Related Experiment Video
Updated: Jun 17, 2026
12:03
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
Published on: May 25, 2019
Adaptive tracking for periodically time-varying and nonlinearly parameterized systems using multilayer neural
1Department of Applied Mathematics, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China. wshchen@126.com
IEEE Transactions on Neural Networks
|January 15, 2010
Summary
This study introduces adaptive neural network tracking control for strict-feedback systems. The novel control scheme effectively handles unknown disturbances, ensuring system stability and accurate tracking performance.
Area of Science:
- Control Engineering
- Artificial Intelligence
- Nonlinear Systems
Background:
- Strict-feedback systems often face challenges with unknown time-varying disturbances.
- Accurate modeling of system uncertainties is crucial for effective control design.
Purpose of the Study:
- To design an adaptive neural network tracking control for strict-feedback systems with unknown disturbances.
- To develop a robust control algorithm capable of handling nonlinear uncertainties.
Main Methods:
- Utilized a novel approximator combining Multilayer Neural Network (MNN) and Fourier Series Expansion (FSE) to model system uncertainties.
- Employed Dynamic Surface Control (DSC) and Integral-type Lyapunov Function (ILF) techniques for control algorithm design.
Main Results:
- Guaranteed ultimate uniform boundedness of all closed-loop signals.
- Demonstrated convergence of tracking error to a small residual set around the origin.
- Validated the control scheme's feasibility through two simulation examples.
Conclusions:
- The proposed adaptive neural network tracking control is effective for strict-feedback systems with unknown disturbances.
- The combination of MNN, FSE, DSC, and ILF provides a robust and feasible control solution.
- The control strategy ensures system stability and minimizes tracking errors.
Related Concept Videos
01:21Linear Approximation in Time Domain
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
01:26Linear Approximation in Frequency Domain
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
01:26Feedback 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...
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...
01:22Multi-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...
In the absence of...
01:30Vector Functions and Motion: Problem Solving
Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...
01:23Linear 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...
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...