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Feedback control systems

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...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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...

Linear time-invariant Systems

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...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Open and closed-loop control systems

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...

State Space Representation

State Space Representation

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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Related Experiment Video

Updated: Apr 8, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With

Chih-Lyang Hwang, Chau Jan

IEEE Transactions on Neural Networks and Learning Systems

|July 1, 2015

Summary

This study introduces a novel recurrent neural network (RNN) adaptive control for nonlinear dynamic systems with time-varying delays. The method enhances system performance and robustness, overcoming limitations of traditional robust control for NARMA models.

Related Experiment Videos

Last Updated: Apr 8, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Area of Science:

Control Systems Engineering
Artificial Intelligence
Nonlinear Dynamics

Background:

Traditional robust control for Nonlinear Autoregressive Moving Average (NARMA) models faces challenges with time-varying delays, bounded uncertainty, and pre-learned models.
Recurrent Neural Networks (RNNs) are used for online learning of NARMA models, but performance degrades with significant system variations.

Purpose of the Study:

To develop an advanced RNN-based adaptive control strategy for multivariable nonlinear dynamic systems with time-varying delays.
To address the limitations of existing robust control methods for NARMA models.

Main Methods:

An approximate NARMA model is used for system representation.
A recurrent neural network (RNN) is employed for online system identification.
A compensating network addresses RNN approximation errors.
An epsilon-modification learning law with weight matrix projection ensures boundedness.

Main Results:

The proposed RNN-based adaptive control achieves semiglobally ultimately bounded tracking.
The boundedness of the estimated weight matrix is guaranteed.
Simulations demonstrate the effectiveness and robustness of the control strategy.

Conclusions:

The novel RNN-based adaptive control effectively manages multivariable nonlinear systems with time-varying delays.
The approach provides a robust solution outperforming traditional methods.
The method ensures stable system performance and parameter boundedness.