Online Model Identification Research Articles

Towards maximum efficiency of an open-cathode PEM fuel cell system: A comparative experimental demonstration

Despite its high energy density and zero-carbon emissions, the path to large commercialization for Proton Exchange Membrane Fuel Cell (PEMFC) systems faces challenges related to cost reduction, efficiency enhancement, and durability improvement. One key strategy to address these hurdles is the development of reliable control algorithms that ensure operation at the Maximum Efficiency Point (MEP) of PEMFC systems. This optimization is essential for achieving high efficiency, a crucial factor for PEMFC technology commercial viability. This work explores the experimental design, implementation, and evaluation of Maximum Efficiency Point Tracking (MEPT) control algorithms for a PEMFC system. The investigated methods span across five distinct categories: conventional, data-driven, metaheuristics, online model identification, and filter-based approaches. To assess these methods, a real-time small-scale PEMFC system test bench was built. The setup consists of 200 W open-cathode PEMFC, industrial step-up DC–DC converter, programmable load and a cost-effective measurement and monitoring unit. Merits and demerits of each method are then discussed based on different performance metrics such as tracking speed, hydrogen consumption and power stress. Comparative analysis of the results reveals that, while all the algorithms achieved a maximum electrical efficiency of about 48%, data-driven methods exhibited a superior trade-off between performance metrics. This ultimately leads to improved PEMFC durability.

Online Adaptive Model Identification and State of Charge Estimation for Vehicle-Level Battery Packs

Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive semi-definiteness of the covariance matrix is guaranteed. Combined with the adaptive noise covariance matched filtering algorithm and real-time compensation of system error, the proposed method solves the problem of ever-degrading estimation accuracy in the presence of time-varying noise with unknown statistical characteristics. Using a 66.2-kWh vehicle battery pack, we experimentally verified that the proposed algorithm could achieve high estimation accuracy with guaranteed numerical stability. The maximum error of SOC estimation can be bounded by 1%, and the root-mean-square error is as low as 0.47% under real-world vehicle operating conditions.

CEBoosting: Online sparse identification of dynamical systems with regime switching by causation entropy boosting.

Regime switching is ubiquitous in many complex dynamical systems with multiscale features, chaotic behavior, and extreme events. In this paper, a causation entropy boosting (CEBoosting) strategy is developed to facilitate the detection of regime switching and the discovery of the dynamics associated with the new regime via online model identification. The causation entropy, which can be efficiently calculated, provides a logic value of each candidate function in a pre-determined library. The reversal of one or a few such causation entropy indicators associated with the model calibrated for the current regime implies the detection of regime switching. Despite the short length of each batch formed by the sequential data, the accumulated value of causation entropy corresponding to a sequence of data batches leads to a robust indicator. With the detected rectification of the model structure, the subsequent parameter estimation becomes a quadratic optimization problem, which is solved using closed analytic formulas. Using the Lorenz 96 model, it is shown that the causation entropy indicator can be efficiently calculated, and the method applies to moderately large dimensional systems. The CEBoosting algorithm is also adaptive to the situation with partial observations. It is shown via a stochastic parameterized model that the CEBoosting strategy can be combined with data assimilation to identify regime switching triggered by the unobserved latent processes. In addition, the CEBoosting method is applied to a nonlinear paradigm model for topographic mean flow interaction, demonstrating the online detection of regime switching in the presence of strong intermittency and extreme events.

Adaptive event-triggered mechanism-based online system identification framework for marine craft

Online dynamic model identification is a promising technology for designing the model-based controller of marine crafts. This study confronts the online model identification and maneuvering prediction for marine crafts with an adaptive event-triggered mechanism. In particular, the least square-twin support vector machines algorithm is exploited to identify the dynamic model accurately. Meanwhile, an online adaptive event-triggered identification and prediction framework is proposed to deal with the issue of when to update the dynamic model. Finally, a comparative analysis of the identification and prediction performance between the proposed framework and the non-updated model method is performed. The former superiority is highlighted by experiments based on a marine craft, which shows that it reduces the root mean square error of the yaw rate by up to 45.9%. Thereby, the proposed online identification framework offers great potential for future practical applications of marine crafts.

