Showing papers on "System identification published in 2009"
19 Apr 2009
TL;DR: A new approach to adaptive system identification when the system model is sparse is proposed, which results in a zero-attracting LMS and a reweighted zero attractor, and it is proved that the ZA-LMS can achieve lower mean square error than the standard LMS.
Abstract: We propose a new approach to adaptive system identification when the system model is sparse. The approach applies l 1 relaxation, common in compressive sensing, to improve the performance of LMS-type adaptive methods. This results in two new algorithms, the zero-attracting LMS (ZA-LMS) and the reweighted zero-attracting LMS (RZA-LMS). The ZA-LMS is derived via combining a l 1 norm penalty on the coefficients into the quadratic LMS cost function, which generates a zero attractor in the LMS iteration. The zero attractor promotes sparsity in taps during the filtering process, and therefore accelerates convergence when identifying sparse systems. We prove that the ZA-LMS can achieve lower mean square error than the standard LMS. To further improve the filtering performance, the RZA-LMS is developed using a reweighted zero attractor. The performance of the RZA-LMS is superior to that of the ZA-LMS numerically. Experiments demonstrate the advantages of the proposed filters in both convergence rate and steady-state behavior under sparsity assumptions on the true coefficient vector. The RZA-LMS is also shown to be robust when the number of non-zero taps increases.
681 citations
TL;DR: In this paper, a tutorial on cyclostationarity oriented towards mechanical applications is presented, with 20 examples devoted to illustrating key concepts on actual mechanical signals and demonstrating how cyclostatarity can be taken advantage of in machine diagnostics, identification of mechanical systems and separation of mechanical sources.
519 citations
TL;DR: In this article, a flexure-based, piezoelectric stack-actuated XY nanopositioning stage was designed to combine the ability to scan over a relatively large range (25times25 mum) with high scanning speed.
Abstract: The design, identification, and control of a novel, flexure-based, piezoelectric stack-actuated XY nanopositioning stage are presented in this paper. The main goal of the design is to combine the ability to scan over a relatively large range (25times25 mum) with high scanning speed. Consequently, the stage is designed to have its first dominant mode at 2.7 kHz. Cross-coupling between the two axes is kept to -35 dB, low enough to utilize single-input--single-output control strategies for tracking. Finite-element analysis (FEA) is used during the design process to analyze the mechanical resonance frequencies, travel range, and cross-coupling between the X- and Y-axes of the stage. Nonlinearities such as hysteresis are present in such stages. These effects, which exist due to the use of piezoelectric stacks for actuation, are minimized using charge actuation. The integral resonant control method is applied in conjunction with feedforward inversion technique to achieve high-speed and accurate scanning performances, up to 400 Hz.
347 citations
TL;DR: In this article, a general approximation approach on l 0 norm, a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm, which is equivalent to add a zero attractor in the iterations, by which the convergence rate of small coefficients, that dominate the sparse system, can be effectively improved.
Abstract: In order to improve the performance of least mean square (LMS) based system identification of sparse systems, a new adaptive algorithm is proposed which utilizes the sparsity property of such systems. A general approximating approach on l 0 norm-a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm. This integration is equivalent to add a zero attractor in the iterations, by which the convergence rate of small coefficients, that dominate the sparse system, can be effectively improved. Moreover, using partial updating method, the computational complexity is reduced. The simulations demonstrate that the proposed algorithm can effectively improve the performance of LMS-based identification algorithms on sparse system.
343 citations
TL;DR: In this paper, the authors used the covariance driven stochastic subspace identification method (SSI-COV) and a hierarchical clustering algorithm for the identification of the bridge first 12 modes.
338 citations
TL;DR: In this paper, a unique dynamic disturbance decoupling control (DDC) strategy, based on the active disturbance rejection control (ADRC) framework, is proposed for square multivariable systems.
270 citations
TL;DR: A factorization is introduced which makes it possible to form a predictor that predicts the output, which is based on past inputs, outputs, and scheduling data, and contains the LPV equivalent of the Markov parameters.
250 citations
TL;DR: This paper initiates a novel approach for simultaneously identifying the topological structure and unknown parameters of uncertain general complex networks with time delay and is effective for uncertain delayed complex dynamical networks with different node dynamics.
