Closed-loop System Identification Research Articles

Non-excitation closed-loop identification based on hysteresis bias relay feedback

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.

Joint input–output identification of unstable systems with kernel regularization

This paper discusses closed-loop identification of unstable systems. In particular, we first apply the joint input–output identification method and then convert the identification problem of unstable systems into that of stable systems, which can be tackled by using kernel-based regularization methods. We propose to identify two transfer functions by kernel regularization, the one from the reference signal to the input, and the one from the reference signal to the output. Since these transfer functions are stable, kernel regularization methods can construct their accurate models. Then the model of unstable system is constructed by ratio of these functions. The effectiveness of the proposed method is demonstrated by a numerical example and a practical experiment with a DC motor.

A Partial Approximate Least Absolute Deviation-Based Identification Algorithm for a Multivariable Closed-Loop System with Spike Noise

Considering the stability and safety of industrial production, the open-loop system cannot meet the requirements of industrial production, so more and more attention has been paid to multivariable closed-loop system identification. This study focuses on the identification of a multivariate closed-loop system with spike noise in which the model order of the feedback channel is lower than that of the forward channel. Combining principal component analysis (PCA), which is used to eliminate the correlation between the data matrix elements, and the derivable approximate least absolute criterion function, partial approximate least absolute deviation (PALAD) for multivariable closed-loop system identification is proposed. By introducing a deterministic function to replace the absolute value in partial approximate least absolute deviation, the non-differentiable problem of the least absolute deviation function can be solved, and the identifiability of the multivariable closed-loop system in the case mentioned above is theoretically verified. Simulation experiments show the validity of the PALAD algorithm. Compared with the partial least squares (PLS) method, PALAD can effectively restrain the spike noise that follows an SαS distribution and shows stronger robustness when white and spike noises exist simultaneously.

Noninvasive Electrical Stimulation Neuromodulation and Digital Brain Technology: A Review.

We review the research progress on noninvasive neural regulatory systems through system design and theoretical guidance. We provide an overview of the development history of noninvasive neuromodulation technology, focusing on system design. We also discuss typical cases of neuromodulation that use modern noninvasive electrical stimulation and the main limitations associated with this technology. In addition, we propose a closed-loop system design solution of the "time domain", "space domain", and "multi-electrode combination". For theoretical guidance, this paper provides an overview of the "digital brain" development process used for noninvasive electrical-stimulation-targeted modeling and the development of "digital human" programs in various countries. We also summarize the core problems of the existing "digital brain" used for noninvasive electrical-stimulation-targeted modeling according to the existing achievements and propose segmenting the tissue. For this, the tissue parameters of a multimodal image obtained from a fresh cadaver were considered as an index. The digital projection of the multimodal image of the brain of a living individual was implemented, following which the segmented tissues could be reconstructed to obtain a "digital twin brain" model with personalized tissue structure differences. The "closed-loop system" and "personalized digital twin brain" not only enable the noninvasive electrical stimulation of neuromodulation to achieve the visualization of the results and adaptive regulation of the stimulation parameters but also enable the system to have individual differences and more accurate stimulation.

Closed-loop Aspects of Data-Enabled Predictive Control

In recent years, the amount of data available from systems has drastically increased, motivating the use of direct data-driven control techniques that avoid the need of parametric modeling. The aim of this paper is to analyze closed-loop aspects of these approaches in the presence of noise. To analyze this, a unified formulation of several approaches, including Data-enabled Predictive Control (DeePC) and Subspace Predictive Control (SPC) is obtained and the influence of noise on closed-loop predictors is analyzed. The analysis reveals potential closed-loop correlation problems, which are closely related to well-known results in closed-loop system identification, and consequent control issues. A case study reveals the hazards of noise in data-driven control.

Closed-Loop System Identification Approach of the Inertial Models

The mathematical model that approximates the dynamics of the industrial process is essential for the efficient synthesis of control algorithms in industrial applications. The model of the process can be obtained according to the identification procedures in the open-loop, or in the closed-loop. In the open-loop, the identification methods are well known and offer good process approximation, which is not valid for the closed-loop identification, when the system provides the feedback output and doesn’t permit it to be identified in the open-loop. This paper offers an approach for experimental identification in the closed-loop, which supposes the approximation of the process with inertial models, with or without time delay and astatism. The coefficients are calculated based on the values of the critical transfer coefficient and period of the underdamped response of the closed-loop system with P controller, when system achieves the limit of stability. Finally, the closed-loop identification was verified by the computer simulation and the obtained results demonstrated, that the identification procedure in the closed-loop offers good results in process of estimation of the model of the process.

Analysis of low-frequency vibration in a steam turbine based on closed-loop system identification

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.

