Online Identification Algorithm Research Articles

Adaptive control for refrigeration via online identification

Vapor compression refrigeration systems (VCRS) occupy a crucial position in modern society and in the field of thermal sciences. However, the operation of VCRS is subjected to both external disturbances (dynamic-changing environment) and inherent system characteristics (coupling or nonlinear features), leading to issues like reduced refrigeration efficiency and significant fluctuations in cooling capacity. To address these challenges and enhance the adaptability of VCRS in dynamically changing environments, this study establishes a dynamic simulation model for VCRS based on the Switched Moving-Boundary method. The impact of external environmental disturbances on refrigeration performance is investigated, and continuous online identification methods are employed to elucidate its internal coupling characteristics and nonlinear features. The adaptive temperature control method is introduced, benefiting from the developed recursive least squares method with a forgetting factor for online identification, achieving precise model identification which facilities the real-time parameters tuning of adaptive controller. The results indicate that the hybrid paradigm of online identification and adaptive control algorithm not only effectively handles various disturbances but also reduces overshoot and IAE by 2-3 orders of magnitude compared to traditional PID controllers. Adaptive PID control maintains overshoot in the 10-4 order of magnitude and IAE in the 10-5 order of magnitude.

Non-invasive real-time diagnosis of PMSM faults implemented in motor control software for mission critical applications

The machine’s health should be continuously monitored, or a test should be applied on a regular basis to predict failure before fatal damage is incurred. Unexpected failures, particularly in mission-critical applications, can cause irreversible damage to the system, or even human life. This paper introduces a novel non-intrusive, real-time, online Condition Monitoring (CM), and Fault Diagnosis (FD) system for Permanent Magnet Synchronous Machines (PMSMs). Only the motor drive’s built-in sensors, such as current and position sensors, are used to detect three types of faults: inter-turn short circuit, partial demagnetization, and static eccentricity. It encompasses the implementation of algorithms within a motor drive system and the creation of failure mode models. The proposed solution adopts a hardware-free approach, utilizing current/voltage signature analysis for cost-effectiveness. It requires a small memory and short execution time, allowing it to be implemented on a simple motor controller with limited memory and calculation power. The drive system is intended for mission critical applications, therefore, computation load, code size, memory allocation, run-time optimization, etc. are the key focuses for real-time operation. It offers immediate insights into motor’s health without interrupting the drive operation. Additionally, it ensures rapid processing with modest computational requirements, making it adaptable for implementation on any PMSM controller. The non-intrusive nature of this diagnostic approach has the potential to enhance safety in systems reliant on PMSM drives. The proposed method has a high detection accuracy of 98%, is computationally efficient and can detect and classify the fault accurately. Simulation and experimental results demonstrate the efficiency of the FD algorithm for online identification and classification of machine faults. Theoretical hypotheses are proven based on experimental data.

Vertical load estimation in tractors via in-wheel optical sensing

This paper presents a novel approach for estimating the vertical load exerted on the rear axle of agricultural vehicles during fieldwork using tire compression measurements. The system comprises an optical time-of-fight (TOF) sensor integrated into the wheel rim, a microcontroller, and an online identification algorithm. The sensor exhibits minimal measurement error, approximately 1%, which, when combined with effective preprocessing and a piecewise linear regression model fueled by tire pressure data, yields a root mean square error (RMSE) of around 7%, with a maximum drawdown of 3% when considering periodic updates. This technology promises to reduce production costs while enabling optimal tire pressure calibration, thereby ensuring reduced fuel consumption and improved comfort for agricultural vehicle operators.