237 citations
TL;DR: This technical note introduces a provably stable state-feedback design modification for combined/composite adaptive control of multi-input multi-output dynamical systems with matched uncertainties.
Abstract: This technical note introduces a provably stable state-feedback design modification for combined/composite adaptive control of multi-input multi-output dynamical systems with matched uncertainties. The proposed design methodology is applied to control longitudinal dynamics of an aerial vehicle.
234 citations
TL;DR: A novel adaptive neural controller is obtained by constructing a novel quadratic-type Lyapunov-Krasovskii functional, which not only efficiently avoids the controller singularity, but also relaxes the restriction on unknown virtual control coefficients.
234 citations
TL;DR: In order to reduce computational burden and improve the convergence rate of identification algorithms, an auxiliary model based multi-innovation stochastic gradient (AM-MISG) algorithm is derived for the multiple-input single-output systems by means of the auxiliary model identification idea and multi- innovation identification theory.
TL;DR: A method is proposed for the adaptive model predictive control of constrained nonlinear system that explicitly account for the transient effect of parametric estimation error by combining a parameter adjustment mechanism with robust MPC algorithms.
TL;DR: It is shown that the closed-loop dynamics of the proposed multi-agent system can be transformed into a form of a stochastic approximation algorithm and prove its convergence using Ljung's ordinary differential equation approach.
TL;DR: In this paper, the problem of fast active fault-tolerant control using adaptive fault diagnosis observer (AFDO) is studied using a fast adaptive fault estimation (FAFE) algorithm.
Abstract: The problem of fast active fault-tolerant control is studied using adaptive fault diagnosis observer (AFDO). Existence conditions for linear time-invariant system are first introduced to verify whether or not the adaptive observer for fault diagnosis exists. Then a novel fast adaptive fault estimation (FAFE) algorithm is proposed to enhance the performance of fault estimation. Using the on-line obtained fault information, the observer-based fault tolerant controller based on the separation property is designed to compensate for the loss of actuator effectiveness by stabilising the closed-loop system. Furthermore, an extension to a class of nonlinear systems is extensively investigated. Finally, simulation results are presented to illustrate the efficiency of the proposed techniques.
TL;DR: A design scheme for the observer-based output feedback controller is proposed to render the closed-loop networked system exponentially mean-square stable with H"~ performance requirement.
TL;DR: A general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models to cover: nonlinear evolution and observation functions, unknown parameters and (precision) hyperparameters and model comparison and prediction under uncertainty is described.
TL;DR: It is shown that under certain conditions, the robustness properties of the continuous-time design are inherited by the sampled-data design, as long as the sampling period is not too large.
Abstract: In this work, a sampled-data nonlinear observer is designed using a continuous-time design coupled with an inter-sample output predictor. The proposed sampled-data observer is a hybrid system. It is shown that under certain conditions, the robustness properties of the continuous-time design are inherited by the sampled-data design, as long as the sampling period is not too large. The approach is applied to linear systems and to triangular globally Lipschitz systems.
TL;DR: In this paper, a rapid calibration procedure for identifying the parameters of a dynamic model of batteries for use in automotive applications is described, which is a phenomenological model based on an equivalent circuit model with varying parameters that are linear spline functions of the SoC.
TL;DR: The insight obtained from the numerical solution of this problem is derived and design guidelines for nonlinear MPC schemes which guarantee stability of the closed loop for small optimization horizons are derived.
Abstract: We present a technique for computing stability and performance bounds for unconstrained nonlinear model predictive control (MPC) schemes. The technique relies on controllability properties of the system under consideration, and the computation can be formulated as an optimization problem whose complexity is independent of the state space dimension. Based on the insight obtained from the numerical solution of this problem, we derive design guidelines for nonlinear MPC schemes which guarantee stability of the closed loop for small optimization horizons. These guidelines are illustrated by a finite and an infinite dimensional example.
TL;DR: In this article, a neural network is tuned online using novel tuning laws to learn the complete plant dynamics so that a local asymptotic stability of the identification error can be shown.
