This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.

Data-Based Techniques Focused on Modern Industry: An Overview

In this paper, an adaptive fault diagnosis technique is used in Li-ion batteries. The diagnosis process consists of multiple nonlinear models representing signature faults, such as overcharge and overdischarge, causing significant model parameter variation. The impedance spectroscopy of a Li-ion $(\hbox{LiFePO}_{4})$ cell is used, along with the equivalent circuit methodology, to construct nonlinear battery signature-fault models. Extended Kalman filters are utilized to estimate the terminal voltage of each model and to generate residual signals. The residual signals are used in the multiple-model adaptive estimation technique to generate probabilities that determine the signature faults. It can be seen that, by using this method, signature faults can be detected accurately, thus providing an effective way of diagnosing Li-ion battery failure.

/pdf/adaptive-nonlinear-model-based-fault-diagnosis-of-li-ion-5dqdt2q433.pdf

Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries

Adaptive Nonlinear Model-Based Fault Diagnosis of Li-ion Batteries

To effectively reject the influence of gyroscopic effects and gimbal movement on system stability and precision for a single-gimbal magnetically suspended control moment gyro (SGMSCMG), this paper proposes a new control strategy combining an inverse system method and internal model control. The SGMSCMG is modeled using both analytic relationships and experimental data, and its inversion is implemented in a current-mode approach. The common problems associated with the current-mode linearization are successfully handled via compensation filters and internal model controllers. The effectiveness and superiority of this proposed strategy have been demonstrated by both simulation and experimental results.

High-Precision Control for a Single-Gimbal Magnetically Suspended Control Moment Gyro Based on Inverse System Method

To radically eliminate the influence of gyroscopic effects on system stability and to improve the performances of high-precision, fast-response for the high-speed magnetically suspended rotor system in a control moment gyro, this paper proposes a control strategy that combines inverse system method and internal model control. The stability and robustness problems induced by current-mode linearization have been successfully solved by introducing phase-lead compensation and internal model controller. The effectiveness and superiority of the proposed strategy have been demonstrated by both simulation and experimental results.

Decoupling Control of Magnetically Suspended Rotor System in Control Moment Gyros Based on an Inverse System Method

Adaptive internal model control with application to fueling control

A combination of the multiple model adaptive estimation method (MMAE) with an extended Kalman filter (EKF) is considered. The ability of the MMAE method to detect faults based on a predefined hypothesis and the parameter-estimating ability of an EKF results in a very efficient fault detection approach. This so-called extended multiple model adaptive estimation method (EMMAE) has been investigated on a nonlinear model of an aircraft. The results show that it is a very capable method to detect faults of various types.

Extended Multiple Model Adaptive Estimation for the Detection of Sensor and Actuator Faults

A new tuning method for internal model controllers (IMCs) is presented. The parameters of an IMC can be structurally assigned to two groups: 1) parameters of the internal model and 2) parameters of the controller. The method described in this brief suggests a sequential tuning of the two parameter groups. For both groups, the parameter values are found by minimizing a predefined cost function. The optimization is run with a gradient-based minimization procedure where, analogously to the well-known iterative feedback tuning (IFT) scheme, the gradients are computed from signals obtained from closed-loop experiments. Thus, for the calculation of the gradient, the unknown plant is utilized, whereas other ?local? tuning methods suggest the replacement of the real plant by its model to calculate the gradient. The main advantages of the suggested algorithm are its inherent operation in the closed control loop and the fact that, for the tuning of the internal model, no information about the disturbance model is required. The method can be used either for an initial tuning of the controller or for autotuning during operation.

Iterative Tuning of Internal Model Controllers With Application to Air/Fuel Ratio Control

Iterative adaptive air/fuel ratio control

This thesis presents the design of an adaptive air/fuel ratio control system incorporating a λ sensor with varying dynamical properties. As a baseline, the design of a nominal non-adaptive air/fuel ratio controller based on the internal model control principle is presented. For the adaptation of the λ sensor dynamics two different adaptive methods tailormade for the internal model control structure have been developed. The first method suggests a sequential tuning of the parameters of the internal model and the internal controller, respectively. For both parameter groups the parameter values are found by minimizing a predefined cost function. The optimization is run with a gradient-based minimization procedure where, analogously to the well-known iterative feedback tuning scheme, the gradients are computed from signals obtained from closed-loop experiments. Thus for the calculation of the gradient, the unknown plant is utilized, whereas other “local” tuning methods suggest to replace the real plant by its model to calculate the gradient. The suggested algorithm inherently operates under closed loop conditions and allows for the different roles of the disturbances that occur during the two tuning steps. As a consequence of this, the adapted internal model can be shown to be unbiased whereas for the tuning of the internal controller the disturbances are rightly taken into account. The second concept is based on direct adaptation of the internal model. To that end, the internal model is formulated in an output-error identification model structure that can be adapted by a linear regression on filtered versions of the input and output signals of the internal model. The parameter vector of the regression is adapted by using a standard adaptive law. The output-error approach has advantageous properties concerning the parameter convergence and the control performance when disturbances are present. To guarantee the global stability of the adaptive systems, the denominator polynomial of the transfer function of the unknown plant needs to be known, however, it is shown in this thesis that only a rough initial internal model in the control-relevant frequency range is necessary for stability. Both methods are described in a general framework, such that, they can

Model-based adaptive air/fuel ratio control for an automotive gasoline engine

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