Stiffness Compensation Control for Centrifugal Compressors Based on Online Parameter Identification of Magnetic Bearings

Significant temperature rise will degrade the stability of the active magnetic bearing (AMB) rotor system, which is an obstacle to the industrial implementation of magnetically suspended centrifugal compressors (MSCCs) with high power density. Aiming to ensure stable operation of the suspended rotor at high temperatures, this paper presents a stiffness compensation method based on online parameter identification. First, the linearized model at the operating point is derived through the establishment of a magnetic circuit model. The analysis of the temperature factor of bearing stiffness parameters is conducted. Then, the forgetting factor recursive least squares online identification algorithm is employed to obtain the coil resistance affected by bearing temperature in real time. Finally, a stiffness compensation control scheme is designed based on the identification results and current rotor displacement, in combination with series and feedback compensation to apply the same electromagnetic force to the rotor during temperature rise. Simulation and experimental results validate that the proposed method can effectively suppress the low-frequency fluctuation and guarantee the stability of the AMB rotor system.

Recursive system identification method from binary input/output measurements

AbstractThis paper presents two online identification algorithms of finite impulse response (FIR) systems using binary measurements both on the input and on the output. These algorithms are based on the least mean square (LMS) technique and on the estimation of the correlation functions of the input and output from binary data. Note that the second algorithm is a simplified version of the first one in the case of a white noise on the input. The convergence and variance analyses are provided. A numerical example is given to demonstrate the effectiveness of the proposed algorithms.

A survey on parameters estimation of the proton exchange membrane fuel cells based on the swarm-inspired optimization algorithms

The main purpose of this study is to review various swarm-inspired optimization algorithms to discuss the significance of some established works in this area. Accurate parameter estimation is required to guarantee proper modeling of PEMFCs. However, because PEMFC models are complex, non-linear, and multivariate, parameter estimation is quite difficult. To estimate the linear and non-linear parameters of a PEMFC model in real time, this work investigates PEMFC model parameters estimation methods with a focus on online identification algorithms, which are thought of as the foundation of designing a global energy management strategy. Various PEMFC models with various classifications and objectives are initially addressed in this regard. The parameters of two well-known semi-empirical models in the literature, including 500 W BCS PEMFC and the 6 kW NedSstack PS6 PEMFC have then been identified using some potential swarm-inspired optimization algorithms for practical applications, such that the TSD error for the NedStack PS6 and BCS PEMFC based on the swarm-inspired optimization algorithms provide averagely 2.22 and 0.047, respectively. Finally, the obtained accomplishments and upcoming difficulties are highlighted.

Either-or communication-based identification of FIR systems with binary-valued observations and channel uncertainty

Event-triggered mechanism can effectively save communication resources, however, when it encounters channel uncertainty, the remote receiver cannot distinguish between “the sender did not send data” and “the sender sent data but the data lost” when it does not receive data, which causes that it is difficult to make full use of the information provided by the event-triggered mechanism. This paper addresses the identification of FIR (Finite Impulse Response) systems with binary-valued observations and either-or communication mechanism when the packet loss probability is known and unknown respectively. When the packet loss probability is known, it is used for compensation in the parameter estimation. An online identification algorithm is proposed, its strong convergence is proved, and its asymptotic normality is given. Furthermore, how does the packet loss probability affect the algorithm performance is discussed. When the packet loss probability is unknown, an identification algorithm is proposed to jointly estimate it and unknown system parameters by redesigning the either-or communication mechanism. The strong convergence of the algorithm is shown. The tradeoff between the communication rate and the convergence performance of the identification algorithm is modelled as a constrained optimization problem, and its solution is obtained. The rationality of theoretical results is verified by numerical simulation.

Identification of ARMA models with binary-valued observations

This paper studies system identification of ARMA models with binary-valued observations. Compared with existing quantized identification of ARMA models, this problem is more challenging since the accessible information is much less. Different from the identification of FIR models with binary-valued observations, the prediction of original system output and the parameter both need to be estimated in ARMA models. We propose an online identification algorithm consisting of parameter estimation and prediction of original system output. The parameter estimation and the prediction of original output are strongly coupled but mutually reinforcing. By analyzing the two estimates at the same time instead of analyzing separately, we finally prove that the parameter estimate can converge to the true parameter with convergence rate O(1/k) under certain conditions. Simulations are given to demonstrate the theoretical results.

