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Related Concept Videos

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...
Linear time-invariant Systems01:23

Linear time-invariant Systems

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 calculated...
PID Controller01:19

PID Controller

Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
Phase-lead and Phase-lag Controllers01:22

Phase-lead and Phase-lag Controllers

Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass filters, manage...
PI Controller: Design01:24

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
Neural Control of Respiration01:18

Neural Control of Respiration

The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...

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Related Experiment Videos

A Real-Time Offset-Free Neural Network MPC Framework for Aeroengine Control on Embedded Systems.

Wen-Tao Li, Si-Xin Wen, Xue-Fang Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |June 11, 2026
    PubMed
    Summary

    This study introduces a novel real-time offset-free Model Predictive Control (MPC) framework for aeroengines. It uses neural networks and adaptive error compensation to eliminate steady-state errors, improving control accuracy and dynamic response.

    Related Experiment Videos

    Area of Science:

    • Aerospace Engineering
    • Control Systems
    • Artificial Intelligence

    Background:

    • Model Predictive Control (MPC) for aeroengines faces challenges with complex nonlinear dynamics.
    • Neural networks (NNs) offer nonlinear approximation but suffer from errors in embedded systems, causing steady-state offsets.

    Purpose of the Study:

    • To develop a real-time offset-free MPC framework for aeroengines.
    • To enhance tracking performance and eliminate steady-state deviations in closed-loop control.

    Main Methods:

    • Incorporated an NN-based prediction model with a multivariable adaptive error compensator.
    • Developed a low-rank approximation linearization method for computational efficiency.
    • Hardware-accelerated the framework on a Zynq deep-learning processing unit (DPU).

    Main Results:

    • Achieved faster dynamic response and improved steady-state accuracy compared to conventional MPC.
    • Demonstrated reduced computation latency through hardware acceleration.
    • Validated performance in realistic aeroengine control scenarios via hardware-in-the-loop (HIL) experiments.

    Conclusions:

    • The proposed real-time offset-free MPC framework effectively eliminates steady-state errors in aeroengine control.
    • The approach offers practical potential for aerospace applications due to enhanced performance and efficiency.