Thus, the rank minimization problem is reformulated as an intermediate signal construction problem. The main assumption used in this paper is that static nonlinearity is monotonically non-decreasing in order to guarantee a unique combination of a static nonlinear block and a Finite Impulse Response (FIR) linear block. The rank minimization is then relaxed to a convex optimization problem using a nuclear norm. The main contribution of this paper is that the proposed method extends the rank minimization approach to Hammerstein system identification, and does not need a bilinear parametrization and singular value decomposition (SVD), which are commonly used in two-step approaches for Hammerstein system identification.

Subspace identification with eigenvalue constraints

Standard subspace methods for the identification of discrete-time, linear, time-invariant systems are transformed into generalized convex optimization problems in which the poles of the system estimate are constrained to lie within user-defined convex regions of the complex plane. The transformation is done by restating subspace methods such as the minimization of a Frobenius norm affine in the estimate parameters, allowing the minimization to be augmented with convex constraints. The constraints are created using linear-matrix-inequality regions, which generalize standard Lyapunov stability to arbitrary convex regions of the complex plane. The algorithm is developed for subspace methods based on estimates of the extended observability matrix and methods based on estimates of state sequences, but it is extendable to all subspace methods. Simulation examples demonstrate the utility of the proposed method.

Feedforward control for disturbance rejection: Model matching and other methods

Feedforward control can improve disturbance rejection performance of a system when the measurement of the disturbance is available. This paper discusses the design of feedforward control in the discrete-time domain using the model matching methods that compute optimal and stable feedforward controllers. It is shown that the existence of a non-zero solution to the model matching problem depends on the difference between the relative degrees of the plant dynamics and the disturbance dynamics. A number of approximate dynamic inversion techniques commonly used for feedforward control design are reviewed and compared with the model matching methods. These feedforward control design methods are then applied to the application of an example tape head track-following servo system where the feedforward controller aims at reducing the position error caused by the lateral tape motion. Simulation results are presented to demonstrate the effectiveness of the feedforward control.

Towards an Integrated Cyberinfrastructure for Scalable Data-driven Monitoring, Dynamic Prediction and Resilience of Wildfires

Abstract Wildfires are critical for ecosystems in many geographical regions. However, our current urbanized existence in these environments is inducing the ecological balance to evolve into a different dynamic leading to the biggest fires in history. Wildfire wind speeds and directions change in an instant, and first responders can only be effective if they take action as quickly as the conditions change. What is lacking in disaster management today is a system integration of real-time sensor networks, satellite imagery, near-real time data management tools, wildfire simulation tools, and connectivity to emergency command centers before, during and after a wildfire. As a first time example of such an integrated system, the WIFIRE project is building an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. This paper summarizes the approach and early results of the WIFIRE project to integrate networked observations, e.g., heterogeneous satellite data and real-time remote sensor data with computational techniques in signal processing, visualization, modeling and data assimilation to provide a scalable, technological, and educational solution to monitor weather patterns to predict a wildfires Rate of Spread.

Robust Estimation and Automatic Controller Tuning in Vibration Control of Time Varying Harmonic Disturbances

Abstract This paper presents theoretical and experimental results of a newly developed automatic controller tuning algorithm called Robust Estimation for Automatic Controller Tuning (REACT) to tune a linear feedback controller to the unknown spectrum of disturbances present in a feedback loop. With model uncertainty and controller perturbations described in (dual) Youla parametrizations, the REACT algorithm allows recursive least squares based tuning of a feedback controller in the presence of model uncertainty to minimize the variance of control performance related signal. It is shown how stability of the feedback can be maintained during adaptive regulation, while simulation and experimental results on a mechanical test bed of an active suspension system illustrate the effectiveness of the algorithm for vibration isolation of periodic disturbances with unknown and varying frequencies.

Brief paper: An alternative Kalman-Yakubovich-Popov lemma and some extensions

This paper introduces an alternative formulation of the Kalman-Yakubovich-Popov (KYP) Lemma, relating an infinite dimensional Frequency Domain Inequality (FDI) to a pair of finite dimensional Linear Matrix Inequalities (LMI). It is shown that this new formulation encompasses previous generalizations of the KYP Lemma which hold in the case the coefficient matrix of the FDI does not depend on frequency. In addition, it allows the coefficient matrix of the frequency domain inequality to vary affinely with the frequency parameter. One application of this results is illustrated in an example of computing upper bounds to the structured singular value with frequency-dependent scalings.