Online Model Identification for State of Charge Estimation for Lithium-ion Batteries with Missing Data

Online Model Identification for State of Charge Estimation for Lithium-ion Batteries with Missing Data

Robust Navigation Control of a Microrobot With Hysteresis Compensation

Navigation control of microrobots in vivo has great potential in precision medicine and has attracted considerable attention in recent years. The control performance of the existing methods is considerably affected by hysteresis nonlinearity. This article presents a robust control method that can overcome hysteresis influence in navigating a microrobot actuated by an electromagnetic coil system. A motion planner that combines the breadth-first search (BFS) method and genetic algorithm (GA) is used to plan a reliable and flexible trajectory for the microrobot navigation. To compensate for hysteresis nonlinearity existing in the system, the Prandtl–Ishlinskii (PI) model is introduced. A robust controller that integrates adaptive sliding mode control (ASMC) and nonlinear disturbance observer is designed to guarantee the stability and accuracy of the microrobot in motion. Experiments have been performed to demonstrate the effectiveness of the proposed approach. The success of this research will advance the microrobot navigation for in vivo applications. Note to Practitioners—The motivation of this article is to eliminate the effect of hysteresis caused by the electromagnetic system on microrobot motion. Existing research on hysteresis compensation mostly uses inverse model or online model identification, which is not suitable for real-time control of the microrobot. In addition, the disturbance and uncertainty caused by the microfluidic environment at different flow rates will also affect the control performance of the microrobot. To solve the above problems, an adaptive robust control method is developed in this article. Experimental results have shown that the proposed method can successfully control the microrobot in the in vitro and in vivo environment.

On Computer Mouse Pointing Model Online Identification and Endpoint Prediction

This article proposes a new simplified pointing model as a feedback-based dynamical system, including both human and computer sides of the process. It takes into account the commutation between the correction and ballistic phases in pointing tasks. We use the mouse position increment signal from noisy experimental data to achieve our main objectives: to estimate the model parameters online and predict the task endpoint. Some estimation tools and validation results, applying linear regression techniques on the experimental data are presented. We also compare with a similar prediction algorithm to show the potential of our algorithm’s implementation.

Robustness analysis of Adaptive Model Predictive Control for uncertain non-linear dynamic systems using s-gap metric concepts

Robustness analysis of adaptive control systems, when operating in a certain domain, has been a gulf during the past decades. This problem is more complicated in the case of non-linear dynamic systems including un-modelled dynamics as unstructured uncertainty. To find a clear solution for this famous and interesting problem, limitations and effects of controller operation on performance of on-line model identification procedure (and vice versa) must be determined. In this paper, as the main novelty, we show that it needs some developments and new concepts in robust control theory as the s-gap metric, generalized stability margin (GSM) and modifications on the gain bound calculation. These achievements help us to present an on-line identification method with its convergence proof in sense of the s-gap metric and a relation between GSM and identifier convergence area. Therefore, consideration of GSM in Adaptive Model Predictive Control (AMPC) cost function concludes a systematic solution relating controller robustness and adaptivity, clearly. To this aim, a linear matrix inequality (LMI) representation for GSM constraint is suggested. Also, the stability of AMPC on a certain operating domain is guaranteed in sense of the s-gap metric and GSM. All of these help to determine the attraction area of closed loop system and we show that there exists a trade-off between each two cases of the attraction area size, convergence area size and robustness of closed loop control system. Finally, simulations and experimental results imply on correctness of the proposed method.

Explicit adaptive power system stabilizer design based an on-line identifier for single-machine infinite bus

This paper proposes an explicit adaptive controller to damp oscillations and to enhance the single machine infinite bus SMIB stability. Owing to the increasing requests for renewable energy and operating conditions, the identification for power systems has been increased recently. Changes in the power system parameters cause to use an explicit self-tuning control. The controller structure consists of combined on-line identifier and a feedback controller as PID and a radial basis function neural network (RBFNN) which acts as an adaptive power system stabilizer for SMIB. An adaptive linear neural network (ADALINE) depending on the input and output of open loop system is employed as on-line model identification to mimic on-line the SMIB output. The difference between SMIB and the identified model responses is used to adjust the ADALANN model weights on-line depending on a recursive least squares principle RLS and a recursive least square with adaptive directional forgetting RLSMadf. The particles swarm optimization (PSO) beside RLS and RLSMadf assess the weights of (RBFNN) and coefficients of PID controllers depending on the on-line ADALINE model weights. The proposed controller is validated with several operating conditions under various disturbances. The simulation results show the proposed controller whose parameters depend on on-line tuning techniques provides better performance than a conventional PID controller.

