A Laguerre Predictive Functional control (LPFC) is a simple input shaping method, which can improve the prediction consistency and closed-loop performance of the conventional approach (PFC). However, it is well-known ...
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A Laguerre Predictive Functional control (LPFC) is a simple input shaping method, which can improve the prediction consistency and closed-loop performance of the conventional approach (PFC). However, it is well-known that an input shaping method, in general, will affect the loop sensitivity of a system. Hence, this paper presents a formal sensitivity analysis of LPFC by considering the effect of noise, unmeasured disturbance and parameter uncertainty. Sensitivity plots from bode diagrams and closed-loop simulation are used to illustrate the controller robustness and indicate that although LPFC often provides a better closed-loop tracking response and disturbance rejection, this may involve some trade-off with the sensitivity to noise and parameter uncertainty. Finally, to validate the practicality of the results, the sensitivity of the LPFC control law is illustrated on real-time laboratory hardware.
This work proposes an improved method for Predictive Functional control (PFC) to handle an integrating process. Instead of assuming a constant future input, the dynamic is shaped with a first-order Laguerre polynomial...
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This work proposes an improved method for Predictive Functional control (PFC) to handle an integrating process. Instead of assuming a constant future input, the dynamic is shaped with a first-order Laguerre polynomial so that it converges to the expected steady state value. This modification provides simpler coding and tuning compared to the conventional method in the literature. Simulation results show that the proposed controller improves the consistency of the open-loop prediction of an integrating process and thus improves closed-loop performance and constraint handling properties. The practicality of this algorithm is also validated on laboratory hardware.
In this work, discrete-time extremum seeking algorithms for unconstrained optimization problems are developed. A general class of non-commutative maps and one- and two point function evaluation polices are presented t...
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In this work, discrete-time extremum seeking algorithms for unconstrained optimization problems are developed. A general class of non-commutative maps and one- and two point function evaluation polices are presented to approximate a gradient-descent algorithm, suitable for extremum seeking problems. Moreover, adaptive step size rules are discussed to achieve faster convergence and vanishing steady state oscillations.
The objective of this study is to determine an optimal substrate feed rate for optimizing the fructo-oligosaccharide production by Aureobasidium pullulans in a fed batch reactor. The feed profile is charecterized by t...
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This paper proposes a dynamic and decentralized fault detection method. The plant is divided in groups whose members are selected using linear and non-linear modelling techniques. In each group a Principal Component A...
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This paper proposes a dynamic and decentralized fault detection method. The plant is divided in groups whose members are selected using linear and non-linear modelling techniques. In each group a Principal Component Analysis model does the fault detection, including delayed data to get a dynamic method. Then, a central node fuses the results of each group, using Bayesian Index Criterion (BIC), to get a global detection outcome. The method was tested on a widely used benchmark and compared with other proposal to check its effectiveness.
A Laguerre Predictive Functional control (LPFC) is a simple input shaping method, which can improve the prediction consistency and closed-loop performance of the conventional approach (PFC). However, it is well-known ...
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In this work, a heuristic as operational tool to estimate the lactate threshold and to facilitate its integration into the training process of recreational runners is proposed. To do so, we formalize the principles fo...
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The fault diagnosis scheme of the rotor bearing system often conducted by using either signal analysis approach or modeling method. In practice, the structure of the rotor bearing system is complex and contains many n...
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The fault diagnosis scheme of the rotor bearing system often conducted by using either signal analysis approach or modeling method. In practice, the structure of the rotor bearing system is complex and contains many nonlinear factors. Therefore, it is hard to use the model-based method for fault detection. Thus, signal analysis approach is more efficient. In the signal analysis approach, frequency response function is widely applied. However, the existing analyzing methods of frequency response function have some limitations, such as multidimensional property. Thus, in this study, the concept of Nonlinear Response Spectrum Function(NRSF) is proposed to solve the problem. Finally, a simulation is conducted to identify the multi-fault rotor bearing systems by the proposed NRSFs feature and Support Vector Machine(SVM) classifier, showing that the NRSF-SVM approach has an excellent performance in fault identification of rotor bearing system.
Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. This paper presents a framework using Gaussian process (GP) ...
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Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. This paper presents a framework using Gaussian process (GP) to fuse SAR images of different modalities and to segment dark areas (assumed oil spill) for oil spill detection. A new covariance function; a product of an intrinsically sparse kernel and a Rational Quadratic Kernel (RQK) is used to model the prior of the estimated image allowing information to be transferred. The accuracy performance evaluation demonstrates that the proposed framework has 37% less RMSE per pixel and a compelling enhancement visually when compared with existing methods.
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