Related Experiment Videos
Multi-rate sampled-data control for T-S fuzzy systems with mismatched fuzzy basis functions
1Faculty of Electrical and Electronic Engineering, PHENIKAA University, Hanoi, Vietnam.
Plos One
|August 7, 2026
Summary
This study presents a new multi-rate sampled-data (MRSD) stabilization criterion for T-S fuzzy systems, improving control with mismatched fuzzy basis functions (FBFs). The method enhances system stability by leveraging advanced Lyapunov-Krasovskii functionals (LKFs) and relaxation techniques.
Area of Science:
- Control Systems Engineering
- Fuzzy Logic Systems
- Nonlinear Control Theory
Background:
- T-S fuzzy systems are widely used for modeling nonlinear systems.
- Multi-rate sampled-data (MRSD) control offers improved performance but introduces complexity.
- Mismatched fuzzy basis functions (FBFs) pose a significant challenge in controller design.
Purpose of the Study:
- To develop a novel multi-rate sampled-data (MRSD) stabilization criterion for Takagi-Sugeno (T-S) fuzzy systems.
- To address the challenge of mismatched fuzzy basis functions (FBFs) in MRSD control.
- To enhance the exploitation of system information and sampling-induced delays.
Main Methods:
- Incorporation of an improved looped-functional and a discontinuous function into the Lyapunov-Krasovskii functional (LKF).
- Development of a refined relaxation technique for parameterized linear matrix inequalities (PLMIs) to handle mismatched FBFs.
- Explicit leveraging of system measurements and sampling-induced delays within the LKF construction.
Main Results:
- A new, effective MRSD stabilization criterion is proposed for T-S fuzzy systems with mismatched FBFs.
- The proposed method demonstrates improved performance by fully utilizing system information.
- The developed relaxation technique effectively addresses the conditions expressed as PLMIs.
Conclusions:
- The proposed MRSD stabilization criterion is effective and practical for T-S fuzzy systems with mismatched FBFs.
- The advanced LKF construction and relaxation technique offer significant advantages in control design.
- The study provides a valuable contribution to the field of fuzzy control and sampled-data systems.
Related Concept Videos
01:16Sampling Theorem
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
01:22Upsampling
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
01:18Aliasing
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
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...
01:17Bandpass Sampling
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
01:11Sampling Continuous Time Signal
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...