MSLCFinder: An Algorithm in Limited Resources Environment for Finding Top-k Elephant Flows

Encrypted traffic accounts for 95% of the total traffic in the backbone network environment with Tbps bandwidth. As network traffic becomes more and more encrypted and link rates increase in modern networks, the measurement of encrypted traffic relies more on collecting and analyzing massive network traffic data that can be separated from the support of high-speed network traffic measurement technology. Finding top-k elephant flows is a critical task with many applications in congestion control, anomaly detection, and traffic engineering. Owing to this, designing accurate and fast algorithms for online identification of elephant flows becomes more and more challenging. Existing methods either use large-size counters, i.e., 20 bit, to prevent overflows when recording flow sizes or require significant space overhead to measure the sizes of all flows. Thus, we adopt a novel strategy, called count-with-uth-level-sampling, in this paper, to find top-k elephant flows in limited resource environments. Moreover, the proposed algorithm, called MSLCFinder, incurs lightweight counter and uth-level multi-sampling with small, constant processing for millions of flows. Experimental results show that MSLCFinder can achieve more than 97% precision with an extremely limited hardware resource. Compared to the state-of-the-art, our method realizes the statistics and filtering of millions of data streams with less memory.

Power Optimization Distribution Method for Fuel Cell System Cluster Comprehensively Considering System Economy

In high-power applications, the use of fuel cell system cluster (FCSC) is increasingly valued. However, most of the existing strategies emphasis in optimizing a certain control objective (e.g., efficiency or consumption), but do not fully consider the impact of other factors such as stack degradation. To address this research gap, this article proposes a power allocation method related to the system economy, which considers multiple factors that affect the operating cost of FCSC. The efficiency and hydrogen consumption are analyzed, and two high-order polynomials are used to represent these two curves. Besides, considering that the fuel cell (FC) output characteristics are variable, an online identification algorithm is adopted to update the system parameters in real-time. Moreover, in the formulated cost function, stack degradation is also considered. What is more, to ensure that the FCs can operate in the high efficiency range as much as possible, we also add constraints to limit the FCs output power. The effectiveness of the proposed method has been verified on a hardware-in-the-loop simulation platform. Compared with the equal distribution strategy, the proposed method can effectively ameliorate the economy of the system. Furthermore, the proposed method has been experimentally validated and its real-time operation ability has been highlighted on an actual FCSC.

Application of Hardware-in-the-Loop Simulation Technology in the Development of Electro-Hydraulic Servo System Control Algorithms

In this paper, we present a method to identify paramaters for controlling electro-hydraulic servo systems in a real-time environment. With the aim of addressing the problem that it is difficult to accurately obtain the state space equation parameters of the physical entity of the electro-hydraulic servo system, we introduce an online identification theory (recursive least squares method) for identifying said parameters of the state space model in a valve-controlled symmetrical cylinder system. After accurately obtaining the parameters of the system, nonlinear control of the valve-controlled symmetrical cylinder system is carried out using a backstepping algorithm. In order to verify the actual effect of the online identification algorithm and backstepping algorithm, a hardware-in-the-loop (HIL) simulation platform for the valve-controlled symmetrical cylinder system is built in a Linux real-time system, and the real-time performance of the system is evaluated, which demonstrates that the platform can be reliably applied for subsequent system identification and backstepping verification. The results of the HIL simulation test demonstrate that the online identification algorithm and backstepping control method developed in this paper are effective and reliable.

Modeling and Adaptive Tension Control of Chain Transmission System With Variable Stiffness and Random Load

In the fully mechanized mining face, proper chain tension of chain transmission system is of great significance in reducing the failure rate and improving the service life of scraper conveyor. However, the disturbance caused by random load and variable stiffness makes it very difficult to control chain tension. An adaptive tension control scheme combining a neural command filtering backstepping algorithm and online identification is proposed in this article based on a novel tension model. First of all, the proposed tension model is represented by a linear regression part and a nonlinear mapping part, which effectively reflects the chain transmission characteristics and avoids the full measurement of each chain ring. Then, Levant's differentiator and state observer are used to expand input variables for identification algorithm and neural networks estimation. To avoid the adverse effect of random load on parameter adaption, stiffness online identification algorithm is employed in improving the tension tracking performance. To compensate for nonlinear mapping part and uncertain nonlinearity caused by random load, neural adaptive estimator is effectively integrated with the robust integral term in backstepping design. Moreover, Lyapunov stability of the proposed controller is guaranteed. Finally, comparative experimental results of six controllers are completed to verify the effectiveness of the proposed controller in the static and random load mode.

