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To solve the aircraft dynamics modeling problem in the entire envelope range, this work proposes a closed-loop system identification method based on deep learning. A closed-loop flight test was designed, under the framework of the closed-loop flight test, the motion mode of the aircraft was fully stimulated by the input signal of the control rudder surface and the airspeed and position commands. The lateral and longitudinal aerodynamic coefficients were solved from the flight test data, and the black box relationship between the aerodynamic coefficients and their influencing factors was established based on the deep network technology. The aerodynamic coefficient black box model was combined with the dynamics and kinematic equations of the aircraft to form a deep network dynamic model of the aircraft, which belongs to a gray box dynamics model. The deep network can easily and uniformly process different batches of flight test data, thus combining the flight test data at different flight state points, and finally building a complete aerodynamic model within the entire envelope range. Three groups of flight tests were performed: the first group of tests was used for model set training, the second group of test data was used for the selection of the best model, and the third group of flight tests was used for model validation. The model verification was completed from two aspects: the prediction of the aerodynamic coefficient and the prediction of the flight state variables. The results show that the deep network model can complete high-precision modeling of aerodynamic coefficients; and the gray box dynamic model can complete the modeling of aircraft dynamics within the entire envelope, and can be used as a long-term, high-precision flight simulator. [ABSTRACT FROM AUTHOR]

In this paper was proposed an approach for control-relevant identification. The experimental identification the mathematical model of the underdamped inertial systems is performed in the closed-loop and the control object is proposed to be approximated with transfer function with inertia second order, where the coefficients are calculated based on the simple analytical expressions according to the values extracted from underdamped step response of the closed-loop system with P controller. The proposed approach of control-relevant identification was verified by the computer simulation in software package MATLAB and the obtained results demonstrated, that the identification approach offers good results in process of estimation the model of the control object and methodology of tuning the PID controller offers possibility of imposing the performance to the automatic control system. [ABSTRACT FROM AUTHOR]

Steam turbines are used to generate thermal power in electric power plants. They are important industrial equipment that support societal infrastructure. The stable operation of steam turbines is necessary to maintain long-term electrical power supply. However, low-frequency vibration, which is referred to as steam-whirl-induced vibration, is a self-excited vibration that can damage turbines and hinders stable operation. Therefore, a prediction model and stable margin for steam-whirl-induced vibration in steam turbines should be developed. In this study, we propose a method for modeling steam-whirl-induced vibration using closed-loop system identification. This method directly creates a vibration model from the rotor displacement data. The gain, damping, and natural frequency of the vibration were calculated using this model. Moreover, an equation for the relationship between the damping and load was derived using the model, and the stable margin for increasing the load was estimated. Steam-whirl-induced vibration was modeled using the proposed method for the displacement data obtained from an actual steam turbine. The characteristics of the model are in good agreement with the experimental results, indicating the feasibility of using the model to predict steam-whirl-induced vibration. [ABSTRACT FROM AUTHOR]

• system identification method based on hysteresis loop bias relay feedback without using excitation is designed • hysteresis loop bias relay instead of a controller is proposed • a parameter scheme for the hysteresis loop bias relay to guide the selection of parameters is proposed This paper proposes a closed-loop system identification method based on hysteresis loop bias relay feedback without using excitation. The method utilizes hysteresis loop bias relay instead of a controller to ensure the informativity of data without external excitation and with only noisy feedback. Additionally, this paper proposes a parameter scheme for the hysteresis loop bias relay to guide the selection of parameters more rationally, thus improving the accuracy of the identification model. Compared to recently proposed switching controller methods, the advantage of this method lies in the fact that it requires fewer samples and shortens data collection time. The method's effectiveness is verified through simulation comparisons with typical methods. [ABSTRACT FROM AUTHOR]

This paper provides a subspace method for closed-loop identification, which clearly specifies the model order from noisy measurement data. The method can handle long I/O data of the target system to be noise-tolerant and determine the model order via nuclear norm minimization. First, the proposed method compresses the long data by projecting them to an appropriate low dimensional subspace, then obtains a low order model whose order is specified by a combination of data compression and nuclear norm minimization. Its effectiveness is demonstrated through detailed numerical examples.

