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Abstract Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness.

Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness. [ABSTRACT FROM AUTHOR]

It is known that analytical manipulation of the time-domain representation of fractional order transfer function is, hitherto, a challenging task. In this study, a controller design methodology based on the direct synthesis design method is proposed using bi-fractional order transfer function as a reference model and certain time-domain criteria. First, an analysis examines the effects of bi-fractional order transfer function parameters, that is, commensurate fractional order, damping ratio and natural frequency, on the system time-domain criteria. This examination has allowed us to set up a relation between damping ratio and commensurate fractional order. Next, a polynomial function fitting is established in order to express the damping ratio in terms of this commensurate order. Bi-fractional-order reference model is regenerated using the above-mentioned relationship. Furthermore, a control design algorithm that utilizes the newly derived bi-fractional order reference model is developed by considering control signal limitations. The proposed fractional order control design method is then compared with globally optimized fractional order proportional-integral-derivative (PID) controllers under the same circumstances and performance index. Finally, the implementation of the proposed controller design algorithm is done on a real-time active suspension system. The results are very satisfactory and incoherent with the simulations. [ABSTRACT FROM AUTHOR]

Model design and motion control are considered the cornerstones of the robotic field that allow for achieving performance tasks. This article proposes a new dynamic modeling and control approach for very lightweight mechanical systems carrying payloads. The selection of the model and the design of the control are elaborated on using a fractional order framework under different conditions. The use of fractional order calculus is justified by the better performance that reveals a fractional order model compared to an integer order model of similar complexity. The mechanical structure of very lightweight manipulators has vibrations that impede the accurate positioning of their end effector. Moreover, they have actuators with high friction and sensors to measure the vibrations, which often are strain gauges, that have offset and high-frequency noise. All these mentioned problems might degrade the mechanical system's performance. Hence, to overcome these inconveniences, two nested-loop controls are examined: an inner loop that controls the motor dynamics and removes the friction effects and an outer loop implemented to eliminate the beam vibrations by adapting the input-state feedback linearization technique. Then, we propose a new fractional order control scheme that (1) removes the strain gauge offset disturbances, (2) reduces the risk of the actuator's saturation caused by the high-frequency noise of strain gauges and (3) reduces the dynamic effects of huge payload changes. We prove that our fractional controller has enhanced robustness with respect to the above-mentioned problems. Finally, the investigated approach is validated experimentally by applying it to a lightweight robot mounted on an air table. [ABSTRACT FROM AUTHOR]

Forecasting the dynamical behaviors of nonlinear systems over long time intervals represents a great challenge for scientists and has become a very active area of research. The employment of the well-known artificial recurrent neural networks (RNNs)-based models requires a high computational cost, and they usually maintain adequate accuracy for complicated dynamics over short intervals only. In this work, an efficient reservoir-computing (RC) approach is presented to predict the time evolution of the complicated dynamics of a fractional order hyperchaotic finance model. Compared with the well-known deep learning techniques, the suggested RC-based forecasting model is faster, more accurate for long-time prediction, and has a smaller execution time. Numerical schemes for fractional order systems are generally time-consuming. The second goal of the present study is to introduce a faster, more efficient, and simpler simulator to the fractional order chaotic/hyperchaotic systems. The RC model is utilized in a proposed RC-based digital image encryption scheme. Security analysis is carried out to verify the performance of the proposed encryption scheme against different types of statistical, KPA, brute-force, CCA, and differential attacks. [ABSTRACT FROM AUTHOR]

