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1 – 10 of over 1000The flexibility of batch process enables its wide application in fine-chemical, pharmaceutical and semi-conductor industries, whilst its complexity necessitates control…
Abstract
Purpose
The flexibility of batch process enables its wide application in fine-chemical, pharmaceutical and semi-conductor industries, whilst its complexity necessitates control performance monitoring to ensure high operation efficiency. This paper proposes a data-driven approach to carry out controller performance monitoring within batch based on linear quadratic Gaussian (LQG) method.
Design/methodology/approach
A linear time-varying LQG method is proposed to obtain the joint covariance benchmark for the stochastic part of batch process input/output. From historical golden operation batch, linear time-varying (LTV) system and noise models are identified based on generalized observer Markov parameters realization.
Findings
Open/closed loop input and output data are applied to identify the process model as well as the disturbance model, both in Markov parameter form. Then the optimal covariance of joint input and output can be obtained by the LQG method. The Hotelling's Tˆ2 control chart can be established to monitor the controller.
Originality/value
(1) An observer Markov parameter approach to identify the time-varying process and noise models from both open and closed loop data, (2) a linear time-varying LQG optimal control law to obtain the optimal benchmark covariance of joint input and output and (3) a joint input and output multivariate control chart based on Hotelling's T2 statistic for controller performance monitoring.
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Hui Shao, Zhi Xiong, Jianxin Xu, Bing Hua and Song Han
The federated filter created by Carlson has been widely used in multi-sensor integrated navigation. Compared with no-reset federated filter, the reset mode has greater…
Abstract
Purpose
The federated filter created by Carlson has been widely used in multi-sensor integrated navigation. Compared with no-reset federated filter, the reset mode has greater sub-filters’ performance, but faults of any subsystem would affect other healthy subsystems via global fusion and the sub-optimality of sub-filters’ estimation has influence on fault detection sensitivity. It’s a challenge to design a robust reset federated filter.
Design/methodology/approach
The time-varying observation noise is designed to reduce proportions of observation information in faulty sub-filters. A new dynamic information distribution algorithm based on optimal residual chi-square detection function is presented to reduce proportions of faulty sub-filters’ estimation in information fusion filter.
Findings
The robust filtering algorithm represents a filtering strategy for reset federated filter. Compared with fault isolation, the navigation result is smoother by using this algorithm. It has significant benefits in avoiding faulty sensors’ contamination and the performance of federated filter is greatly improved.
Research limitations/implications
The approach described in this paper provides a new method to deal with federated reset filter’s faulty problems. This new robust federated filter algorithm possesses a great potential for various applications.
Practical implications
The approach described in this paper can be used in multi-sensor integrated navigation with no fewer than three sensors.
Originality/value
Compared with conventional approach of fault isolation, the proposed algorithm does not destroy the continuity and integrity of the filtering process. It improves the performance of the federated filter by reducing proportions of faulty observation information. It also reduces the influence of sub-optimality on fault detection sensitivity.
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An identification scheme to identify interconnected discrete-time (DT) varying systems.
Abstract
Purpose
An identification scheme to identify interconnected discrete-time (DT) varying systems.
Design/methodology/approach
The purpose of this paper is the identification of interconnected discrete time varying systems. The proposed technique permits the division of global system to many subsystems by building a vector observation of each subsystem and then using the gradient method to identify the time-varying parameters of each subsystem. The convergence of the presented algorithm is proven under a given condition.
Findings
The effectiveness of the proposed technique is then shown with application to a simulation example.
Originality/value
In the past decade, there has been a renewed interest in interconnected systems that are multidimensional and composed of similar subsystems, which interact with their closest neighbors. In this context, the concept of parametric identification of interconnected systems becomes relevant, as it considers the estimation problem of such systems. Therefore, the identification of interconnected systems is a challenging problem in which it is crucial to exploit the available knowledge about the interconnection structure. For time-varying systems, the identification problem is much more difficult. To cope with this issue, this paper addresses the identification of DT dynamical models, composed by the interconnection of time-varying systems.
