Stability analysis

Bavafa-Toosi, Yazdan

doi:10.1016/b978-0-12-812748-3.00003-3

Cited by 3 publications

(5 citation statements)

References 98 publications

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“…Stability is the fundamental requirement for any control system, ensuring reliable and predictable performance even in challenging situations. 111,112 The term 'stability' refers to the ability of a control system to respond within a specified range when subjected to bounded control inputs and unfavorable disturbances. 113 Control theory offers a range of approaches to assess stability, such as evaluating the gains of linear and non-linear operators that describe the system's dynamic response.…”

Section: Stabilitymentioning

confidence: 99%

“…96 One popular and straightforward approach in analyzing stability is the use of the Lyapunov stability method, which involves constructing a Lyapunov function within the cost function. 111 Studies have demonstrated the benefits of employing a Lyapunov-based approach in enhancing stability within control strategies. [118][119][120][121] For instance, quadratic programming for MPC has been developed based on the Lyapunov function, highlighting improved stability.…”

Section: Stabilitymentioning

confidence: 99%

“…Stability is the fundamental requirement for any control system, ensuring reliable and predictable performance even in challenging situations 111,112 . The term ‘stability’ refers to the ability of a control system to respond within a specified range when subjected to bounded control inputs and unfavorable disturbances 113 .…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…However, when employing MPC with constraints, conventional linear techniques or frequency‐domain stability analysis methods are not directly applicable 96 . One popular and straightforward approach in analyzing stability is the use of the Lyapunov stability method, which involves constructing a Lyapunov function within the cost function 111 . Studies have demonstrated the benefits of employing a Lyapunov‐based approach in enhancing stability within control strategies 118–121 .…”

Section: Model Predictive Controlmentioning

confidence: 99%

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Control strategy for biopharmaceutical production by model predictive control

Eslami,

Jungbauer

2024

Biotechnology Progress

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The biopharmaceutical industry is rapidly advancing, driven by the need for cutting‐edge technologies to meet the growing demand for life‐saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real‐time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.

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Section: Stabilitymentioning

confidence: 99%

Section: Stabilitymentioning

confidence: 99%

Section: Model Predictive Controlmentioning

confidence: 99%

Section: Model Predictive Controlmentioning

confidence: 99%

See 2 more Smart Citations

Control strategy for biopharmaceutical production by model predictive control

Eslami,

Jungbauer

2024

Biotechnology Progress

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“…The equation of motion of the BLDCM is as follows: where, , and represent load torque, damping coefficient, and moment of inertia, respectively. Hence, the characteristic equation of BLDCM can be expressed as 10 , 33 : where, K emf is the back electromotive force constant. Figure 2 is the block diagram of the speed control system for BLDCM.…”

Section: Mathematical Mode Of Bldcmmentioning

confidence: 99%

A novel PID controller for BLDCM speed control using dual fuzzy logic systems with HSA optimization

Wang

et al. 2022

Sci Rep

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In order to enhance the speed control performance of the brushless DC motor (BLDCM), a novel proportion integration differentiation (PID) is proposed in this paper by using dual fuzzy logic systems (FLSs) with harmony search algorithm (HSA) optimization, which is called DFPID-HSA. Firstly, the FLS1 in DFPID-HSA locks the three coefficients of the PID controller in an extensive range on the basis of the system error and error change rate. Then, the FLS2 is optimized by HSA (HSA-F2) to obtain the precise correction of the three coefficients. To get the optimal global harmony better, the improved dynamic adjustment mode is used for the pitch adjustment rate (PAR) and distance bandwidth (BW) in HSA, and the triple selection method is adopted in the composition harmony section to realize the global search. Finally, DFPID-HSA provides the optimal supply control signal to BLDCM so that it can control the speed effectively. Moreover, the stability of the system is analyzed by the pole, Lyapunov, and Nyquist determination methods. And the sensitivity analysis of DFPID-HSA is carried out under the condition of different motor’s mechanical parameters to check its robustness. In addition, the superiority of DFPID-HSA is verified by MATLAB simulation and experiment platform.

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Optimal control and stability analysis of monkeypox transmission dynamics with the impact of contaminated surfaces

Hassan,

Aldila,

Noor Aziz

2024

Front. Appl. Math. Stat.

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This study presents a comprehensive analysis of the transmission dynamics of monkeypox, considering contaminated surfaces using a deterministic mathematical model. The study begins by calculating the basic reproduction number and the stability properties of equilibrium states, specifically focusing on the disease-free equilibrium and the endemic equilibrium. Our analytical investigation reveals the occurrence of a forward bifurcation when the basic reproduction number equals unity, indicating a critical threshold for disease spread. The non-existence of backward bifurcation indicates that the basic reproduction number is the single endemic indicator in our model. To further understand the dynamics and control strategies, sensitivity analysis is conducted to identify influential parameters. Based on these findings, the model is reconstructed as an optimal control problem, allowing for the formulation of effective control strategies. Numerical simulations are then performed to assess the impact of these control measures on the spread of monkeypox. The study contributes to the field by providing insights into the optimal control and stability analysis of monkeypox transmission dynamics. The results emphasize the significance of contaminated surfaces in disease transmission and highlight the importance of implementing targeted control measures to contain and prevent outbreaks. The findings of this research can aid in the development of evidence-based strategies for mitigating the impact of monkeypox and other similar infectious diseases.

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Stability analysis

Cited by 3 publications

References 98 publications

Control strategy for biopharmaceutical production by model predictive control

Control strategy for biopharmaceutical production by model predictive control

A novel PID controller for BLDCM speed control using dual fuzzy logic systems with HSA optimization

Optimal control and stability analysis of monkeypox transmission dynamics with the impact of contaminated surfaces

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