Dynamic-decision-based Real-time Dispatch for Reducing Constraint Violations

This paper proposes a dynamic-decision-based real-time dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum. In each decision moment, the following tasks are executed in turn: ① locally linearizing the system model at the current operation point with the online model identification by using measurements; ② narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority; ③ minimizing the generation cost; ④ minimizing the security indicators within their security thresholds. Compared with the existing real-time dispatch strategies, the proposed method can adjust the deviations caused by unpredictable power flow fluctuations, avoid dispatch bias caused by model parameter errors, and reduce the constraint violations in the dispatch decision process. The effectiveness of the proposed method is verified with the IEEE 39-bus system.

Event-Triggered Control for Safety-Critical Systems With Unknown Dynamics

This paper addresses the problem of safety-critical control for multi-agent systems with unknown dynamics in unknown environments. It has been shown that stabilizing affine control systems to desired (sets of) states while optimizing quadratic costs subject to state and control constraints can be reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in this approach is obtaining accurate system dynamics of all components in the system, which is especially difficult when online model identification is required given limited computational resources and system data. We address this problem by proposing a robust framework (to unknown dynamics including uncertainties) through defining adaptive affine control dynamics that are updated based on the error states obtained by real-time sensor measurements. We define a CBF for a safety requirement on the unmodelled agents based on the adaptive dynamics and error states, and reformulate the safety-critical control problem as the above mentioned sequence of QPs. Then we determine a set of events that trigger the QPs and ensure safety when solving them. We also derive a condition that guarantees the satisfaction of a CBF constraint between events. The proposed framework can also be used for state convergence guarantees for systems with unknown dynamics based on CLFs. We illustrate the effectiveness of the proposed framework on a robot control problem, an adaptive cruise control problem and a traffic merging problem using autonomous vehicles. We also compare the proposed event-driven method with the classical time-driven approach.

Online Model Identification for State of Charge Estimation of Lithium-Ion Battery with Missing Data

Online Model Identification for State of Charge Estimation of Lithium-Ion Battery with Missing Data

Online identification of a link function degradation model for solid oxide fuel cells under varying-load operation

Prognostic is a potential tool for improving the durability of solid oxide fuel cells (SOFCs), which usually involves building a degradation model for prediction. However, the existing degradation models based on parallel constant operation datasets are inaccurate for integration with operation optimization and control problems of SOFCs under varying-load operation due to the nonuniform degradation behaviors. To address this issue, a link function degradation model is proposed, and its parameters are identified online with a cyclic batch identification procedure based on the maximum likelihood method, which provides results representing the degradation trend on a timescale of 103 h. The link function takes the form of an empirical function, which describes how operating parameters affect the degradation and is easy to integrate with control designs. The existence of the link function is proven on the varying-load experiment datasets of two flat-chip SOFCs because it statistically improves the prediction accuracy and stability compared with a constant degradation speed model. Furthermore, the effectiveness of the proposed identification procedure for time-varying degradation behaviors on the timescale of 104 h is also validated with 30,000-h simulation datasets.

Cholesky Factorization Based Online Sequential Multiple Kernel Extreme Learning Machine Algorithm for a Cement Clinker Free Lime Content Prediction Model

Aiming at the difficulty in real-time measuring and the long offline measurement cycle for the content of cement clinker free lime (fCaO), it is very important to build an online prediction model for fCaO content. In this work, on the basis of Cholesky factorization, the online sequential multiple kernel extreme learning machine algorithm (COS-MKELM) is proposed. The LDLT form Cholesky factorization of the matrix is introduced to avoid the large operation amount of inverse matrix calculation. In addition, the stored initial information is utilized to realize online model identification. Then, three regression datasets are used to test the performance of the COS-MKELM algorithm. Finally, an online prediction model for fCaO content is built based on COS-MKELM. Experimental results demonstrate that the fCaO content model improves the performance in terms of learning efficiency, regression accuracy, and generalization ability. In addition, the online prediction model can be corrected in real-time when the production conditions of cement clinker change.

Online Model Identification and Updating in Multi-Platform Pseudo-Dynamic Simulation of Steel Structures – Experimental Applications

ABSTRACT A new technique is proposed for online model identification and updating in multi-platform pseudo-dynamic simulation of steel structures which brings novelty through eliminating the need for optimization during the identification stage of material/section behavior. Furthermore, the proposed procedure requires no prior information about the updated component of the numerical substructure and free of backward inconsistency. The efficiency of the proposed online model updating procedure is investigated by fourteen simulations which have real physical experimental substructures. A three span, small-scale bridge model with different pier heights is selected as the investigated structure. The simulations are performed under two different types of dynamic loads: instantaneous constant loading and strong ground motion records. The repeatability of tests, the effects of strong ground motion records and different pier heights are investigated. Furthermore completely numerical dynamic analyses with advanced models are also performed for comparison purposes. The results demonstrated that the proposed online model identification and updating procedure can be used with sufficient precision in multi-platform pseudo-dynamic simulations of steel structures.

