System Identification Experiments Research Articles

A nonlinear Wiener system identification based on improved adaptive step-size glowworm swarm optimization algorithm for three-dimensional elliptical vibration cutting

Three-dimensional elliptical vibratory cutting (3D EVC) is a precision machining technology that has been extensively used in the aerospace, military, and civil industries. However, if the best processing performance and precise control are required, system identification is the primary prerequisite. Therefore, a 3D EVC nonlinear Wiener model system identification is studied, and an improved adaptive step-size glowworm swarm optimization algorithm (IASGSO) is proposed to identify and optimize the nonlinear Wiener system model parameters. The step-size value of the IASGSO algorithm is adjusted by the distance based on the global best point, which overcomes the shortcomings of the traditional glowworm swarm optimization (GSO) algorithm that is easy to be premature and generate oscillation near the global optimal solution in the later period. The results show that the IASGSO algorithm has the advantage of effectively searching the global optimum value over the traditional GSO algorithm by using two standard test functions to test the performance of both the GSO algorithm and IASGSO algorithm. Based on the system identification experiment, the fitting rate of the proposed method can reach the accuracy of 98.46%, proving the proposed IASGSO algorithm feasibility and accuracy to a large extent. It is expected that both the proposed IASGSO algorithm and the findings in this work will not only solve the local convergence problem of traditional algorithms but also make sense for the precision machining of processing systems.

System identification of lumped parameter models for weakly nonlinear systems

Experimental system identification through modal analysis is often conducted by assuming linear constitutive laws and kinematic relationships. Significant prediction errors from these identified models can occur when the amplitude dependent contribution of nonlinearities is ignored. In this paper, the impact of such nonlinearities on the parameter identification and accuracy of a linear model is shown to depend significantly on the experimental procedures. Using modal analysis techniques and focusing on vibration near the fundamental mode, a cantilevered beam is modeled as a single degree of freedom oscillator with parameters to be determined through experimental system identification. Various testing procedures are described in detail and used to demonstrate the impact of nonlinearities on the outcome of different system identification processes. The signal control methods compared are (1) open loop, (2) constant input, and (3) constant response excitation. Forcing types considered are single point and distributed. Nonlinearities in experimental response are shown for all procedures, and it is demonstrated that the observed character and degree of the nonlinearities vary depending on the testing procedure. The paper makes various recommendations for experimental system identification procedures using linear structural models, which include developing empirical models for parameter shifts due to nonlinearities and using constant response tests to minimize nonlinear effects in the measured system response. By implementing these identification methods, parameter variations can be included in the linear model to significantly reduce predicted response and parameter errors for weakly nonlinear second-order systems.

Quadrotor aircraft intelligent system identification experiment design

Aircraft prototyping and modeling is usually associated with resource expensive techniques and significant post flight analysis. The NASA Learn-To-Fly concept targets the replacement of the conventional ground-based aircraft model development and prototyping approaches with an efficient real time paradigm. The work presented herein describes the development of an intelligent excitation input design technique that determines excitation frequencies based on predefined rotational motion dynamic model. The input design is then evaluated on quadcopter unmanned aircraft that utilizes the new multisine input design. In order to minimize flight excursions without compromising the modeling capabilities, multisine input power spectrum is optimized based on the vehicle’s frequency response. The proposed methodology emphasizes excitation of modal frequencies which yields flight data rich information content. The generated optimized multisine input design is utilized for a quadcopter aircraft system identification and the performance is compared to conventional uniform amplitudes design. Simulation results show highly accurate model estimation in all identification results in addition to reduction of induced perturbations and power consumption. Additionally, the generated model prediction capabilities are not compromised after power spectrum optimization. Overall, the proposed technique introduces an efficient and intelligent system identification experiment design that can minimize the time and effort spent during excitation input design.

Disturbance feedforward control for active vibration isolation systems with internal isolator dynamics

This paper presents a disturbance feedforward control strategy for active vibration isolation systems that have internal isolator dynamics. The structure of the feedforward controller follows from modeling, but to achieve robust performance the tuning of the controller parameters will be based on measurement data. More specifically, the controller poles, which correspond to internal isolator dynamics, are obtained offline from system identification experiments, while the controller zeros are obtained from online self-tuning using measured sampled-data systems. Self-tuning is used to reduce parameter uncertainty when having too limited identification possibilities, and to account automatically for the effect of noise amplification. Measurement results obtained from an industrial vibration isolation system show that residual vibrations are largely reduced up to the level of output noise limitations.

Free Final Time Input Design Problem for Robust Entropy-Like System Parameter Estimation.

