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Related Concept Videos

Feedback control systems01:26

Feedback control systems

378
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...
378
Linear time-invariant Systems01:23

Linear time-invariant Systems

334
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...
334
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

123
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
123
Transient and Steady-state Response01:24

Transient and Steady-state Response

242
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...
242
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

139
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
139
State Space to Transfer Function01:21

State Space to Transfer Function

280
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:
280

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Event-Triggered Adaptive Tracking With Guaranteed Transient Performance for Switched Nonlinear Systems Under

Xueliang Wang, Jianwei Xia, Ju H Park

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    This study introduces an event-triggered neural network control for nonlinear systems, enhancing communication efficiency and ensuring performance. The novel approach guarantees bounded signals and robust transient tracking, even with asynchronous switching.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Dynamics
    • Artificial Intelligence

    Background:

    • Switched nonlinear systems present challenges in control due to mode transitions.
    • Ensuring transient performance and communication efficiency in these systems is critical.
    • Existing methods may require initial condition information or limit switching frequency.

    Purpose of the Study:

    • To develop an event-triggered neural network tracking control scheme for switched nonlinear systems.
    • To improve communication resource utilization via a mode-dependent event-triggered communication mechanism (MDETCM).
    • To guarantee transient performance without relying on initial system or signal conditions.

    Main Methods:

    • Design of a mode-dependent event-triggered communication mechanism (MDETCM) considering asynchronous switching.
    • Introduction of normalized function transformation and tan-type barrier functions for transient performance constraints.
    • Integration of improved admissible edge-dependent average dwell time (AED-ADT) with adaptive backstepping control.
    • Proposal of a state-feedback tracking algorithm.

    Main Results:

    • Significant savings in communication resources achieved by the MDETCM.
    • Transient performance ensured without initial condition dependency, unlike traditional prescribed performance bound (PPB) control.
    • All closed-loop signals demonstrated to be bounded.
    • The proposed control scheme validated for a single-link robot system.

    Conclusions:

    • The proposed event-triggered neural network control scheme effectively ensures transient performance for switched nonlinear systems.
    • The novel MDETCM enhances communication efficiency and handles asynchronous switching.
    • The method offers a robust and resource-efficient solution applicable to robotic systems.