Virtual Signal Injection Maximum Torque per Ampere Control Based on Inductor Identification

The high-frequency signal injection-type maximum torque per ampere (MTPA) algorithm is usually employed to control the operation of interior permanent magnet synchronous motors (IPMSMs). The MTPA algorithm exhibits good dynamic performance and anti-interference ability. However, due to the injection of a high-frequency current signal, problems such as torque ripple and additional loss are encountered. Therefore, in this paper, a virtual signal injection control (VSIC) method that does not require actual injection is proposed for solving the aforementioned problems while yielding good performance. However, in the control process of the proposed method, the d-axis inductance parameter affects the accuracy of the torque information, resulting in errors in the system. To solve this problem, an online identification algorithm of model reference adaptive systems (MRAS) based on the Popov super stability theory as the basis for the design of the adaptive law is proposed in this paper. The d-axis inductance parameter of the motor is obtained in real-time and then introduced into the control system by using the VSIC method. Finally, VSIC-type MTPA control based on inductance identification is realized. The proposed algorithm does not depend on the design parameters of the motor and exhibits good dynamic response and anti-interference performance.

State of charge estimation of Power lithium-ion battery based on an Affine Iterative Adaptive Extended Kalman Filter

State of charge (SOC) is a very important parameter for power lithium-ion battery in battery operation, but it cannot be measured directly, so it needs to be accurately estimationed. Considering the time-varying characteristics of the model parameters of the power lithium-ion battery, an online identification algorithm called Adaptive Forgetting Factor Recursive Augmented Least Squares (AFFRALS) is proposed. It can obtain a more accurate model compared with the offline method (fixed model parameters) by considering that the noise of power lithium-ion battery system is commonly non-Gaussian white noise in practice, which is different from the existing equivalent circuit models. Then, an Affine Iterative Adaptive Extended Kalman Filter (AIAEKF) method is proposed to deal with the non-Gaussian white noise and accelerate the convergence rate of the estimated results when the initial SOC value is wrong. Experiments demonstrate the effective of the online identification method as well as the SOC estimation method. This method shows a faster convergence rate and better SOC estimation performance than the traditional Extend Kalman Filter (EKF) method. The RMSE of the SOC estimation results is less than 0.023 except for 0 °C, which rarely occurs in practice.

Online identification of optimal efficiency of multi-stack fuel cells(MFCS)

With the development of fuel cells, the demand for high-power fuel cells has gradually increased, but the multi-stack fuel cell system(MFCS) has received wide attention due to the existing technical barriers. The optimal efficiency of the multi-stack fuel cell system is the target, and the power distribution point of the multi-stack fuel cell is obtained by combining the KKT condition. Equipped with the recursive gradient correction method, the online identification of the optimal efficiency of the system is realized. The results are compared with the average distribution and Daisy-chain methods, and the online identification algorithm results in the highest optimal efficiency and the lowest hydrogen consumption. It was validated on two 1kW air-cooled fuel cell systems.

Embedded Point Iteration Based Recursive Algorithm for Online Identification of Nonlinear Regression Models

This paper presents a novel online identification algorithm for nonlinear regression models. The online identification problem is challenging due to the presence of nonlinear structure in the models. Previous works usually ignore the special structure of nonlinear regression models, in which the parameters can be partitioned into a linear part and a nonlinear part. In this paper, we develop an efficient recursive algorithm for nonlinear regression models based on analyzing the equivalent form of variable projection (VP) algorithm. By introducing the embedded point iteration (EPI) step, the proposed recursive algorithm can properly exploit the coupling relationship of linear parameters and nonlinear parameters. In addition, we theoretically prove that the proposed algorithm is mean-square bounded. Numerical experiments on synthetic data and real-world time series verify the high efficiency and robustness of the proposed algorithm.

