Black-box System Identification Research Articles

An Empirical Human Controller Model for Preview Tracking Tasks.

Real-life tracking tasks often show preview information to the human controller about the future track to follow. The effect of preview on manual control behavior is still relatively unknown. This paper proposes a generic operator model for preview tracking, empirically derived from experimental measurements. Conditions included pursuit tracking, i.e., without preview information, and tracking with 1 s of preview. Controlled element dynamics varied between gain, single integrator, and double integrator. The model is derived in the frequency domain, after application of a black-box system identification method based on Fourier coefficients. Parameter estimates are obtained to assess the validity of the model in both the time domain and frequency domain. Measured behavior in all evaluated conditions can be captured with the commonly used quasi-linear operator model for compensatory tracking, extended with two viewpoints of the previewed target. The derived model provides new insights into how human operators use preview information in tracking tasks.

A MISO model for power consumption in virtualized servers

Energy efficiency is always a concern in hosting servers. When any new development is added to a host server, the power consumption of the host server must be theoretically and empirically re-evaluated. Because of the ongoing development trends in computing systems at the hardware, software and middleware levels, deriving a direct mathematical model for quantifying the power consumption of a host server is difficult. Therefore, a system identification is used to construct the power consumption model for virtualized hosting servers. To date, three types of system identifications have been used in the literature for defining the power consumption model: the first-principles, the black-box and the gray-box identification approaches. To the best of our knowledge, the majority of these approaches are apparently used to model the power consumption in a single-input single-output (SISO) system model, in which a hardware component is reconfigured to meet the power budget target. In this paper, to accommodate the ongoing development trends in computing systems, we propose a multi-input single-output (MISO) model for modeling the power consumption of virtualized hosting servers. We use the black-box system identification method, and we utilize the Auto-Regressive eXogenous (ARX) mathematical model to construct the MISO power model. We compare our MISO power model with the SISO power model that is used in existing state-of-the-art works. Empirically, our MISO power model exhibits higher accuracy than the existing SISO power model in predicting power consumption. Using our model, we can achieve approximately 98 % accuracy in predicting the power consumption of virtualized hosting servers.

Rotor Temperature Modeling of an Induction Motor using Subspace Identification

Knowledge of the rotor temperature is important for an efficient and secure operation of induction motors, especially in electric vehicles. However, in vehicle applications, measuring the rotor temperature is not possible, making a model-based estimation necessary. In this paper, the modeling of the rotor temperature is presented based on subspace identification, a black-box system identification method. The performed steps to achieve a high quality model are described in detail, including a systematic selection of inputs from the large amount of available signals. The resulting model is applied as an open-loop observer and evaluated using measurements of a specially equipped induction motor on a test bench, where the rotor temperature was measurable. Finally, some remarks are given regarding the on-board implementation of the identified model into a vehicle control unit for open-loop observation of the rotor temperature.

Nonlinear Black-box System Identification through Neural Networks of a Hysteretic Piezoelectric Robotic Micromanipulator

Piezoelectric micromanipulators are used in applications with precise and high dynamics positioning. This recognition is thanks to their high resolution, bandwidth and stiffness. Its nonlinear behavior, however, complicates the design of robust control laws with respect to no or imprecise sensing. In this context, this work presents the identification of a piezoelectric micromanipulator through nonlinear black-box neural networks with data acquired in a laboratory setup. A comparison is made regarding the model complexity. The results show the accuracy of the models, their statistical validity and that they were able to capture the dynamics of the micromanipulator adequately.

Linear and nonlinear system identification techniques for modelling of a remotely operated underwater vehicle

As opposed to classical mathematical-based modelling approach, this paper reports a black-box system identification technique for characterising the dynamics of a remotely operated vehicle (ROV). A linear system identification technique is employed to model the vehicle dynamics. However, use is also made of advance neural networks-based nonlinear system identification approach to model rudder-depth channel nonlinear behaviour. Different model validity tests are also employed to instil confidence in the identified linear and nonlinear ROV dynamic models. High fidelity models obtained for the multi-degree-of-freedom vehicle are of immense importance for developing ROV simulators, pilot training and autopilot design.