In this paper, a novel method is proposed to design a free final time input signal, which is then used in the robust system identification process. The solution of the constrained optimal input design problem is based on the minimization of an extra state variable representing the free final time scaling factor, formulated in the Bolza functional form, subject to the D-efficiency constraint as well as the input energy constraint. The objective function used for the model of the system identification provides robustness regarding the outlying data and was constructed using the so-called Entropy-like estimator. The perturbation time interval has a significant impact on the cost of the real-life system identification experiment. The contribution of this work is to examine the economic aspects between the imposed constraints on the input signal design, and the experiment duration while undertaking an identification experiment in the real operating conditions. The methodology is applicable to the general class of systems and was supported by numerical examples. Illustrative examples of the Least Squares, and the Entropy-Like estimators for the system parameter data validation where measurements include additive white noise are compared using ellipsoidal confidence regions.

Combined distributed incremental affine projection algorithm for acoustic echo cancellation

To improve the performance of the conventional distributed incremental affine projection algorithm (DIAPA) in acoustic echo cancellation, a combined distributed incremental affine projection algorithm is proposed in this brief. The proposed algorithm combines two DIAPA equipped with different projection orders by employing a mixing scalar parameter, where one with high projection order guarantees fast convergence rate for the initial stage and the other with low projection order maintains small misalignment for the steady stage. The mixing scalar parameter is endowed with capability to adapt itself to be close to one in the initial stage and approximate to zero in the steady-state stage. Moreover, the analysis of the mixing scalar parameter is presented. We also take into account the computational complexity of the proposed algorithm. The simulations conducted in the system identification and acoustic echo cancellation experiments illustrate the superiority of our findings.

Economically Optimal Input Design Approach for Identification of Constrained Processes

Input design is the process of designing informative inputs which maximizes the information about system dynamics using minimal cost and resources in a system identification experiment. In this work, we focus on designing inputs based on the economic framework for identification of constrained processes. The process is nominally constrained and feasibility during identification is ensured by “backing off” (or moving away) from the active constraints. The economic cost or penalty due to the “back-off” is minimized while ensuring feasibility and satisfying constraints on the quality of the identified model. The proposed economic based formulation results in a nonconvex optimization problem. This problem can be solved using the bisection algorithm for single input–single output (SISO) systems. We present a two-stage iterative solution algorithm to solve for multi-input–multi-output (MIMO) systems where a convex optimization problem is solved at each stage. The optimal input is realized as a white noise passing through an M-tap filter. The filter coefficients are obtained by a noniterative spectral factorization technique. The efficacy of the proposed approach has been successfully demonstrated in a mass spring damper system as well as in a Van der Vusse reactor system.

System identification of a low-order heat transfer model for shape memory alloy wires

Shape memory alloys exhibit a strong thermomechanical coupling. This coupling has been modeled extensively using energy-based constitutive models. Equally as important in the dynamics of shape memory alloy–based systems is the heat transfer behavior. The literature is comprehensive on parameter identification for constitutive models but is lacking in a complete resource for identification of the parameters for the heat transfer behavior. In this article, the parameters for a low-order differential equation heat transfer model are determined through experimental system identification. This is completed with two objectives in mind: keeping the order of the model low and accurately modeling the response within an acceptable tolerance. A comparison of identified model parameters to nominal values shows similar magnitudes in all parameters with exception to a significant difference in the heat transfer coefficient. For all parameters, it is shown that a methodical system identification is necessary to develop an accurate predictive model. This article details a clear method to identify the parameters of the low-order heat transfer model.

A family of robust adaptive filtering algorithms based on sigmoid cost

In this paper, a new framework of cost function for designing robust adaptive filtering algorithms is developed. This new cost framework, called sigmoid cost function, results from imbedding the conventional cost function into the sigmoid framework. Utilizing this proposed sigmoid cost framework, several important members (e.g., the sigmoid least mean square, sigmoid least absolute difference, sigmoid least mean fourth, sigmoid least mean logarithmic square, sigmoid least logarithmic absolute difference algorithms) of this family of robust adaptive filtering algorithms are proposed. In the proposed sigmoid cost framework with a steepness parameter α, a greater (smaller) value of α results in a slower (faster) convergence. Thus, an adaptive α is proposed which is based on an exponential function with respect to the L1-norm of the system error. In addition, the mean-square deviation analysis of the proposed algorithms is also carried out and their accuracies are verified via system identification experiment. Simulations in system identification and acoustic echo-cancellation scenarios have demonstrated that the proposed algorithms outperform the corresponding order's generalized maximum correntropy criterion, normalized least mean square using step-size scaler, sign algorithm and least logarithmic absolute difference algorithms.