In this paper, we propose a simple framework for closed-loop system identification: the stabilized output error method. Traditional closed-loop system identification methods rely on the linearity of the target system, and they require the identification of noise models or prior knowledge or identifiability of the feedback controllers to obtain unbiased estimates. But in many real-world applications, the noise dynamics and feedback controllers are complex and difficult to identify, and the nonlinearity may not be ignored. The proposed framework introduces a virtual controller that stabilizes the error between model prediction and the output of the target system. This enables us to apply the output error method, which gives unbiased estimates without depending on the noise model and is applicable to a wide range of models, including nonlinear systems, to closed-loop system identification problems. The paper describes the framework and gives the theoretical support and design guidelines for virtual controllers. Through numerical examples, we show the effectiveness of the proposed framework in various situations, which include identification of (a) linear gray box models, (b) systems in the presence of disturbances having realistic complexity, and (c) a nonlinear unstable system in a human-in-the-loop environment.

This report documents the findings of the study “Vehicle/Driver Monitoring for Enhanced Safety of Transit Buses” conducted as part of the PATH project TO5400. The goal of study TO5400 is todesign and implementation of a reliable vehicle/driver monitoring system with aim to enhance driving safety of transit buses.The main objectives of the project are: (1) to identify a simply model that best describes the driving patterns of human drivers, and (2) to develop an algorithm which can generate warning messages when there is a provision of danger.In this study, it is assumed that the driver sets the angle of the steering wheel in response to the lateral deviation of the vehicle from the center line of the road and/or the road curvature ahead of the vehicle which he/she infers from the visual perception. It is also assumed that the dynamic relationship between the lateral deviation and the road curvature (i.e. the inputs to the driver) and the steering angle (i.e. the output from the driver) may be represented by a linear model with Auto Regressive Moving Average with eXogeneous (ARMAX) structure. The order of the ARMAX model is determined by off-line analysis of experimental data collected from actual driving tests. Once the order of the model is determined, the parameters of the model can be estimated by processing the input and output data in real time during actual driving.The on-line data processing and prediction can also estimate three critical parameters for driving safety: the driver’s response time (tr), the time-to-collision (TTC) and the time-to-lane-crossing (TLC) TLC. A timely warning message scheme, which incorporates all the three parameters, is designed to determine whether an alarm should be issued.

Industrial robots are electro-mechanical systems with double integrator behaviour, necessitating operation and model identification under closed-loop control conditions. The Inverse Dynamic Identification Model (IDIM) is a mechanical model based on Newton’s laws that has the advantage of being linear with respect to the parameters. Existing Instrumental Variable (IDIM-IV) estimation provides a robust solution to this estimation problem and the paper introduces an improved IDIM-PIV method that takes account of the additive noise characteristics by adding prefilters that provide lower variance estimates of the IDIM parameters. Inspired by the prefiltering approach used in optimal Refined Instrumental Variable (RIV) estimation, the IDIM-PIV method identifies the nonlinear physical model of the robot, as well as the noise model resulting from the feedback control system. It also has the advantage of providing a systematic prefiltering process, in contrast to that required for the previous IDIM-IV method. The issue of an unknown controller is also considered and resolved using existing parametric identification. The evaluation of the new estimation algorithms on a six degrees-of-freedom rigid robot shows that they improve statistical efficiency, with the controller either known or identified as an intrinsic part of the IDIM-PIV algorithm. [ABSTRACT FROM AUTHOR]

We present a frequency-domain technique for identifying multivariable feedback and feedforward subsystems that are interconnected with a known subsystem. This subsystem identification algorithm uses closed-loop input–output data, but no other system signals are assumed to be measured. In particular, neither the feedback signal nor the outputs of the unknown subsystems are assumed to be measured. We use a candidate-pool approach to identify the feedback and feedforward transfer function matrices, while guaranteeing asymptotic stability of the identified closed-loop transfer function matrix. The main analytic result shows that if the data noise is sufficiently small and the candidate pool is sufficiently dense, then the parameters of the identified feedback and feedforward transfer function matrices are arbitrarily close to the true parameters. [ABSTRACT FROM AUTHOR]