Closed-loop identification of multi-axis active magnetic bearing systems based on decentralized and decoupling control

In response to the open-loop instability of magnetic bearing systems, this paper develops a closed-loop identification method to obtain an accurate plant model for controller design. The identification method is based on a decentralized and decoupling control architecture, which allows the decoupling of the multi-input-multi-output (MIMO) system into two single-input-single-output (SISO) systems for simplifying the identification process. The identification uses a pseudo-random binary sequence as the excitation signal. A parameterized model is obtained through a parameter estimation algorithm based on the output error model. To ensure that the model prediction error can converge during the computation process, a filter is incorporated into the estimation algorithm. The filter can be synthesized systematically by solving a set of linear matrix inequalities. The performance of the proposed closed-loop identification method is firstly verified by simulations. Then it is implemented on a five-axis magnetic bearing platform and the parameterized model obtained from the identification is compared to the experimental frequency response.

Design, Modeling, and Control Allocation of a Heavy-Lift Aerial Vehicle Consisting of Large Fixed Rotors and Small Tiltrotors

In this article, we propose an unconventional heavy-lift aerial vehicle (HLAV), present the design and its control allocation analysis, and prove the concept by the demonstration of the experimental test prototype in the outdoor environment. We aim for a mechanically robust and simple vehicle design that efficiently performs heavy lifting compared to other common aerial vehicles under certain width constraints, without using a complex swashplate mechanism. The HLAV performs the task of carrying the main load with two large propellers that are efficient, thanks to the greater disk area, and includes two small tilting propellers for controllability. This system has a “PPNN” (P: clockwise and N: counterclockwise) propeller arrangement to compensate for the reaction torques of equally sized propeller pairs placed on opposite sides. However, conventional quadcopters with the “PPNN” propeller arrangement lose their controllability upon hovering. Therefore, servo motors are integrated into the small propellers to ensure controllability. The nonlinear dynamic model of the HLAV is built by the Newton–Euler approach, and the system is linearized around the hover equilibrium point for controllability analysis. Model parameters are estimated using closed-loop system identification tools. High-level controllers are designed with the “loop shaping” method. To show feasibility, the proposed system is experimentally verified with a newly designed prototype, and sufficient trajectory tracking performance is achieved in a stable manner.

Synthesis cascade estimation for aircraft system identification

PurposeThe purpose of this paper extends the authors’ previous contributions on aircraft system identification, such as open loop identification or closed loop identification, to cascade system identification. Because the cascade system is one special network system, existing in lots of practical engineers, more unknown systems are needed to identify simultaneously within the statistical environment with the probabilistic noises. Consider this problem of cascade system identification, prediction error method is proposed to identify three unknown systems, which are parameterized by three unknown parameter vectors. Then the cascade system identification is transferred as one parameter identification problem, being solved by the online subgradient descent algorithm. Furthermore, the nonparametric estimation is proposed to consider the general case without any parameterized process. To make up the identification mission, model validation process is given to show the asymptotic interval of the identified parameter. Finally, simulation example confirms the proposed theoretical results.Design/methodology/approachFirstly, aircraft system identification is reviewed through the understanding about system identification and advances in control theory, then cascade system identification is introduced to be one special network system. Secondly, for the problem of cascade system identification, prediction error method and online subgradient decent algorithm are combined together to identify the cascade system with the parameterized systems. Thirdly from the point of more general completeness, another way is proposed to identify the nonparametric estimation, then model validation process is added to complete the whole identification mission.FindingsThis cascade system corresponds to one network system, existing in lots of practice, such as aircraft, ship and robot, so it is necessary to identify this cascade system, paving a way for latter network system identification. Parametric and nonparametric estimations are all studied within the statistical environment. Then research on bounded noise is an ongoing work.Originality/valueTo the best of the authors’ knowledge, research on aircraft system identification only concern on open loop and closed loop system identification, no any identification results about network system identification. This paper considers cascade system identification, being one special case on network system identification, so this paper paves a basic way for latter more advanced system identification and control theory.

A LOW-COST APPROACH TO DATA-DRIVEN FUZZY CONTROL OF SERVO SYSTEMS

Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) open-loop data-driven system identification to produce the process model from input-output data expressed as the system step response, (ii) Proportional-Integral (PI) controller tuning using the Extended Symmetrical Optimum (ESO) method, (iii) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller in terms of the modal equivalence principle, (iv) closed-loop data-driven system identification, (v) PI controller tuning using the ESO method, (vi) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller. The steps (iv), (v) and (vi) are optional. The approach is applied to the position control of a nonlinear servo system. The experimental results obtained on laboratory equipment validate the approach.

Optimal Input Spectrum Design for Closed-Loop System Identification

Optimal Input Spectrum Design for Closed-Loop System Identification

Finite Sample Properties for Closed Loop System Identification

In this paper, after closed loop system identification is reviewed, asymptotic analysis and finite sample analysis for closed loop system identification are studied respectively, corresponding to the infinite data and finite data. More specifically, within the framework of infinite data, the cost function is modified to its simplified form, and one optimal feedback controller is obtained based on our own derivations. The simplified cost function and optimal feedback controller are benefit for practical application. Furthermore, the asymptotic variance of that optimal feedback controller is also yielded from the point of asymptotic analysis. In the case of finite data, finite sample properties are constructed for closed loop system identification, then one difference between the sampled identification criterion and its corresponding expected criterion is derived as an explicit form, which can bound one guaranteed interval for the sampled identification criterion. Finally, one simulation example is used to prove the efficiency of our proposed theories.