Dynamic Characteristics Analysis and Test of Dual-Driving Feed System Driven by Center of Gravity

Dual-driving feed system (DDFS) driven by center of gravity (DCG) has been widely used in advanced manufacturing machine for its high rigidity and precision. However, the DCG technology requires that the joint force coincides with the center of gravity of the sliding stage. The dual-driving synchronization and tracking performance will be affected by the change of center of gravity of the sliding stage. Therefore, this paper proposes dynamic characteristics modeling, identification, and control scheme for DDFS driven by center of gravity (DCG). Firstly, a redundancy dynamic model including rotation and pitch vibration caused by the change of the position of center of gravity is presented for DDFS DCG based on the Lagrange method. The model parameters are identified by system identification experiment, and the predictive natural frequencies and vibration modes by the proposed dynamic model are compared by modal experiment. Moreover, the dynamic model-based cross-coupled sliding mode control (CCSMC) is proposed for DDFS DCG. Then, the proposed dynamic model-based CCSMC has been compared with normal cross-coupled sliding mode control (NCCSMC). Both the simulation and experimental results show that the proposed dynamic characteristics analysis and test scheme of DDFS DCG are validated effectively by comparisons.

An Experiment on Closed-loop System Identification of UAV Using Dual-rate Sampling

This paper reports on a closed-loop system identification experiment of an unmanned air vehicle (UAV) with the sampling period of the measured output being an integral multiple of the hold period of the control input. In more detail, the period of the control input of UAV is 5ms, while the position and attitude as the output of UAV are measured by a motion capture system with the sampling period 10ms. A dynamical model of UAV during hovering flight, which is an unstable system, is obtained using a dual-rate closed-loop subspace model identification method. The key point of our method is to apply a closed-loop subspace model identification method proposed by the authors, in order to obtain the so-called lifted model from dual-rate sampling data. Practical usefulness of our dual-rate closed-loop subspace model identification method has been demonstrated by the lab-scale real-life experiment.

Optimal identification experiment design for LPV systems using the local approach

In this paper, we consider the problem of optimally designing the experimental conditions for LPV system identification with the local approach. Such an LPV system identification experiment is characterized by a number of local LTI identification experiments performed at constant values of the scheduling variable. The main contribution of this paper is to determine these constant values of the scheduling variable as well as the input spectra of the corresponding local LTI identification experiments in order to obtain a user-defined model accuracy with the least input energy.

Gain-Scheduled Autopilot Design and Validation for an Experimental Guided Projectile Prototype

This paper investigates the design and validation of a gain-scheduled skid-to-turn autopilot for an experimental canard-guided, fin-stabilized 80-mm-guided projectile prototype in a hardware-in-the-loop (HIL) wind tunnel-based setup. The projectile dynamics are a priori unknown and are determined from open-loop system identification experiments conducted on the test setup. To this end, a nonlinear airframe model is discussed and considered for linearization around equilibrium operating points. The linear model parameters are estimated from several data collection experiments, resulting in a family of linear models. This knowledge is leveraged in a second step to compute a family of robustly stable multiobjective disturbance-rejection and reference-tracking controllers, which are implemented on the HIL test setup using a zero-pole gain interpolation scheme. The time-domain performance of the autopilot is finally assessed by the means of numerical simulations and experimental results gathered from the test setup, demonstrating the excellent suitability of the proposed approach.

Experimental System Identification of a 63kV Substation Post Insulator and the Development of Its Fragility Curves by Dynamic Finite Element Analyses

Electric substations play a great role in electric power systems as the most important lifelines, and post insulators are among the most-used parts in many components of substations. Therefore, realizing the seismic behavior of insulators helps in creating more realistic seismic evaluations of substations, and accordingly, the whole network. In this study, first, various damages to the high-voltage electricity substation insulators during past earthquakes are briefly reviewed. Next, by using the results of a previous experimental study, a 63 kV insulator was modeled using the finite element technique, and by using the accelerograms of 69 earthquakes in 13 PGA categories, the maximum stress values were obtained by time history analyses. Finally, considering an appropriate failure criterion the failure probabilities for all categories were obtained, and the corresponding fragility curves were developed. Results show that a single parameter, particularly PGA, as the hazard index does not lead to satisfactory results for fragility curves.

Modeling and Identification of Irrigation Channel Dynamics Affected by Wind

In this paper, models of open water canals affected by wind stress are derived from first principles and system identification experiments. We show that by adding wind speed and wind direction information to the Saint Venant equations, a physical model capable of describing the actual channel dynamics is obtained. Further, a control-oriented transfer function model is proposed to describe the water level response to wind stress. Validation results using operational data collected from the Waranga Western Main Channel in Australia when strong winds were blowing support our models. The finding will contribute to improve automated water management systems in canals.