A data-driven quadratic stability condition and its application for stabilizing unknown nonlinear systems

This paper proposes a data-driven stability criterion for quadratic stabilization of unknown nonlinear discrete-time systems. The novelty of this quadratic stability criterion lies in the direct use of the time series of system states, instead of using mathematical models. The data-driven stability criterion is utilized to design a control for stabilizing unknown nonlinear systems using online black-box system identification. The effectiveness and the adaptability of the proposed approach are compared with those of adaptive feedback linearization method with an example of stabilizing a nonlinear aeroelastic system.

Intelligent computationally efficient modelling of multi-input multi-output non-linear dynamical process plants: An industrial steam generator case study

Most of the process plants have intrinsic non-linear, time-varying, non-minimum phase and coupling characteristics that result in a highly time-consuming and tedious task to derive complicated non-linear dynamic equations governing on such types of processes. On the other hand, even though such equations can be analytically extracted, they have not worked out in most cases yet. To find a simple fix that can address these hurdles associated with analytical modelling, two types of non-analytical models, namely, simulator and predictor, are proposed based on a computationally efficient technique so-called locally linear neuro-fuzzy modelling. That is, a simulator model as well as a specially structured long-term predictor model that is built based on a sequential arrangement of single-stage predictor models is presented for multi-input multi-output non-linear dynamical process plants and feasibly applied to a real water-tube steam generator for the first time. An adaptive evolving algorithm named linear model tree contributes to estimate the parameters of neuro-fuzzy simulator and predictor models. Furthermore, an order selection method based on Lipschitz theory, whose merit is being totally independent from developing any model prior to commencing tedious modelling trials, is also proposed for the first time by defining a modified Lipschitz index to remedy the order determination problem within the very first steps of black-box non-linear system identification. The blend of such a fully modelling-independent order selection algorithm and a unique type of computationally inexpensive non-linear neuro-fuzzy model lessens the overall computational burden of modelling procedure that represents a great concern in a black-box system identification approach. The recorded data from a real water-tube steam generator operating at Abbot Power plant unit of Champaign, IL, were exploited to carry out experimental modelling in order to reveal the pros and cons of the presented models.

Active noise control using adaptive POLYnominal Gaussian WinOwed wavelet networks

The capabilities of wavelet networks in function approximation make them appealing for black box system identification. In this paper, a new active noise control (ANC) algorithm is developed based on adaptive wavelet networks. The proposed adaptive nonlinear noise control approach employs frames from POLYnominal WinOwed with Gaussian wavelets. Also, a novel network structure for active noise control is derived incorporating a nonlinear static mapping cascaded with an infinite impulse response filter to model the dynamic part of the network. Online dynamic backpropagation learning algorithms based on gradient descent method are applied to adjust the network parameters. Local convergence of the closed-loop system is proved using discrete Lyapunov function.The performance of the proposed ANC system is examined for typical linear/nonlinear cases. The simulation results demonstrate superior performance of this method in terms of stability, fast convergence rate and noise attenuation while avoiding curse of dimensionality.

An Electromyographic-driven Musculoskeletal Torque Model using Neuro-Fuzzy System Identification: A Case Study

The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications.

Black-box system identification for reduced order model construction

For the simulation of multi-physics, multi-scale phenomena, it is often advantageous to build a comprehensive system- or process-model from a collection of sub-models, each of them purposely constructed to describe a certain aspect of the overall problem with high accuracy at low computational cost. Such strategies of divide et impera (“divide and conquer”) integrate modeling approaches of different complexity for different phenomena and scales. Reduced order models (ROMs) identified from time series data can play an important part in such a scheme.The present paper reviews a body of work in aero- and thermo-acoustics, where computational fluid dynamics (CFD) simulation is combined with tools from system identification to characterize the dynamic response of a sub-system (an “element”) to incoming flow perturbations. The element under consideration is treated as a “black box” with a given structure of inputs and outputs. In general, multiple inputs and multiple outputs are present (MIMO model), in the simplest case only a single input and a single output need be considered (SISO structure). Once the response to a broad-band excitation signal is determined by numerical simulation, a ROM representation of the element dynamics can be deduced with system identification. For that purpose, a wide range of methods is available, selection of the most suitable method for a given problem is a non-trivial matter.Selected results obtained with the CFD/SI approach are reviewed, supplemented by best practice recommendations for successful and accurate identification of ROMs from time series data. Perspectives for the use of this method in other fields of science and engineering are developed.