Model design and motion control are considered the cornerstones of the robotic field that allow for achieving performance tasks. This article proposes a new dynamic modeling and control approach for very lightweight mechanical systems carrying payloads. The selection of the model and the design of the control are elaborated on using a fractional order framework under different conditions. The use of fractional order calculus is justified by the better performance that reveals a fractional order model compared to an integer order model of similar complexity. The mechanical structure of very lightweight manipulators has vibrations that impede the accurate positioning of their end effector. Moreover, they have actuators with high friction and sensors to measure the vibrations, which often are strain gauges, that have offset and high-frequency noise. All these mentioned problems might degrade the mechanical system’s performance. Hence, to overcome these inconveniences, two nested-loop controls are examined: an inner loop that controls the motor dynamics and removes the friction effects and an outer loop implemented to eliminate the beam vibrations by adapting the input-state feedback linearization technique. Then, we propose a new fractional order control scheme that (1) removes the strain gauge offset disturbances, (2) reduces the risk of the actuator’s saturation caused by the high-frequency noise of strain gauges and (3) reduces the dynamic effects of huge payload changes. We prove that our fractional controller has enhanced robustness with respect to the above-mentioned problems. Finally, the investigated approach is validated experimentally by applying it to a lightweight robot mounted on an air table.

Forecasting the dynamical behaviors of nonlinear systems over long time intervals represents a great challenge for scientists and has become a very active area of research. The employment of the well-known artificial recurrent neural networks (RNNs)-based models requires a high computational cost, and they usually maintain adequate accuracy for complicated dynamics over short intervals only. In this work, an efficient reservoir-computing (RC) approach is presented to predict the time evolution of the complicated dynamics of a fractional order hyperchaotic finance model. Compared with the well-known deep learning techniques, the suggested RC-based forecasting model is faster, more accurate for long-time prediction, and has a smaller execution time. Numerical schemes for fractional order systems are generally time-consuming. The second goal of the present study is to introduce a faster, more efficient, and simpler simulator to the fractional order chaotic/hyperchaotic systems. The RC model is utilized in a proposed RC-based digital image encryption scheme. Security analysis is carried out to verify the performance of the proposed encryption scheme against different types of statistical, KPA, brute-force, CCA, and differential attacks.

Structural identifiability analysis of fractional-order equivalent circuit models (FO-ECMs) obtained through electrochemical impedance spectroscopy (EIS) remains as a challenging problem. Aside from practical challenges such as measurement noise and the selection of excitation signals, no widely accepted analytical or numerical proof addressing the structural identifiability of impedance spectroscopy FO-ECMs exists. Through the use of coefficient mapping technique, this paper proposes novel computationally-efficient algebraic equations for numerical structural identifiability analysis of a widely used FO-ECM with Grünwald–Letnikov fractional derivative approximation and two constant phase elements (CPEs) including the Warburg term. The effects of the length of the data and sampling time on the proposed method for the structural identifiability analysis of the two-CPE FO-ECMs are discussed along with two examples and a discussion of the results. [ABSTRACT FROM AUTHOR]

In order to solve some analysis or control problems for fractional order models, integer order approximations are often used. However, in many works, approximation error is not taken into account, leading to results that cannot be guaranteed for the initial fractional order model. The objective of the paper is thus to provide a new methodology that takes into account approximation error and leads to rewriting the fractional order model as an uncertain integer order model. [ABSTRACT FROM AUTHOR]

In previous decades, it has been observed that many physical systems are well characterised by fractional order models. Hence, their identification is attracting more and more interest of the scientific community. However, they pose a more difficult identification problem, which requires not only the estimation of model coefficients but also the determination of fractional orders with the tedious calculation of fractional order derivatives. This study focuses on an identification scheme, in the time domain, of dynamic systems described by linear fractional order differential equations. The proposed identification method is based on the recursive least squares algorithm applied to an ARX structure derived from the linear fractional order differential equation using adjustable fractional order differentiators. The basic ideas and the derived formulations of the identification scheme are presented. Illustrative examples are presented to validate the proposed linear fractional order system identification approach. [ABSTRACT FROM AUTHOR]

In this work we present the construction and evaluation of recursive Bayesian estimation algorithms for human motion tracking using fractional order models. The presented schemes include both fixed fractional order as well as variable order models with implicit and explicit model order estimation schemes. The performance of the designed techniques is compared to a set of conventional integral order methods using several measurements of human hand and head motions. [ABSTRACT FROM PUBLISHER]