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Related Concept Videos

Control Systems01:10

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
Feedback control systems01:26

Feedback control systems

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...
Open and closed-loop control systems01:17

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

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...
Controller Configurations01:22

Controller Configurations

Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller aligns...
Effects of feedback01:24

Effects of feedback

Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...

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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Published on: May 8, 2021

Prescribed Performance Output Feedback/Observer-Free Robust Adaptive Control of Uncertain Systems Using Neural

A K Kostarigka, G A Rovithakis

    IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
    |June 29, 2011
    PubMed
    Summary

    This study introduces a novel neural network controller for complex nonlinear systems, ensuring stable performance even with disturbances. The observer-free design guarantees system output and signal boundedness for improved control.

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    Last Updated: May 31, 2026

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    Published on: May 8, 2021

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear System Dynamics
    • Artificial Intelligence in Engineering

    Background:

    • Designing controllers for uncertain nonlinear systems is challenging due to complex dynamics and external disturbances.
    • Existing methods often require detailed system models or observers, limiting their applicability.
    • Ensuring both output performance and internal signal stability is crucial for robust control.

    Purpose of the Study:

    • To develop an output feedback, observer-free continuous controller for multiple-input-multiple-output (MIMO) uncertain nonlinear systems.
    • To guarantee prescribed performance bounds on the system output and boundedness of all closed-loop signals.
    • To address the impact of additive external disturbances and unmodeled dynamics.

    Main Methods:

    • Utilized a neural network-based approach for controller design.
    • Employed an output feedback strategy, eliminating the need for an observer.
    • Incorporated assumptions on system properties, including unboundedness observability and output Lagrange stability of unmodeled dynamics.
    • Ensured the nominal system is output feedback equivalent to a strictly passive one.

    Main Results:

    • Successfully designed a continuous controller capable of handling MIMO uncertain nonlinear systems.
    • Guaranteed prescribed performance bounds on the system output.
    • Ensured the boundedness of all other closed-loop signals in the presence of disturbances and unmodeled dynamics.
    • Demonstrated the controller's effectiveness through simulations on an induction motor system.

    Conclusions:

    • The proposed neural network controller offers a robust solution for controlling complex nonlinear systems without requiring an observer.
    • The controller effectively manages external disturbances and unmodeled dynamics while maintaining performance and stability.
    • The observer-free, output feedback approach provides a practical advancement in nonlinear control engineering.