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Related Concept Videos

State Space Representation01:27

State Space Representation

335
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
335
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.8K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.8K
State Space to Transfer Function01:21

State Space to Transfer Function

360
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
360
Multimachine Stability01:25

Multimachine Stability

256
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
256
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

4.1K
The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
4.1K
Transfer Function to State Space01:23

Transfer Function to State Space

482
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
482

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Related Experiment Videos

Event-Triggered Recursive State Estimation for Stochastic Complex Dynamical Networks Under Hybrid Attacks.

Yun Chen, Xueyang Meng, Zidong Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |August 31, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study develops an event-triggered recursive state estimation method for stochastic complex dynamical networks facing hybrid cyberattacks. The proposed approach ensures the estimation error remains stochastically bounded, enhancing network security and efficiency.

    Related Experiment Videos

    Area of Science:

    • Control Systems Engineering
    • Network Security
    • Stochastic Systems

    Background:

    • Complex dynamical networks are susceptible to cyberattacks, impacting state estimation.
    • Existing methods may not efficiently handle hybrid attacks or network burdens.

    Purpose of the Study:

    • To investigate event-based recursive state estimation for stochastic complex dynamical networks under hybrid cyberattacks.
    • To develop an estimator that reduces transmission rates and network load while ensuring estimation accuracy.

    Main Methods:

    • Introduced a hybrid cyberattack model encompassing deception and denial-of-service attacks.
    • Employed an event-triggered mechanism for data transmission to reduce network burden.
    • Derived an upper bound on estimation error covariance by solving coupled Riccati-like difference equations.
    • Recursively obtained the estimator gain matrix to minimize the error bound.

    Main Results:

    • The estimation error is proven to be stochastically bounded with probability 1.
    • The event-triggered mechanism effectively reduces transmission rates and network load.
    • The developed estimator design method was validated through an illustrative example.

    Conclusions:

    • The proposed event-based recursive state estimation method is effective for stochastic complex dynamical networks under hybrid cyberattacks.
    • The method enhances network security and operational efficiency by managing data transmission and bounding estimation errors.