Chaos control of fractional order nonlinear Bloch equation by utilizing sliding mode controller

Baishya, Chandrali; Premakumari, Ramapura N.; Samei, Mohammad Esmael; Naik, Manisha Krishna

doi:10.1016/j.chaos.2023.113773

Cited by 14 publications

(1 citation statement)

References 63 publications

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“…This equation establishes the relationship between the frequency of the RF pulse and the strength of the magnetic field, which is crucial for controlling the excitation of nuclear spins. Afterwards, the RF (receiver) coil detects signals emitted by relaxing nuclei during the relaxation processes, and this is a time-domain signal, usually represented by the free induction decay (FID) [25] signal which undergo Fourier Transform for image reconstruction. The FID is mathematically expressed in Equations 7, 8 and 9 through the Bloch equations in two main components of the longitudinal (𝑀 𝑧 ) and transverse magnetization (𝑀 𝑥𝑦 ), which describe the behavior of nuclear magnetization in response to RF pulses and during relaxation processes.…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

Nalumansi,

Mwangi,

Kamucha

2024

IJEER

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One powerful technique that can offer a thorough examination of the body's internal structure is magnetic resonance imaging (MRI). MRI's lengthy acquisition times, however, may restrict its clinical usefulness, particularly in situations where time is of the essence. Compressed sensing (CS) has emerged as a potentially useful method for cutting down on MRI acquisition times; nevertheless, the effectiveness of CS-MRI is dependent on the selection of the sparsity-promoting algorithm and sampling scheme. This research paper presents a novel method based on adaptive multi-extreme particle swarm optimization (AMEPSO) and dual tree complex wavelet transform (DTCWT) for fast image acquisition in magnetic resonance. The method uses AMEPSO in order to maximize the sampling pattern and minimize reconstruction error, while also exploiting the sparsity of MR images in the DTCWT domain to improve directional selectivity and shift invariance. MATLAB software was used for simulation of the proposed method. In comparison with the particle swarm optimized-DTCWT (PSODTCWT) and DTCWT algorithms, respectively, the results demonstrated an improvement in the peak signal-to-noise ratio of 8.92% and 15.92% and a higher structural similarity index measure of 3.69% and 7.5%. Based on these improvements, the proposed method could potentially make high-quality, real-time MRI imaging possible, which might improve detection and treatment of medical conditions and increase the throughput of MRI machines.

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Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

Nalumansi,

Mwangi,

Kamucha

2024

IJEER

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Stabilization of fractional nonlinear systems with disturbances via sliding mode control

Wang,

Cui,

Zhang

et al. 2024

Intl J Robust & Nonlinear

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In this article, the sliding mode control (SMC) of fractional nonlinear systems (FNSs) with disturbances is studied. First, a useful fractional power‐rate inequality for is extended to a more general form . Based on the generalized inequality, the method of a modified fractional integral SMC is completed, in which the parameter of the symbolic function is extended to and . This increases the degree of freedom of the sliding mode surface (SMS). Then, the SMC of FNSs is studied for the cases of known and unknown disturbances. In the case of known disturbance, it is proved by the generalized inequality and quadratic Lyapunov function method that the state of the FNSs can converge asymptotically to zero on the SMS. For the case of unknown disturbance, a fractional disturbance observer is used to estimate the disturbance by introducing auxiliary variables . The disturbance estimation error of the proposed FNSs is proved to be bounded. An equivalent global control law is obtained and asymptotic convergence of the FNSs under the action of the controller is proved. Finally, two numerical simulation examples are given to verify the feasibility of the method proposed in this article.

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Vibration suppression and P-bifurcation of a randomly excited fractional-order damping system controlled by nonlinear energy sink

Sun,

Liao,

Yang

2024

Nonlinear Dyn

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Chaos control of fractional order nonlinear Bloch equation by utilizing sliding mode controller

Cited by 14 publications

References 63 publications

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

Stabilization of fractional nonlinear systems with disturbances via sliding mode control

Vibration suppression and P-bifurcation of a randomly excited fractional-order damping system controlled by nonlinear energy sink

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