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Related Concept Videos

State Space Representation01:27

State Space Representation

435
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...
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BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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 of...
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Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Linear time-invariant Systems01:23

Linear time-invariant Systems

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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.
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Quantized Adaptive Finite-Time Bipartite NN Tracking Control for Stochastic Multiagent Systems.

Ying Wu, Yingnan Pan, Mou Chen

    IEEE Transactions on Cybernetics
    |August 5, 2020
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    Summary

    This study introduces a novel adaptive control strategy for complex nonlinear multiagent systems, addressing unknown dynamics, sensor faults, and hysteresis for precise finite-time bipartite tracking control.

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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Stochastic Systems

    Background:

    • High-order stochastic pure-feedback nonlinear multiagent systems present significant control challenges.
    • Existing finite-time control methods often struggle with unknown nonlinearities and complex system components like hysteresis and sensor faults.

    Purpose of the Study:

    • To develop a quantized adaptive finite-time bipartite tracking control strategy for nonlinear multiagent systems with unknown dynamics, sensor faults, and Prandtl-Ishlinskii (PI) hysteresis.
    • To address the limitations of existing control methods by handling unknown nonlinearities and system complexities without parameter lower bound estimation.

    Main Methods:

    • A novel distributed control method is proposed using adaptive compensation techniques.
    • Radial basis function neural networks are employed to approximate unknown nonlinear functions and mitigate algebraic loops.
    • Dynamic surface control is utilized to prevent the 'explosion of complexity' during controller design.
    • Lyapunov stability theorem is applied to guarantee system stability and control performance.

    Main Results:

    • The proposed control strategy ensures semiglobal practical finite-time stability in probability for all closed-loop system signals.
    • Effective bipartite tracking control performance is achieved despite unknown nonlinearities, sensor faults, and PI hysteresis.
    • The method successfully overcomes the 'explosion of complexity' issue inherent in traditional control designs.

    Conclusions:

    • The developed quantized adaptive finite-time bipartite tracking control strategy is effective for complex nonlinear multiagent systems.
    • The approach provides a robust solution for systems with unknown dynamics, sensor faults, and hysteresis.
    • Simulation results validate the practical applicability and effectiveness of the proposed control method.