System Identification and H∞ Observer Design for TRMS

A dynamic model for the two-degree-of-freedom Twin Rotor MIMO System (TRMS) is extracted using a blackbox system identification technique. Its behaviour in certain aspects resembles that of a helicopter, with a significant cross coupling between longitudinal and lateral directional motions. Hence, it is an interesting identification and control problem. Using the extracted model an H∞ observer is designed which will estimate the states of the system in presence of worst case noise assumed to be impact on the system.

Modeling and Control of Cutter Vibration in the Presence of Random Distribution of Microhardness of Workpiece and Nonlinear Cutting Forces in Lathe Process

This work presents a comprehensive approach to the control of tool's position, in the presence of machine tool structure vibration, nonlinear cutting force, and random tool vibration due to random distribution of microhardness of workpiece material. The controller is combination of Proportional and linear quadratic gaussian- (P-LQG-) type constructed from an augmented model of both tool-actuator dynamics and a nonlinear dynamic model relating tool displacement to cutting forces. The latter model is obtained using black-box system identification of experimental orthogonal cutting data in which tool displacement is the input and cutting force is the output. The controller is evaluated and its performance is demonstrated.

Coordinated control design of multiple HVDC links based on model identification

This paper presents a method for designing a centralized coordinated controller for several HVDC links. The controller increases the damping of the power oscillations by modulating the current through the HVDC links in a coordinated fashion. To design a centralized coordinated controller a reduced order open system model is estimated. The open system model of the power system is estimated using the Numerical Algorithms for Subspace State-Space System Identification (N4SID) algorithm which is a black-box system identification technique. The current set-point change through the HVDC links is the set of input signals and the speeds of the generators are the set of outputs. This controller design method increases the damping significantly, which is shown for a small power system.

Identification of low-order parameter-varying models for large-scale systems

In this paper we propose a novel procedure for obtaining low-dimensional models of large-scale multi-phase, non-linear, reactive fluid flow systems. Our approach is based on the combination of methods of proper orthogonal decompositions, black-box system identification techniques and non-linear spline based blending of local linear black-box models to create a reduced order linear parameter-varying model. The proposed method, which is of empirical nature, gives computationally very efficient low-order process models for large-scale processes. The proposed method does not need Galerkin type of projections on equation residuals to obtain the reduced order models and the proposed method is of generic nature. The efficiency of the proposed approach is illustrated on a benchmark problem of an industrial glass manufacturing process where the process non-linearity and non-linearity arising due to the corrosion of refractory materials is approximated using a linear parameter varying model. The results show good performance of the proposed framework.

Identification of Low Order Parameter Varying Models for Large Scale Systems

In this paper we propose a novel procedure for obtaining reduced dimensional models of large scale multi-phase, non-linear, reactive fluid flow systems with geometric parameter uncertainty (corrosion). Our approach is based on the combinations of methods of Proper Orthogonal Decomposition (POD), black box System Identification (SID) techniques and nonlinear spline based blending of local black box models to create Reduced Order Linear Parameter Varying (RO-LPV) model. The proposed method gives computationally very efficient reduced dimension models for processes with parameter uncertainty. The efficiency of proposed approach is illustrated on a benchmark problem depicting industrial Glass Manufacturing Process (GMP) with corrosion of refractory materials as a process parameter uncertainty. The results show good performance of the proposed method.