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The purpose of this paper is to propose a new control strategy based on adaptive inverse control aiming at high performance control of permanent magnet synchronous motor (PMSM).
Abstract
Purpose
The purpose of this paper is to propose a new control strategy based on adaptive inverse control aiming at high performance control of permanent magnet synchronous motor (PMSM).
Design/methodology/approach
This scheme adopts the vector control with double closed-loop structure and introduces a multi-dimensional Taylor network (MTN) inverse control method into velocity-loop. First, the invertibility of PMSM’s mathematical model is proved. Second, a novel dynamic network (MTN) is presented, which has simple structure and faster computing speed. Besides, to realize the high-precision speed control, three MTNs are applied to achieve system modeling, inverse modeling and noise disturbance elimination which correspond to the function of the adaptive identifier, adaptive feed-forward controller and nonlinear adaptive filter, respectively.
Findings
This scheme is designed with the full consideration of the PMSM’s particularity. For the PMSM’s unknown dynamics and time-varying characteristics, the variable forgetting factor recursive least squares algorithm is adopted to improve identification ability, and the weight-elimination algorithm is used to remove redundant regression items in the MTN identifier and inverse controller. In addition, to reduce the influence arose from measurement noise and other stochastic factors, adaptive MTN filter is introduced to eliminate noise disturbance. The computational results show that the proposed scheme possesses excellent control performance and better robustness against the load disturbance.
Originality/value
The paper presents a new inverse control scheme with MTN which is practical and flexible, and the MTN-based control system is very promising for real-time applications.
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I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to…
Abstract
I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.
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Gonzalo Garcia, Shahriar Keshmiri and Thomas Stastny
Nonlinear model predictive control (NMPC) is emerging as a way to control unmanned aircraft with flight control constraints and nonlinear and unsteady aerodynamics. However, these…
Abstract
Purpose
Nonlinear model predictive control (NMPC) is emerging as a way to control unmanned aircraft with flight control constraints and nonlinear and unsteady aerodynamics. However, these predictive controllers do not perform robustly in the presence of physics-based model mismatches and uncertainties. Unmodeled dynamics and external disturbances are unpredictable and unsteady, which can dramatically degrade predictive controllers’ performance. To address this limitation, the purpose of this paper is to propose a new systematic approach using frequency-dependent weighting matrices.
Design/methodology/approach
In this framework, frequency-dependent weighting matrices jointly minimize closed-loop sensitivity functions. This work presents the first practical implementation where the frequency content information of uncertainty and disturbances is used to provide a significant degree of robustness for a time-domain nonlinear predictive controller. The merit of the proposed method is successfully verified through the design, coding, and numerical implementation of a robust nonlinear model predictive controller.
Findings
The proposed controller commanded and controlled a large unmanned aerial system (UAS) with unsteady and nonlinear dynamics in the presence of environmental disturbances, measurement bias or noise, and model uncertainties; the proposed controller robustly performed disturbance rejection and accurate trajectory tracking. Stability, performance, and robustness are attained in the NMPC framework for a complex system.
Research limitations/implications
The theoretical results are supported by the numerical simulations that illustrate the success of the presented technique. It is expected to offer a feasible robust nonlinear control design technique for any type of systems, as long as computational power is available, allowing a much larger operational range while keeping a helpful level of robustness. Robust control design can be more easily expanded from the usual linear framework, allowing meaningful new experimentation with better control systems.
Originality/value
Such algorithms allows unstable and unsteady UASs to perform reliably in the presence of disturbances and modeling mismatches.
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M. Majeed and Indra Narayan Kar
The purpose of this paper is to estimate aerodynamic parameters accurately from flight data in the presence of unknown noise characteristics.
Abstract
Purpose
The purpose of this paper is to estimate aerodynamic parameters accurately from flight data in the presence of unknown noise characteristics.
