scholarly journals Suboptimal Filtering of Networked Discrete-Time Systems with Random Observation Losses

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Shouwan Gao ◽  
Pengpeng Chen
Keyword(s):  
Discrete Time ◽  
Estimation Error ◽  
Multiple Input Multiple Output ◽  
Discrete Time System ◽  
Time Step ◽  
Error Covariance ◽  
Mean Square Stability ◽  
Input Multiple Output ◽  
Multiple Measurements ◽  
Time Systems

This paper studies the remote filtering problem over a packet-dropping network. A general multiple-input-multiple-output (MIMO) discrete-time system is considered. The multiple measurements are sent over different communication channels every time step, and the packet loss phenomenon in every communication channel is described by an independent and identically distributed (i.i.d) Bernoulli process. A suboptimal filter is obtained which can minimize the mean squared estimation error. The convergence properties of the estimation error covariance are studied, and mean square stability of the suboptimal filter is proved under standard assumptions. A simulation example is exploited to demonstrate the effectiveness of the results.

scholarly journals A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
S. Alonso-Quesada ◽  
M. De la Sen ◽  
A. Ibeas
Keyword(s):  
Discrete Time ◽  
Iterative Learning Control ◽  
Linear Models ◽  
Multiple Input Multiple Output ◽  
Binary Sequence ◽  
Learning Control ◽  
Iterative Learning ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.

scholarly journals Optimal guaranteed cost filtering for Markovian jump discrete-time systems

2004 ◽  
Vol 2004 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Magdi S. Mahmoud ◽  
Peng Shi
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Estimation Error ◽  
Linear Matrix ◽  
State Estimator ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Discrete Time Systems ◽  
Linear State ◽  
Time Systems

This paper develops a result on the design of robust steady-state estimator for a class of uncertain discrete-time systems with Markovian jump parameters. This result extends the steady-state Kalman filter to the case of norm-bounded time-varying uncertainties in the state and measurement equations as well as jumping parameters. We derive a linear state estimator such that the estimation-error covariance is guaranteed to lie within a certain bound for all admissible uncertainties. The solution is given in terms of a family of linear matrix inequalities (LMIs). A numerical example is included to illustrate the theory.

Modal Control of Linear Time-Invariant Discrete-Time Systems

1969 ◽  
Vol 2 (8) ◽  
pp. T133-T136 ◽  
Author(s):  
B. Porter ◽  
T. R. Crossley
Keyword(s):  
Discrete Time ◽  
Linear Time ◽  
Feedback Loops ◽  
Modal Control ◽  
Discrete Time System ◽  
Linear Time Invariant ◽  
Modal Theory ◽  
Discrete Time Systems ◽  
Time Invariant ◽  
Time Systems

Modal control theory is applied to the design of feedback loops for linear time-invariant discrete-time systems. Modal theory is also used to demonstrate the explicit relationship which exists between the controllability of a mode of a discrete-time system and the possibility of assigning an arbitrary value to the eigenvalue of that mode.

CHAOTIFICATION OF DISCRETE-TIME SYSTEMS USING NEURONS

2004 ◽  
Vol 14 (04) ◽  
pp. 1405-1411 ◽  
Author(s):  
H. S. KWOK ◽  
WALLACE K. S. TANG
Keyword(s):  
Computer Simulations ◽  
Discrete Time ◽  
Activation Function ◽  
Feedback Signal ◽  
Discrete Time System ◽  
Hyperbolic Tangent ◽  
Time System ◽  
Discrete Time Systems ◽  
Arbitrary Dimensions ◽  
Time Systems

In this paper, a neuron is introduced for chaotifying nonchaotic discrete-time systems with arbitrary dimensions. By modeling the neuron with a hyperbolic tangent activation function, a scalar feedback signal expressed in a linear combination of the neuron outputs is used. Chaos can then be generated from the controlled discrete-time system. The existence of chaos is verified by both theoretical proof and computer simulations.

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