TL;DR: Through simulation in Matlab by selecting appropriate fuzzy rules are designed to tune the parameters Kp, Ki and Kd of the PID controller, the performance of the hydraulic system has improved significantly compare to conventional PID controller.
Abstract: In this paper, Self Tuning Fuzzy PID controller is developed to improve the performance of the electro-hydraulic actuator. The controller is designed based on the mathematical model of the system which is estimated by using System Identification technique. The model is performed in a linear discrete model to obtain a discrete transfer function for the system. Model estimation procedures are done by using System Identification Toolbox in Matlab. Data for model estimation is taken from experimental works. Fuzzy logic is used to tune each parameter of PID controller. Through simulation in Matlab by selecting appropriate fuzzy rules are designed to tune the parameters Kp, Ki, and Kd of the PID controller, the performance of the hydraulic system has improved significantly compare to conventional PID controller.
TL;DR: Taghizadeh et al. as mentioned in this paper presented a nonlinear dynamic model of a PWM-driven pneumatic fast switching valve, including electro-magnetic, mechanical and fluid subsystems of the valve.
TL;DR: The model captures the dynamic behavior of a rigid robotic manipulator with elastic joints that includes electromechanical submodels of the motor and gear from which the relationship between the applied torque and the joint torsion is identified.
Abstract: This paper presents a novel approach to the modeling and identification of elastic robot joints with hysteresis and backlash. The model captures the dynamic behavior of a rigid robotic manipulator with elastic joints. The model includes electromechanical submodels of the motor and gear from which the relationship between the applied torque and the joint torsion is identified. The friction behavior in both presliding and sliding regimes is captured by generalized Maxwell-slip model. The hysteresis is described by a Preisach operator. The distributed model parameters are identified from experimental data obtained from internal system signals and external angular encoder mounted to the second joint of a 6-DOF industrial robot. The validity of the identified model is confirmed by the agreement of its prediction with independent experimental data not previously used for model identification. The obtained models open an avenue for future advanced high-precision control of robotic manipulator dynamics.
01 Jan 2009
TL;DR: The concept “cost of complexity” is defined which is a measure of the minimum required experimental effort as a function of the system complexity, the noise properties, and the amount, and desired quality, of theSystem information to be extracted from the data.
Abstract: A key issue in system identification is how to cope with high system complexity. In this contribution we stress the importance of taking the application into account in order to cope with this issue. We define the concept “cost of complexity” which is a measure of the minimum required experimental effort (e.g. used input energy) as a function of the system complexity, the noise properties, and the amount, and desired quality, of the system information to be extracted from the data. This measure gives the user a handle on the trade-offs that must be considered when performing identification with a fixed experimental “budget”. Our analysis is based on the observation that the identification objective is to guarantee that the estimated model ends up within a pre-specified “level set” of the application objective. This geometric notion leads to a number of useful insights: Experiments should reveal system properties important for the application but may also conceal irrelevant properties. The latter, dual, objective can be explored to simplify model structure selection and model error assessment issues. We also discuss practical issues related to computation and implementation of optimal experiment designs. Finally, we illustrate some fundamental limitations that arise in identification of structured systems. This topic has bearings on identification in networked and decentralized systems.
TL;DR: The main contribution is to show that incrementally globally asymptotically stable nonlinear control systems with disturbances admit symbolic models.
Abstract: Symbolic models are abstract descriptions of continuous systems in which symbols represent aggregates of continuous states In the last few years there has been a growing interest in the use of symbolic models as a tool for mitigating complexity in control design In fact, symbolic models enable the use of well-known algorithms in the context of supervisory control and algorithmic game theory for controller synthesis Since the 1990s many researchers faced the problem of identifying classes of dynamical and control systems that admit symbolic models In this paper we make further progress along this research line by focusing on control systems affected by disturbances Our main contribution is to show that incrementally globally asymptotically stable nonlinear control systems with disturbances admit symbolic models
TL;DR: In this article, a global optimization method called "Modal Trimming Method" is adopted to identify the values of model parameters, and the trend and periodic change are first removed from time series data on energy demand and the converted data is used as the main input to a neural network.
TL;DR: The developed wireless distributed infrared sensor system can run as a standalone prisoner/patient monitoring system under any illumination conditions, as well as a complement for conventional video and audio human tracking and identification systems.