Design/methodology/approach
The introduced adaptive filter scheme is composed of two parallel UKFs. At every time‐step, the master UKF estimates the states and parameters using the noise covariance obtained by the slave UKF, while the slave UKF estimates the noise covariance using the innovations generated by the master UKF. This real time innovation‐based adaptive unscented Kalman filter (UKF) is used to estimate aerodynamic parameters of aircraft in uncertain environment where noise characteristics are drastically changing.
Findings
The investigations are initially made on simulated flight data with moderate to high level of process noise and it is shown that all the aerodynamic parameter estimates are accurate. Results are analyzed based on Monte Carlo simulation with 4000 realizations. The efficacy of adaptive UKF in comparison with the other standard Kalman filters on the estimation of accurate flight stability and control derivatives from flight test data in the presence of noise, are also evaluated. It is found that adaptive UKF successfully attains better aerodynamic parameter estimation under the same condition of process noise intensity changes.
Research limitations/implications
The presence of process noise complicates parameter estimation severely. Since the non‐measurable process noise makes the system stochastic, consequently, it requires a suitable state estimator to propagate the states for online estimation of aircraft aerodynamic parameters from flight data.
Originality/value
This is the first paper highlighting the process noise intensity change on real time estimation of flight stability and control parameters using adaptive unscented Kalman filter.
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The purpose of this article is to examine the proposition that the benefits from environmental improvements accrue disproportionately to the rich.
Shijie Dai, Wenhua Zhang, Wenbin Ji, Yufeng Zhao, Hongwei Zheng, Jiaheng Mu, Pengwei Li and Riqing Deng
Considering the influence of environmental noise and modeling error during the process of the robotic automatic grinding aero-engine blade, this study aims to propose a method…
Abstract
Purpose
Considering the influence of environmental noise and modeling error during the process of the robotic automatic grinding aero-engine blade, this study aims to propose a method based on the extended state observer (ESO) to reduce the fluctuation of normal grinding force.
Design/methodology/approach
First, the measurement range of the six-dimensional force sensor is calibrated according to the maximum acceleration of end-effector and grinding force. Second, the gravity and zero drift compensation model is built to compensate for measurement error. Finally, the switching function is designed based on the difference between the expected grinding force and the actual feedback value. When the value of function stays within the switching band, a nonlinear active disturbance rejection control (ADRC) loop is applied. When the function value reaches outside the switching band, an ESO-based sliding mode control (SMC) loop is applied.
Findings
The simulated and experimental results show that the proposed control method has higher robustness compared with proportion-integral-derivative (PID), Fuzzy PID and ADRC.
Research limitations/implications
The processing parameters of this paper are obtained based on the single-factor experiment without considering the correlation between these variables. A new control strategy is proposed, which is not only used to control the grinding force of blades but also promotes the development of industrial control.
Originality/value
ESO is used to observe environmental interference and modeling errors of the system for real-time compensation. The segment control method consisting of ESO-based SMC and ESO-based ADRC is designed to improve the robustness. The common application of the two parts realizes suppression of fluctuation of grinding force.
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Youshuang Ding, Xi Xiao, Xuanrui Huang and Jiexiang Sun
This paper aims to propose a novel system identification and resonance suppression strategy for motor-driven system with high-order flexible manipulator.
Abstract
Purpose
This paper aims to propose a novel system identification and resonance suppression strategy for motor-driven system with high-order flexible manipulator.
Design/methodology/approach
In this paper, first, a unified mathematical model is proposed to describe both the flexible joints and the flexible link system. Then to suppress the resonance brought by the system flexibility, a model based high-order notch filter controller is proposed. To get the true value of the parameters of the high-order flexible manipulator system, a fuzzy-Kalman filter-based two-step system identification algorithm is proposed.
Findings
Compared to the traditional system identification algorithm, the proposed two-step system identification algorithm can accurately identify the unknown parameters of the high order flexible manipulator system with high dynamic response. The performance of the two-step system identification algorithm and the model-based high-order notch filter is verified via simulation and experimental results.
Originality/value
The proposed system identification method can identify the system parameters with both high accuracy and high dynamic response. With the proposed system identification and model-based controller, the positioning accuracy of the flexible manipulator can be greatly improved.
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