Design of an enhanced fractional order PID controller for a class of second-order system

PurposeThis paper aims to design a modified fractional order proportional integral derivative (PID) (FO[PI]λDµ) controller based on the principle of fractional calculus and investigate its performance for a class of a second-order plant model under different operating conditions. The effectiveness of the proposed controller is compared with the classical controllers.Design/methodology/approachThe fractional factor related to the integral term of the standard FO[PI]λDµ controller is applied as a common fractional factor term for the proportional plus integral coefficients in the proposed controller structure. The controller design is developed using the regular closed-loop system design specifications such as gain crossover frequency, phase margin, robustness to gain change and two more specifications, namely, noise reduction and disturbance elimination functions.FindingsThe study results of the designed controller using matrix laboratory software are analyzed and compared with an integer order PID and a classical FOPIλDµ controller, the proposed FO[PI]λDµ controller exhibit a high degree of performance in terms of settling time, fast response and no overshoot.Originality/valueThis paper proposes a methodology for the FO[PI]λDµ controller design for a second-order plant model using the closed-loop system design specifications. The effectiveness of the proposed control scheme is demonstrated under different operating conditions such as external load disturbances and input parameter change.

Frequency Response From Solar PV: A Dynamic Equivalence Closed-Loop System Identification Approach

The analysis of the frequency response of integrated transmission-distribution networks with deep penetration of solar photovoltaic (PV) generation faces major challenges due to the complexity emerging from the dynamic models of the numerous and diverse PV units involved. This article proposes converter-based dynamic equivalent models for both distributed (distribution network-connected) and large-scale (transmission network-connected) PV units, which take into account practical issues, such as measurement and coordination delays. Different from the previous work that adopted an open-loop identification, the unknown model parameters are identified here through a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">closed-loop</i> identification process based on the least-square minimization. This allows capturing the continuous interaction between system and PV responses, thus improving the outcome of the overall frequency response model. The proposed models are validated with the real data from the August 2018 separation event in Australia. The results demonstrate the excellent performance of the proposed models in determining the frequency response from PV in both transmission and distribution networks, hence paving the way to its adoption in the frequency stability analysis in low-carbon grids dominated by frequency-responsive renewables.

A Simple Framework for Identifying Dynamical Systems in Closed-Loop

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.

Online closed loop system identification for the actuator of a flying vehicle

The frequent minor wars have brought to the fore guided missiles as the main weapon against all types of military targets. Restoring the single shot kill probability can be achieved by using two methods as follows: first, using large warhead with the large lethal area; second, design a robust guidance system to reduce the miss-distance with high single shot kill probability. The performance of an anti-tank guided missile can be enhanced by select the nominal model to design the guidance computer system. Furthermore, an online closed-loop system identification is required to adaptive autopilot design. This paper is devoted to using system identification in position control robotic benchmark via open and closed loops system identification. Hence, applying the closed-loop methods to identify the actuator and airframe of the intended plant in order to design a self-tuning controller. The online system identification is based on the recursive least square method.

Online closed loop system identification for the actuator of a flying vehicle

Online closed loop system identification for the actuator of a flying vehicle

Collection disruption management and channel coordination in a socially concerned closed-loop supply chain: A game theory approach

Uncertainty in real-world situations disrupts operations, including the collection process in closed-loop supply chains (CLSCs). A collection disruption is more critical in the pharmaceutical sector since pharmaceutical leftovers contain chemicals that threaten the environment and human health. This paper revolves around the challenges of a real pharmaceutical case that implements circular economy principles through a closed-loop system design, takes sustainability issues into account, and seeks for effective management of collection disruption. The case includes a manufacturer, who invests in green research and development (R&D), and two retailers competing on corporate social responsibility (CSR) efforts to boost the collection amount and market demand. This competitive environment raises conflict of interests and complicates the interactions between members, which need to be neutralized by an appropriate coordination plan. This paper proposes an analytical scenario-based coordination model that resolves channel conflicts and pays dividends to the involving members through augmenting their social, economic, and environmental performance. We show that the coordination plan could be a practical policy to increase the system’s adaptability to disruption. Under the coordinated model, by increasing a retailer’s collection disruption, the other one invests more in CSR efforts to compensate for its competitor’s lower collection, preventing loss for the whole channel. We also demonstrate that the proposed model maintains the chain’s balance and prevents loss in case of a highly competitive CSR-based collection and boosts the collection amount, market demand, and the whole chain’s profitability simultaneously.

Closed-loop identification of MIMO systems in the Prediction Error framework: Data informativity analysis

In the Prediction Error identification framework, it is essential that the experiment yields informative data with respect to the chosen model structure to get a consistent estimate. In this work, we focus on the data informativity property for the identification of multi-inputs multi-outputs systems in closed-loop and we derive conditions to verify if a given external excitation combined with the feedback introduced by the controller yields informative data with respect to the chosen model structure. This study covers the case of multiple-inputs multiple-outputs model structures that are natural generalizations of the classical single-input single-output model structures used in Prediction Error identification and the classical types of external excitation vectors, i.e., vectors whose elements are either multisine or filtered white noises.