It is of great importance to develop an online modeling method for chemical processes operated in closed loop for better understanding, monitoring the process or other purposes without endangering the system. This paper intends to devise an online system identification method, particularly for the batch process, by fully exploiting its intrinsic repetitiveness. It properly uses the information from the time direction and the batch direction, thus leading to a gradual performance enhancement. In addition, the identification method formulates the priori controller knowledge such as closed-loop stability as optimization constraints to refine the parameter estimates. A trust region method is employed to overcome the significant computation burden of directly handling these constraints such as solving Lyapunov inequalities. An adaptive filter is introduced to further smooth the parameter estimates. Finally, the effectiveness of the approach is verified by three numerical examples including a two-tank system. [ABSTRACT FROM AUTHOR]

In this paper we consider the identification of a linear module that is embedded in a dynamic network using noisy measurements of the internal variables of the network. This is an extension of the errors-in-variables (EIV) identification framework to the case of dynamic networks. The consequence of measuring the variables with sensor noise is that some prediction error identification methods no longer result in consistent estimates. The method developed in this paper is based on a combination of the instrumental variable philosophy and closed-loop prediction error identification methods, and leads to consistent estimates of modules in a dynamic network. We consider a flexible choice of which internal variables need to be measured in order to identify the module of interest. This allows for a flexible sensor placement scheme. We also present a method that can be used to validate the identified model. [ABSTRACT FROM AUTHOR]

In today's globally competitive marketplace, chemical plants and refineries are looking at new ways to increase plant efficiency, production rates, safety and reliability. In the process control arena, base-level PID tuning optimization, APC (Advanced Process Control) and MPC (Model Predictive Control) remain attractive and under-utilized options. This paper describes various new and novel ideas harnessing the power from primary PID control improvements and APC/MPC implementation. The techniques and methodologies described can increase a plant's profit margin from 2 to 10 %. Spectacular increases in plant profits as high as 15 to 20 % (equivalent to 2 mil Euros/year) have been achieved and demonstrated in some cases. A few real examples from actual industrial plants have been provided in this paper. [ABSTRACT FROM AUTHOR]

This paper deals with system identification for control of linear parameter varying systems. In practical applications, it is often important to be able to identify small plant changes in an incremental manner without shutting down the system and/or disconnecting the controller; unfortunately, closed-loop system identification is more difficult than open-loop identification. In this paper we prove that the so-called Hansen scheme, a technique known from linear time-invariant systems theory for transforming closed-loop system identification problems into open-loop-like problems, can be extended to accommodate linear parameter varying systems as well. We investigate the identified subsystem's parameter dependency and observe that, under mild assumptions, the identified subsystem is affine in the parameter vector. Various identification methods are compared in direct and Hansen Scheme setups in simulation studies, and the application of the Hansen Scheme is seen to improve the identification performance. [ABSTRACT FROM AUTHOR]

Short-term cardiovascular regulation mediated by the sympathetic and parasympathetic branches of the autonomic nervous system has been investigated by multivariate autoregressive (MVAR) modeling, providing insightful analysis. MVAR models employ, as inputs, heart rate (HR), systolic blood pressure (SBP) and respiratory waveforms. ECG (from which HR series is obtained) and respiratory flow waveform (RFW) can be easily sampled from the patients. Nevertheless, the available methods for acquisition of beat-to-beat SBP measurements during exams hamper the wider use of MVAR models in clinical research. Recent studies show an inverse correlation between pulse wave transit time (PWTT) series and SBP fluctuations. PWTT is the time interval between the ECG R-wave peak and photoplethysmography waveform (PPG) base point within the same cardiac cycle. This study investigates the feasibility of using inverse PWTT (IPWTT) series as an alternative input to SBP for MVAR modeling of the cardiovascular regulation. For that, HR, RFW, and IPWTT series acquired from volunteers during postural changes and autonomic blockade were used as input of MVAR models. Obtained results show that IPWTT series can be used as input of MVAR models, replacing SBP measurements in order to overcome practical difficulties related to the continuous sampling of the SBP during clinical exams. [ABSTRACT FROM PUBLISHER]

