The incremental stability analysis of Lurie systems consisting of the feedback interconnection between a linear time-invariant (LTI) system and a slope-bounded nonlinearity is considered. We first show that the increm...
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ISBN:
(数字)9783907144107
ISBN:
(纸本)9798331540920
The incremental stability analysis of Lurie systems consisting of the feedback interconnection between a linear time-invariant (LTI) system and a slope-bounded nonlinearity is considered. We first show that the incremental input-output mappings generated by the set of slope-bounded nonlinearities satisfy a set of biased integral quadratic constraints (IQCs) defined by Popov multipliers. Then, a frequency-domain inequality (FDI) condition on the LTI system is proposed for establishing incremental closed-loop stability via an incremental form of IQC theory. Application of the KYP lemma yields an equivalent linear matrix inequality (LMI) condition.
To study the effects of uncertainty in autonomous motion planning and control, an 8-DOF model of a tractor-semitrailer is implemented and analyzed. The implications of uncertainties in the model are then quantified an...
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With the new feature MATLAB Compiler™ available starting R2020b Matlab, an application developed in Matlab can be packaged as docker standalone application and deployed using Docker® container service. The target...
With the new feature MATLAB Compiler™ available starting R2020b Matlab, an application developed in Matlab can be packaged as docker standalone application and deployed using Docker® container service. The target system running the standalone application must be a Linux distribution and requires a MATLAB® Runtime installation and an active docker service. The proposed standalone application is a PV simulator GUI programmatically implemented in Matlab based on two photovoltaic (PV) panels’ data sheets. The obtained PV simulator is launched in both MATLAB® environment, where initial was designed and externally in a Linux environment as a standalone application independent of the design program. No differences between the reported results, this proving the possibility of large-scale usability of what was designed.
Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accur...
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Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM *** this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.
In this paper, we propose a framework to iden-tify discrete-time, multi-input/multi-output (MIMO), switched-linear systems (SLSs) from input-output data. The key step is an observer-based transformation to a switched ...
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ISBN:
(数字)9798350354409
ISBN:
(纸本)9798350354416
In this paper, we propose a framework to iden-tify discrete-time, multi-input/multi-output (MIMO), switched-linear systems (SLSs) from input-output data. The key step is an observer-based transformation to a switched auto-regressive with exogenous input (SARX) model. This transformation has a nontrivial kernel complicating identification, but converts the state-space identification problem to SARX model identification. Issues resulting from this transformation are carefully addressed by considering equivalance classes.
In this paper, we present a study on using weighted total least squares method for parameter estimation of errors-in-variables models with quadratic regressors. The statistics of error is analyzed to fill in the gap b...
In this paper, we present a study on using weighted total least squares method for parameter estimation of errors-in-variables models with quadratic regressors. The statistics of error is analyzed to fill in the gap between basic assumptions in weighted total least squares and our case. A modified Cramér-Rao lower bound is introduced for error quantification in the proposed method. We perform evaluations based on simulations with comparisons to standard least squares and generalized total least squares. Numerical results show that the proposed method outperforms the others in terms of estimation accuracy.
The multiphase LLC converter is commonly used for high-power and high step-down applications. However, the tolerances in the tank circuit parameters cause uneven current sharing (CS), reducing system reliability. Addi...
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We study model predictive control (MPC) problems for stochastic LTI systems, where the noise distribution is unknown, compactly supported, and only observable through a limited number of i.i.d. noise samples. Building...
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This paper presents an assay of the dynamic state of charge characteristics of a Li-ion battery. It is proposed an approach of forecasting battery’s state of charge that can be used by battery management system to pr...
This paper presents an assay of the dynamic state of charge characteristics of a Li-ion battery. It is proposed an approach of forecasting battery’s state of charge that can be used by battery management system to predict the remained quantity of charge within the battery at a certain time, based on real time data acquisition and using a simplified electrical circuit model that simulates, with small errors, the battery signal, with experimentally validation of the results.
We study stochastic dynamical systems in settings where only partial statistical information about the noise is available, e.g., in the form of a limited number of noise realizations. Such systems are particularly cha...
We study stochastic dynamical systems in settings where only partial statistical information about the noise is available, e.g., in the form of a limited number of noise realizations. Such systems are particularly challenging to analyze and control, primarily due to an absence of a distributional uncertainty model which: (1) is expressive enough to capture practically relevant scenarios; (2) can be easily propagated through system maps; (3) is closed under propagation; and (4) allows for computationally tractable control actions. In this paper, we propose to model distributional uncertainty via Optimal Transport ambiguity sets and show that such modeling choice satisfies all of the above requirements. We then specialize our results to stochastic LTI systems, and start by showing that the distributional uncertainty can be efficiently captured, with high probability, within an Optimal Transport ambiguity set on the space of noise trajectories. Then, we show that such ambiguity sets propagate exactly through the system dynamics, giving rise to stochastic tubes that contain, with high probability, all trajectories of the stochastic system. Finally, we show that the control task is very interpretable, unveiling an interesting decomposition between the roles of the feedforward and the feedback control terms. Our results are actionable and successfully applied in stochastic reachability analysis and in trajectory planning under distributional uncertainty.
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