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Related Concept Videos

Feedback control systems01:26

Feedback control systems

515
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
515
Transient and Steady-state Response01:24

Transient and Steady-state Response

327
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
327
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
166
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
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

206
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
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Linear time-invariant Systems01:23

Linear time-invariant Systems

536
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
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Event-Triggered Adaptive Neural Network Control for Stochastic Nonlinear Systems With State Constraints and

Yongchao Liu, Qidan Zhu

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    |August 31, 2021
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    Summary
    This summary is machine-generated.

    This study introduces an event-triggered adaptive neural network (ANN) control strategy for stochastic nonlinear systems. The innovative approach ensures system stability and adheres to state constraints, even with time-varying delays.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Stochastic nonlinear systems present significant control challenges due to inherent uncertainties and complex dynamics.
    • State constraints and time-varying delays further complicate controller design, potentially leading to instability or performance degradation.

    Purpose of the Study:

    • To develop an event-triggered adaptive neural network (ANN) control strategy for stochastic nonlinear systems.
    • To address challenges posed by state constraints and time-varying delays.
    • To enhance control efficiency by reducing data transmission via an event-triggered mechanism (ETM).

    Main Methods:

    • Utilized barrier Lyapunov functions to manage state constraints effectively.
    • Employed neural networks for approximating unknown system dynamics.
    • Applied Lyapunov-Krasovskii functionals to mitigate the impact of time-varying delays.
    • Integrated an event-triggered mechanism (ETM) with the backstepping technique for controller design.

    Main Results:

    • The proposed ANN control scheme guarantees the stability of the stochastic nonlinear systems.
    • Predefined state constraints were successfully maintained throughout the control process.
    • The event-triggered approach demonstrated reduced data transmission and saved communication resources.

    Conclusions:

    • The developed event-triggered adaptive neural network control strategy is feasible and effective for stochastic nonlinear systems with state constraints and time-varying delays.
    • The method offers a promising approach for resource-constrained control applications.
    • Simulation results validate the performance and robustness of the proposed control scheme.