Abstract: This paper presents a wireless distributed pyroelectric sensor system for tracking and identifying multiple humans based on their body heat radiation. This study aims to make pyroelectric sensors a low-cost alternative to infrared video sensors in thermal gait biometric applications. In this system, the sensor field of view (FOV) is specifically modulated with Fresnel lens arrays for functionality of tracking or identification, and the sensor deployment is chosen to facilitate the process of data-object-association. An Expectation-Maximization-Bayesian tracking scheme is proposed and implemented among slave, master, and host modules of a prototype system. Information fusion schemes are developed to improve the system identification performance for both individuals and multiple subjects. The fusion of thermal gait biometric information measured by multiple nodes is tested at four levels: sample, feature, score, and decision. Experimentally, the prototype system is able to simultaneously track two individuals in both follow-up and crossover scenarios with average tracking errors less than 0.5 m. The experimental results also demonstrate system's potential to be a reliable biometric system for the verification/identification of a small group of human subjects. The developed wireless distributed infrared sensor system can run as a standalone prisoner/patient monitoring system under any illumination conditions, as well as a complement for conventional video and audio human tracking and identification systems.
Proceedings Article•
01 Jan 2009TL;DR: The need of the partial knowledge of the nonlinear system dynamics is relaxed in the development of a novel approach to ADP using a two part process: online system identification and offline optimal control training.
Abstract: The optimal control of linear systems accompanied by quadratic cost functions can be achieved by solving the well-known Riccati equation. However, the optimal control of nonlinear discrete-time systems is a much more challenging task that often requires solving the nonlinear Hamilton―Jacobi―Bellman (HJB) equation. In the recent literature, discrete-time approximate dynamic programming (ADP) techniques have been widely used to determine the optimal or near optimal control policies for affine nonlinear discrete-time systems. However, an inherent assumption of ADP requires the value of the controlled system one step ahead and at least partial knowledge of the system dynamics to be known. In this work, the need of the partial knowledge of the nonlinear system dynamics is relaxed in the development of a novel approach to ADP using a two part process: online system identification and offline optimal control training. First, in the system identification process, a neural network (NN) is tuned online using novel tuning laws to learn the complete plant dynamics so that a local asymptotic stability of the identification error can be shown. Then, using only the learned NN system model, offline ADP is attempted resulting in a novel optimal control law. The proposed scheme does not require explicit knowledge of the system dynamics as only the learned NN model is needed. The proof of convergence is demonstrated. Simulation results verify theoretical conjecture.
TL;DR: In this article, an inverse method based on a system-identification technique for identifying impact events on a complex structure with built-in sensors is presented, which uses the transfer functions in the system identification technique to identify the location and force time history of an impact event on a structure without the need of constructing a full-scale accurate structural model or of acquiring excessive training data on the structure, such as neural-network techniques.
Abstract: An inverse method based on a system-identification technique for identifying impact events on a complex structure with built-in sensors is presented. The method using the transfer functions in the system-identification technique identifies the location and force time history of an impact event on a structure without the need of constructing a full- scale accurate structural model or of acquiring excessive training data on the structure, such as neural-network techniques. The system transfer functions for the entire structure are constructed by two sequential procedures: 1) limited impact tests at selected points to establish the system transfer functions from the selected points to a sensor on the structure and 2) an interpolation function approach based on a linear finite element to approximate the system transfer functions from a point inside four neighboring selected points to the sensor. Comprehensive tests with various impact situations verified the accuracy of load and position predictions by the proposed method.
TL;DR: In this article, the authors developed a vehicle sidelip observer that takes the nonlinearities of the system into account, both in the theoretical analysis and the design, to make the observer suitable for implementation in the embedded hardware, and a reduction in the number of tuning parameters compared to the EKF.
Abstract: The objective of this article is to develop a vehicle sideslip observer that takes the nonlinearities of the system into account, both in the theoretical analysis and the design. The design goals include reduction of the computational complexity compared to the EKF, to make the observer suitable for implementation in the embedded hardware, and a reduction in the number of tuning parameters compared to the EKF. Design is based on a standard sensor configuration, and is subjected to the extensive testing in the realist conditions.