Background: Human stance involves multiple segments, including the legs and trunk, and requires coordinated actions of both. A novel method was developed that reliably estimates the contribution of the left and right leg (i.e., the ankle and hip joints) to the balance control of individual subjects. Methods: The method was evaluated using simulations of a double-inverted pendulum model and the applicability was demonstrated with an experiment with seven healthy and one Parkinsonian participant. Model simulations indicated that two perturbations are required to reliably estimate the dynamics of a double-inverted pendulum balance control system. In the experiment, two multisine perturbation signals were applied simultaneously. The balance control system dynamic behaviour of the participants was estimated by Frequency Response Functions (FRFs), which relate ankle and hip joint angles to joint torques, using a multivariate closed-loop system identification technique. Results: In the model simulations, the FRFs were reliably estimated, also in the presence of realistic levels of noise. In the experiment, the participants responded consistently to the perturbations, indicated by low noise-to-signal ratios of the ankle angle (0.24), hip angle (0.28), ankle torque (0.07), and hip torque (0.33). The developed method could detect that the Parkinson patient controlled his balance asymmetrically, that is, the right ankle and hip joints produced more corrective torque. Conclusion: The method allows for a reliable estimate of the multisegmental feedback mechanism that stabilizes stance, of individual participants and of separate legs. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a practical approach to identify a global model of a wind turbine from operational data, while it operates in a turbulent wind field with a varying mean wind speed and under closed-loop control. The approach is based on the realization that the nonlinearities are dominated by the aerodynamics of the rotor, which change with the operating condition. The dynamics of a wind turbine can be decomposed into a nonlinear static part, governed by the torque and thrust characteristics of the rotor, and a linear time-invariant dynamic part. The multi-input–multi-output linear dynamics are estimated using a recent closed-loop subspace identification method. The practical applicability of the algorithm is demonstrated by applying it to data obtained from the NREL CART 3 research turbine. [Copyright &y& Elsevier]

The instrumental variable (IV) method is one of the most renowned methods for parameter estimation. Its bigger advantage is that it is applicable for open-loop as well as for closed-loop systems. The main difficulty in closed-loop identification is due to the correlation between the disturbances and the control signal induced by the loop. In order to overcome this problem, additional excitation signal is introduced. Non-recursive modifications of the instrumental variable method for closed-loop system identification on the base of a generalized IV method have been developed (Atanasov and Ichtev, 2009; Gilson and Van den Hof, 2001; Gilson and Van den Hof, 2003). In this paper, recursive algorithms for theses modifications are proposed and investigated. A simulation is carried out in order to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

Least-squares method is the most popular method for parameter estimation. It is easy applicable, but it has considerable drawback. Under well-known conditions in the presence of noise, the LS method produces asymptotically biased and inconsistent estimates. One way to overcome this drawback is the implementation of the instrumental variable method. In this paper several modifications of this method for closed-loop system identification are considered and investigated. The covariance matrix of the instrumental variable estimates is discussed. A simulation is carried out in order to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

This paper studies the closed-loop system identification (ID) error when a dynamic integral controller is used. Pseudo-random binary sequence (PRBS) q-Markov covariance equivalent realization (Cover) is used to identify the closed-loop model, and the open-loop model is obtained based upon the identified closed-loop model. Accurate open-loop models were obtained using PRBS q-Markov Cover system ID directly. For closed-loop system ID, accurate open-loop identified models were obtained with a proportional controller, but when a dynamic controller was used, low-frequency system ID error was found. This study suggests that extra caution is required when a dynamic integral controller is used for closed-loop system identification. The closed-loop identification framework also has significant effects on closed-loop identification error. Both first- and second-order examples are provided in this paper. [ABSTRACT FROM AUTHOR]

Properties of neural controllers for closed-loop sensorimotor behavior can be inferred with system identification. Under the standard paradigm, the closed-loop system is perturbed (input), measurements are taken (output), and the relationship between input and output reveals features of the system under study. Here we show that under common assumptions made about such systems (e.g. the system implements optimal control with a penalty on mechanical, but not sensory, states) important aspects of the neural controller (its zeros mask the modes of the sensors) remain hidden from standard system identification techniques. Only by perturbing or measuring the closed-loop system “between” the sensor and the control can these features be exposed with closed-loop system identification methods; while uncommon, there exist noninvasive techniques such as galvanic vestibular stimulation that perturb between sensor and controller in this way. [ABSTRACT FROM AUTHOR]