Control-Relevant Design of System Identification Experiments to Improve Behavioral Interventions

This paper presents the design of system identification experiments for physical activity behavioral interventions, utilizing a control-relevant identification test monitoring approach. The goal is to find, in real-time, the shortest possible experiment with sufficient information for identification meeting robust performance conditions. The procedure considers multisinusoidal periodic inputs, and allows modifications on the amplitude and harmonic related frequency content of input signals after each periodic evaluation cycle. The local polynomial method (LPM) is utilized for the computation of transfer function and estimation uncertainties. An improved stopping criterion is formulated relying on robust performance metrics. The procedure is tested through a simulation study relying on a hypothetical system based on a real behavioral intervention.

System Identification and Fault Diagnosis of an Electromagnetic Actuator

The electromagnetic actuators are widely used in the industry due to their simple structure, force characteristics, and low manufacturing costs. However, from control point of view, they are nonlinear systems. In this brief, clustering-based system identification experiments as well as a piecewise mathematical model are described. Furthermore, a model-based fault detection and isolation of the actuator is presented, when the armature movement is sensed indirectly, by sensing only the armature current.

Data-driven robust control of the plasma rotational transform profile and normalized beta dynamics for advanced tokamak scenarios in DIII-D

A control-oriented, two-timescale, linear, dynamic, response model of the rotational transform ι profile and the normalized beta βN is proposed based on experimental data from the DIII-D tokamak. Dedicated system-identification experiments without feedback control have been carried out to generate data for the development of this model. The data-driven dynamic model, which is both device-specific and scenario-specific, represents the response of the ι profile and βN to the electric field due to induction as well as to the heating and current drive (H&CD) systems during the flat-top phase of an H-mode discharge in DIII-D. The control goal is to use both induction and the H&CD systems to locally regulate the plasma ι profile and βN around particular target values close to the reference state used for system identification. A singular value decomposition (SVD) of the plasma model at steady state is carried out to decouple the system and identify the most relevant control channels. A mixed-sensitivity robust control design problem is formulated based on the dynamic model to synthesize a stabilizing feedback controller without input constraints that minimizes the reference tracking error and rejects external disturbances with minimal control energy. The feedback controller is then augmented with an anti-windup compensator, which keeps the given controller well-behaved in the presence of magnitude constraints in the actuators and leaves the nominal closed-loop system unmodified when no saturation is present. The proposed controller represents one of the first feedback profile controllers integrating magnetic and kinetic variables ever implemented and experimentally tested in DIII-D. The preliminary experimental results presented in this work, although limited in number and constrained by actuator problems and design limitations, as it will be reported, show good progress towards routine current profile control in DIII-D and leave valuable lessons for further advancements in the field.

Model measurement based identification of Francis turbine vortex rope parameters for prototype part load pressure and power pulsation prediction

Pressure and power fluctuations of hydro-electric power plants in part-load operation are an important measure for the quality of the power which is delivered to the electrical grid. It is well known that the unsteadiness is driven by the flow patterns in the draft tube where a vortex rope is present. However, until today the equivalent vortex rope parameters for common numerical 1D-models are a major source of uncertainty.In this work, a new optimization-based grey box method for experimental vortex rope modelling and parameter identification is presented. The combination of analytical vortex rope and test rig modelling and the usage of dynamic measurements allow the identification of the unknown vortex rope parameters. Upscaling from model to prototype size is achieved via existing nondimensional parameters. In this work, a new experimental setup and system identification method is proposed which are suitable for the determination of the full set of part load vortex rope parameters in the lab. For the vortex rope, a symmetric model with cavity compliance, bulk viscosity and two pressure excitation sources is developed and implemented which shows the best correspondence with available measurement data. Due to the non-dimensional parameter definition, scaling is possible. This finally provides a complete method for the prediction of prototype part-load pressure and power oscillations.Since the proposed method is based on a simple limited control domain, limited modelling effort and also small modelling uncertainties are some major advantages. Due to the generality of the approach, a future application to other operating conditions such as full load will be straightforward.

System Identification & Modelling of GMRT (Giant Meter Wave Radio Telescope)

AbstractGMRT is a unique Indian facility for radio astronomical research to support precision tracking of celestial objects. Currently a type II position servo at GMRT provides ∼1 arc min tracking accuracy. The tracking accuracy deteriorates in the presence of wind gusts. To improve the tracking accuracy, it is proposed to upgrade GMRT servo system with modern high performance controllers, e.g., a Linear Quadratic Gaussian (LQG) controller. However, in order to design a LQG controller, accurate telescope model is required which can be obtained through an experimental approach based on response measurements (System Identification). This paper presents the experimental setup, test conditions and results of system identification experiments carried out for the Elevation axis of one of the telescopes of GMRT. The model was then validated through simulation experiments. The response of the identified system closely matches that of the physical telescope, setting the stage for designing an optimum controller for GMRT.