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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...
State Space to Transfer Function01:21

State Space to Transfer Function

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:
State Space Representation01:27

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...
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
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...
Conservation of Energy in Control Volume01:14

Conservation of Energy in Control Volume

Consider a turbine operating under steady-flow conditions. The control volume is drawn around the turbine, with fluid entering at one point and exiting at another. The turbine extracts energy from the fluid, which performs mechanical work (shaft work).
For steady flow systems, the time derivative of the stored energy becomes zero since there is no energy accumulation within the control volume. This simplifies the energy equation to:

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Related Experiment Video

Updated: Jun 13, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Virtual State Coupled Sliding Mode Control: An Energy Exchange Approach with Tunable Performance Trade-Off.

Jialong Wang1, Jianli Wang1, Jiaxin Jing1

  • 1College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel virtual state coupled sliding mode control (SMC) method. It enables independent adjustment of energy-performance trade-offs for improved control systems.

Keywords:
bilinear product couplingenergy exchangesliding mode controltransient performance trade-offvirtual state coupling

Related Experiment Videos

Last Updated: Jun 13, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Area of Science:

  • Control Systems Engineering
  • Nonlinear Dynamics
  • Robotics

Background:

  • Traditional sliding mode control (SMC) exhibits rigid trade-offs between speed, overshoot, and energy efficiency.
  • Existing methods lack active energy redistribution mechanisms during transient convergence.

Purpose of the Study:

  • To propose a virtual state coupled SMC method for enhanced energy-performance trade-off management.
  • To introduce a dynamic virtual state with bilinear product coupling into the sliding surface.

Main Methods:

  • Development of a dynamic virtual state with bilinear product coupling (x1x2).
  • Application of linearization and Lyapunov-based stability analyses.
  • Simulation studies and Monte Carlo analysis for performance validation.

Main Results:

  • Achieved up to 53.2% control energy reduction under disturbance-free conditions.
  • Demonstrated up to 54.2% reduction in oscillations under disturbances with controllable energy cost.
  • Validated 100% convergence across 500 trials with parameter perturbations.

Conclusions:

  • The proposed method offers an independently adjustable energy-performance trade-off.
  • Suitable for sensor-based motion systems with strict transient and energy demands.
  • Virtual state coupling provides an active energy exchange channel for improved control.