IN-FLIGHT IDENTIFICATION OF THE AUGMENTED FLIGHT DYNAMICS OF THE RMAX UNMANNED HELICOPTER

The flight dynamics of the Yamaha RMAX unmanned helicopter has been investigated, and mapped into a six degrees of freedom mathematical model. The model has been obtained by a combined black-box system identification technique and a classic model-based parameter identification approach. In particular, the closed-loop behaviour of the built-in attitude control system has been studied, to support the decision whether to keep it as inner stabilization loop or to develop an own stability augmentation system. The flight test method and the test instrumentation are described in detail; some samples of the flight test data are compared to the model outputs as validation, and an overall assessment of the built-in stabilization system is supplied.

Dynamic modelling and open-loop control of a two-degree-of-freedom twin-rotor multi-input multi-output system

A dynamic model for the characterization of a two-degree-of-freedom (2DOF) twin-rotor multi-input multi-output system (TRMS) in hover is extracted using a black box system identification technique. Its behaviour in certain aspects resembles that of a helicopter, with a significant crosscoupling between longitudinal and lateral directional motions. Hence, it is an interesting identification and control problem. The extracted model is employed for designing and implementing a feedforward/open-loop control. Open-loop control is often the preliminary step for development of more complex feedback control laws. Hence, this paper also investigates open-loop control strategies using shaped command inputs for resonance suppression in the TRMS. Digital low-pass and band-stop shaped inputs are used on the TRMS test bed, based on the identified vibrational modes. A comparative performance study is carried out and the results presented. The low-pass filter is shown to exhibit better vibration reduction. When modal coupling exists, decoupled feedback controllers are incapable of eliminating vibration. In such cases, generating motion by shaped reference inputs is clearly advantageous.

BLACK-BOX ATTACK USING NEURO-IDENTIFIER

The cryptanalysis problem can be described as an unknown (Black-Box), system identification problem, and the goal of the cryptography is to build a cryptographic system that is hard to identify. Neural networks are ideal tools for Black-Box system identification. A black-box attack against classical and stream cryptosystems is presented by constructing a Black-Box Neuro-Identifier model to achieve two objectives; the first objective is to determine the key from given plaintext-ciphertext pair, while the second objective is to construct a Neuro-model for the target cipher system.

Dynamic modelling and open-loop control of a two-degree-of-freedom twin-rotor multi-input multi-output system

A dynamic model for the characterization of a two-degree-of-freedom (2DOF) twin-rotor multi-input multi-output system (TRMS) in hover is extracted using a black box system identification technique. Its behaviour in certain aspects resembles that of a helicopter, with a significant crosscoupling between longitudinal and lateral directional motions. Hence, it is an interesting identification and control problem. The extracted model is employed for designing and implementing a feedforward/open-loop control. Open-loop control is often the preliminary step for development of more complex feedback control laws. Hence, this paper also investigates open-loop control strategies using shaped command inputs for resonance suppression in the TRMS. Digital low-pass and band-stop shaped inputs are used on the TRMS test bed, based on the identified vibrational modes. A comparative performance study is carried out and the results presented. The low-pass filter is shown to exhibit better vibration reduction. When modal coupling exists, decoupled feedback controllers are incapable of eliminating vibration. In such cases, generating motion by shaped reference inputs is clearly advantageous.

Dynamic modelling and linear quadratic Gaussian control of a twin-rotor multi-input multi-output system

This paper presents an investigation into the modelling and control of a one-degree-of-freedom (1 DOF) twin-rotor multi-input multi-output (MIMO) system (TRMS). The behaviour of the TRMS in certain aspects resembles that of a helicopter. Hence, it is an interesting identification and control problem. A dynamic model characterizing the TRMS in hover is extracted using a black-box system identification technique. The extracted model is employed in the design of a feedback linear quadratic Gaussian compensator, namely the stability augmentation system (SAS). This has a good tracking capability but requires high control effort and has inadequate authority over residual vibration of the system. These problems are resolved by further augmenting the system with a command path prefilter, resulting in the command and stability augmentation system (CSAS). The combined feedforward and feedback compensator satisfies the performance objectives and obeys the actuator constraint. The control law is implemented in realtime on the TRMS platform.