Handling fault in ambient temperature measurements in a cooling system - A fault tolerant control approach

This paper proposes an active fault tolerant control (AFTC) scheme for detecting and correcting faulty measurements from the ambient temperature sensor that is utilized to calculate the optimal setpoints for a rooftop gas cooler unit operation. The faulty measurements are caused by improper placement of the ambient temperature sensor so that it is exposed to heat generated directly or indirectly by the sun. The AFTC is designed in a plug & play manner with no prior knowledge of the underlying system. The underlying system is identified by applying an online identification algorithm. To ensure the validity of the model several criteria is established. The resulting model is utilized in an observer based fault tolerant controller scheme including a bumpless implementation of the fault correction when a fault has been detected. The validity of the proposed AFTC scheme is investigated using both experimental data as well as simulation.

New Identification Approach and Methods for Plasma Equilibrium Reconstruction in D-Shaped Tokamaks

The paper deals with the identification of plasma equilibrium reconstruction in D-shaped tokamaks on the base of plasma external magnetic measurements. The methods of such identification are directed to increase their speed of response when plasma discharges are relatively short, like in the spherical Globus-M2 tokamak (Ioffe Inst., St. Petersburg, Russia). The new approach is first to apply to the plasma discharges data the off-line equilibrium reconstruction algorithm based on the Picard iterations, and obtain the gaps between the plasma boundary and the first wall, and the second is to apply new identification methods to the gap values, producing plasma shape models operating in real time. The inputs for on-line robust identification algorithms are the measurements of magnetic fluxes on magnetic loops, plasma current, and currents in the poloidal field coils measured by the Rogowski loops. The novel on-line high-performance identification algorithms are designed on the base of (i) full-order observer synthesized by linear matrix inequality (LMI) methodology, (ii) static matrix obtained by the least square technique, and (iii) deep neural network. The robust observer is constructed on the base of the LPV plant models which have the novelty that the state vector contains the gaps which are estimated by the observer, using input and output signals. The results of the simulation of the identification systems on the base of experimental data of the Globus-M2 tokamak are presented.

Echo state networks for online, multi-step MPC relevant identification

This paper presents an online nonlinear system identification algorithm for model predictive control relevant identification, based on an echo state network (ESN) trained using the recursive least squares method with a directional forgetting factor. The proposed online ESN architecture is formed by a reservoir, with fixed recurrent connections, and a reservoir readout mechanism, which is updated online to define the network output. The ESN is used to perform a multi-step prediction task, which can be used for model-predictive control purposes. The proposed online identification algorithm is evaluated in simulation using a conical tank level process and compared with the results of other approaches from the literature. The results show that the proposed algorithm is able to identify the nonlinear characteristics of the process without considering any prior information about it. In addition, the results for long-range predictions are better than the ones obtained with a model linear in its parameters and several baseline ESN models from literature, after the adaptation phase of the proposed algorithm.

An indirect approach for online identification of continuous time‐delay systems

AbstractThis article proposed an online identification technique, also known as a real‐time recursive identification technique for continuous time‐delay systems. The proposed approach is based on online output‐error modeling of unknown continuous time‐delay models, indirectly from discrete time‐delay models. The developed indirect formulation is employed to propose three online identification algorithms using least square, instrument variable, and prediction error minimization techniques. The proposed algorithms utilize auxiliary models and fixed or optimal filtering of input/output data samples to obtain unbiased fixed as well as time‐varying parameter estimates. An estimated error variance‐based stochastic approach is also incorporated to improve the parameter estimates. Extensive simulation experiments are conducted to validate the proposed algorithms' performance. Some useful guidelines are suggested to achieve improved identification accuracy, convergence consistency, and robustness in the presence of the measurement noise and modeling uncertainties. A real practical application for online identification of a heating system is also demonstrated.