Abstract: It is well known that the quality of the parameters identified during an identification experiment depends on the applied excitation signal. Prediction error identification using full order parametric models delivers an ellipsoidal region in which the true parameters lie with some prescribed probability level. This ellipsoidal region is determined by the covariance matrix of the parameters. Input design strategies aim at the minimization of some measure of this covariance matrix. We show that it is possible to optimize the input in an identification experiment with respect to a performance cost function of a closed-loop system involving explicitly the dependence of the designed controller on the identified model. In the present contribution we focus on finding the optimal input for the estimation of the parameters of a minimum variance controller, without the intermediate step of first minimizing some measure of the model parameter accuracy. We do this in conjunction with using covariance formulas which are not asymptotic in the model order, which is rather new in the domain of optimal input design. The identification procedure is performed in closed-loop. Besides optimizing the input power spectrum for the identification experiment, we also address the question of optimality of the controller. It is a wide belief that the minimum variance controller should be the optimal choice, since we perform an experiment for designing a minimum variance controller. However, we show that this may not always be the case, but rather depends on the model structure. [Copyright &y& Elsevier]

Abstract: In this paper, several instrumental variable (IV) and instrumental variable-related methods for closed-loop system identification are considered and set in an extended IV framework. Extended IV methods require the appropriate choice of particular design variables, as the number and type of instrumental signals, data prefiltering and the choice of an appropriate norm of the extended IV-criterion. The optimal IV estimator achieves minimum variance, but requires the exact knowledge of the noise model. For the closed-loop situation several IV methods are put in an extended IV framework and characterized by different choices of design variables. Their variance properties are considered and illustrated with a simulation example. [Copyright &y& Elsevier]

This work presents a newly developed six-axis magnetic suspension stage for precision motion control. The designed travel volume is 4×4×2 mm in translation and 1°×1°×2° in rotation. A dynamic model of the feedback linearized and uncoupled stage is developed for the purpose of motion control. Model parameter variations are demonstrated through closed-loop system identification. In motion control, a parameter variation model is proposed in conjunction with a reduced order observer to compensate the joined effect of disturbance, modeling error, and cross coupling. Experimental results in terms of positioning stability, motion resolution, rotational motion control, model regulation, large travel multiaxis contouring, and disturbance rejection are shown. Uniform positioning stability and invariant dynamic response within the designed travel volume are illustrated. [ABSTRACT FROM PUBLISHER]

This paper presents an indirect adaptive-control approach and its implementation for realizing large travel ultra precision x-y-theta motion control of a magnetic-suspension stage, which is actuated by ten electromagnets and is capable of six-degrees-of-freedom motion. Feedback linearization of the nonlinear force relationship of the electromagnet in terms of the coil current and the air gap is implemented. Due to modeling errors, perfect feedback linearization is not possible, and parameter variations of the feedback-linearized system are demonstrated through closed-loop system identification. Each axis of the feedback-linearized system is then modeled as a double integrator having gain value depending on the position of the stage and subjected to a disturbance. For the purpose of large travel x-y-theta motion control, an indirect adaptive-control algorithm is designed and implemented for each axis of the feedback-linearized system. The developed control algorithm consists of three procedures: a) real-time parameter estimation; b) model cancellation; and c) nominal linear control. Experimental results demonstrate that the indirect adaptive controllers have superior tracking ability when compared to constant gain robust linear H∞ controllers. [ABSTRACT FROM PUBLISHER]

The model-plant mismatch (MPM) can be responsible for poor control performance. This can be solved by locating which channels (i.e., pars of MVs-CVs) are suffering the highest MPM, and then, proceed with the model updating using the same historical data used for the assessment and diagnosis steps. This paper proposes a method for updating models based on the nominal error: a benchmark that quantifies the model discrepancy by comparing the measured output of a system with its corresponding nominal output, i.e., the output of the closed-loop with no MPM or unmeasured disturbances. The main advantages of the method are to avoid usual multivariable model identification and use simpler SISO structures, thus reducing the workload of the model maintenance. The effectiveness of the method is illustrated using the quadruple-tank process with a nonminimum phase operating point to explore multivariable characteristics of the final updated model. All the required methodologies for assessment, diagnosis, and model maintenance are also presented in the paper and can be applied in the same historical data. No additional plant perturbations are required to improve the model. Although it is not limited to Model Predictive Control (MPC), the proposed methodology can be successfully applied to MPC assessment, diagnosis, and model maintenance. [ABSTRACT FROM AUTHOR]