A Novel Method for State of Charge Estimation of Lithiumion Batteries using Embedded Cubature Kalman Filter

The accurate estimation of state of charge (SOC) of Lithium-ion battery is one of the most crucial issues for battery management system (BMS). This paper proposes a novel method for SOC estimation using the embedded cubature Kalman filter (ECKF). ECKF computes the cubature points with a weight depends on an optional parameter, which differs from the standard cubature Kalman filter (CKF). An online model identification method for a second-order RC networks equivalent circuit model using the forgetting factor recursive least squares (FRLS) algorithm has been presented. The Dynamic cycles are used to assess the superiority in improving the accuracy and stability of proposed method compared with the widely used algorithms. Experimental results show that, with 20% initial SOC error, the maximum estimation error is within 1%, which indicates that the proposed ECKF method has higher estimation accuracy and robustness for SOC estimation.

Wide-area power oscillation damper for DFIG-based wind farm with communication delay and packet dropout compensation

Data dropouts and time delays introduced in communication network would degrade the damping performance of the wide-area power oscillation dampers (WPODs) or may even cause instability of the closed-loop system. This paper establishes a data transmission model in the network control system (NCS) and proposes a networked predictive control (NPC) based WPOD of doubly-fed induction generator (DFIG)-based wind farm to damp inter-area oscillations in a large-scale interconnected power system. The major advantage of the proposed WPOD is that it can actively compensate packet dropouts and communication delays involved in wide-area control channels. Additionally, online model identification is employed to handle the parameter uncertainties and operating condition variations of the power system. Case studies are undertaken on the 16-machine 68-bus power system. Simulation results show that the proposed WPOD can provide better damping performances than those of the conventional WPOD over a wide range of operating conditions and different communication delays and packet dropouts.

On-line model identification for the machining process based on multirate process data

Accurate identification of the model parameters of the machining process based on on-line process data is a crucial prerequisite for its model-based control and diagnostics. A typical machining process generates multi-output and multirate data streams. Whereas various sensors provide in-process information about the process, many important process outcomes including product qualities can be only measured in postprocess manner. This paper proposes to improve the identification by using both in-process and postprocess data and by analyzing the identifiability of model parameters. The identification of the model parameters based on multirate output is formulated using the maximum-likelihood estimation and the Fisher information matrix for a multirate-sampled system is derived to study the identifiability of model parameters. A strategy is developed to improve accuracy and robustness of the model identification considering the identifiability. The proposed method is tested on two batches of multirate process data from the cylindrical grinding process. The test results demonstrate using both in-process and postprocess data improves the identifiability and the proposed identification strategy results in improved prediction performance.

Enhanced online model identification and state of charge estimation for lithium-ion battery under noise corrupted measurements by bias compensation recursive least squares

In online battery model identification by recursive least squares (RLS), the identification biases are generated by the noises in the voltage and current measurements, further resulting in the accuracy degradation of model-based state of charge (SOC) estimation. Firstly, the detailed formula derivation presents the relationship between noise variances and identification biases in least squares. Then, through the practical identification on a general battery model, the consistent results from the formulas and simulations both adequately and quantitatively verify that the model identified by RLS is biased, when either only one of voltage and current measurements or both are corrupted by noises. To further assess the noise effects on SOC and parameter estimations, a conventional co-estimation algorithm joining RLS and extended Kalman filter (EKF) is applied into the simulations and experiments especially under noise corrupted measurements, the numerical results show that the estimation accuracy degradation generated by noises is quite considerable. Hence, bias compensation RLS and EKF co-estimation algorithms are proposed to alleviate the impact of the noises. Simulation and experiment studies show that the proposed algorithms can compensate the model identification biases caused by noises and can enhance SOC estimation accuracy under noise corrupted measurements.

A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms

Abstract Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many manufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of multiple objective functions related to process performance and cost is necessary. In this work, a multi-objective optimal experimental design framework is proposed to enhance the efficiency of online model-identification platforms. The proposed framework permits flexibility in the choice of trade-off experimental design solutions, which are calculated online—that is, during the execution of experiments. The application of this framework to improve the online identification of kinetic models in flow reactors is illustrated using a case study in which a kinetic model is identified for the esterification of benzoic acid and ethanol in a microreactor.