19,704 research outputs found
LMI-Based Reset Unknown Input Observer for State Estimation of Linear Uncertain Systems
This paper proposes a novel kind of Unknown Input Observer (UIO) called Reset
Unknown Input Observer (R-UIO) for state estimation of linear systems in the
presence of disturbance using Linear Matrix Inequality (LMI) techniques. In
R-UIO, the states of the observer are reset to the after-reset value based on
an appropriate reset law in order to decrease the norm and settling time
of estimation error. It is shown that the application of the reset theory to
the UIOs in the LTI framework can significantly improve the transient response
of the observer. Moreover, the devised approach can be applied to both SISO and
MIMO systems. Furthermore, the stability and convergence analysis of the
devised R-UIO is addressed. Finally, the efficiency of the proposed method is
demonstrated by simulation results
Beyond the Waterbed Effect: Development of Fractional Order CRONE Control with Non-Linear Reset
In this paper a novel reset control synthesis method is proposed: CRONE reset
control, combining a robust fractional CRONE controller with non-linear reset
control to overcome waterbed effect. In CRONE control, robustness is achieved
by creation of constant phase behaviour around bandwidth with the use of
fractional operators, also allowing more freedom in shaping the open-loop
frequency response. However, being a linear controller it suffers from the
inevitable trade-off between robustness and performance as a result of the
waterbed effect. Here reset control is introduced in the CRONE design to
overcome the fundamental limitations. In the new controller design, reset phase
advantage is approximated using describing function analysis and used to
achieve better open-loop shape. Sufficient quadratic stability conditions are
shown for the designed CRONE reset controllers and the control design is
validated on a Lorentz-actuated nanometre precision stage. It is shown that for
similar phase margin, better performance in terms of reference-tracking and
noise attenuation can be achieved.Comment: American Control Conference 201
Networked control systems in the presence of scheduling protocols and communication delays
This paper develops the time-delay approach to Networked Control Systems
(NCSs) in the presence of variable transmission delays, sampling intervals and
communication constraints. The system sensor nodes are supposed to be
distributed over a network. Due to communication constraints only one node
output is transmitted through the communication channel at once. The scheduling
of sensor information towards the controller is ruled by a weighted
Try-Once-Discard (TOD) or by Round-Robin (RR) protocols. Differently from the
existing results on NCSs in the presence of scheduling protocols (in the
frameworks of hybrid and discrete-time systems), we allow the communication
delays to be greater than the sampling intervals. A novel hybrid system model
for the closed-loop system is presented that contains {\it time-varying delays
in the continuous dynamics and in the reset conditions}. A new
Lyapunov-Krasovskii method, which is based on discontinuous in time Lyapunov
functionals is introduced for the stability analysis of the delayed hybrid
systems. Polytopic type uncertainties in the system model can be easily
included in the analysis. The efficiency of the time-delay approach is
illustrated on the examples of uncertain cart-pendulum and of batch reactor
Further results on exponential estimates of markovian jump systems with mode-dependent time-varying delays
This technical note studies the problem of exponential estimates for Markovian jump systems with mode-dependent interval time-varying delays. A novel LyapunovKrasovskii functional (LKF) is constructed with the idea of delay partitioning, and a less conservative exponential estimate criterion is obtained based on the new LKF. Illustrative examples are provided to show the effectiveness of the proposed results. © 2010 IEEE.published_or_final_versio
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Filtering for nonlinear genetic regulatory networks with stochastic disturbances
In this paper, the filtering problem is investigated for nonlinear genetic regulatory networks with stochastic disturbances and time delays, where the nonlinear function describing the feedback regulation is assumed to satisfy the sector condition, the stochastic perturbation is in the form of a scalar Brownian motion, and the time delays exist in both the translation process and the feedback regulation process. The purpose of the addressed filtering problem is to estimate the true concentrations of the mRNA and protein. Specifically, we are interested in designing a linear filter such that, in the presence of time delays, stochastic disturbances as well as sector nonlinearities, the filtering dynamics of state estimation for the stochastic genetic regulatory network is exponentially mean square stable with a prescribed decay rate lower bound beta. By using the linear matrix inequality (LMI) technique, sufficient conditions are first derived for ensuring the desired filtering performance for the gene regulatory model, and the filter gain is then characterized in terms of the solution to an LMI, which can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures
Improving speech recognition by revising gated recurrent units
Speech recognition is largely taking advantage of deep learning, showing that
substantial benefits can be obtained by modern Recurrent Neural Networks
(RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which
typically reach state-of-the-art performance in many tasks thanks to their
ability to learn long-term dependencies and robustness to vanishing gradients.
Nevertheless, LSTMs have a rather complex design with three multiplicative
gates, that might impair their efficient implementation. An attempt to simplify
LSTMs has recently led to Gated Recurrent Units (GRUs), which are based on just
two multiplicative gates.
This paper builds on these efforts by further revising GRUs and proposing a
simplified architecture potentially more suitable for speech recognition. The
contribution of this work is two-fold. First, we suggest to remove the reset
gate in the GRU design, resulting in a more efficient single-gate architecture.
Second, we propose to replace tanh with ReLU activations in the state update
equations. Results show that, in our implementation, the revised architecture
reduces the per-epoch training time with more than 30% and consistently
improves recognition performance across different tasks, input features, and
noisy conditions when compared to a standard GRU
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