Robust output voltage control of a high gain DC–DC converter under applied load and input voltage uncertainties

Akhormeh, Ahmad Reza Naderi; Akhormeh, Alireza Naderi; Abbaszadeh, Karim

doi:10.1049/iet-pel.2020.0156

Cited by 5 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the proposed MB-based DVMPC, N 1 , N 2 , and n s are considered equal to 8, 2, and 3, respectively. In this case, the entire prediction horizon is 14 × T s , while the number of feasible switching vectors is 2 10 , which leads to 93% reduction in the Although utilizing N 1 = 14 without employing the MB strategy produces improved outcomes in terms of error values and settling time. this choice significantly escalates computational load and simulation duration, necessitating the use of a potent processor with considerable budget.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Based on this outlook, optimal MB coefficients should be selected to yield reasonable performance indicators. Consequently, the selection of N 1 = , N 2 = 2, and n s = 3 emerges as a balance, reducing computational load to an acceptable level (2 10 ) for practical implementation, while still maintaining acceptable settling time, steady-state error, and mean error. Indeed, when compared with the case of N = 14 without utilizing the MB approach, while the settling time and mean error have respectively risen by 19% and 4%, conversely, the computational time has been notably reduced by approximately 92%.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…As such, finding effective methods to control these converters has been a key area of research in recent years [5]. Various control strategies, such as proportional-integral-derivative (PID) [6], sliding mode control [7], fuzzy logic control [8], H∞ [9,10], and model predictive control (MPC) [11], are recently reported for this purpose. Although these methods have proven effective, cer-tain control challenges still remain unresolved.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A move blocking based direct voltage model predictive control to enhance the dynamic performance of DC microgrids containing constant power loads

Tatari,

Banejad,

Kalat

2023

IET Renewable Power Gen

View full text Add to dashboard Cite

Here, a move blocking (MB) based direct voltage model predictive control (DVMPC) strategy is introduced to enhance the dynamic performance of a DC microgrid in presence of constant power loads (CPLs). For this aim, an automatic discrete dynamic model is first developed for a boost converter. Considering the CPL effects on the dynamic model of the boost converter, a model predictive control is then designed to directly regulate the output voltage of the converter. Move blocking strategy is finally integrated into DVMPC in order to extend the prediction horizon, and consequently, dealing with the non‐minimum phase characteristic of the boost converter. The dynamic performance of the boost converter with a CPL load in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is evaluated using MB‐based DVMPC and traditional control strategies. The simulation results conducted using MATLAB/Simulink demonstrate that the proposed approach not only enhances the dynamic performance of the DC microgrid, but also reduces the computational burden on the processor. Moreover, experimental results have been performed to validate the proposed strategy. Finally, the stability analysis of the proposed direct voltage control is provided.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A move blocking based direct voltage model predictive control to enhance the dynamic performance of DC microgrids containing constant power loads

Tatari,

Banejad,

Kalat

2023

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…In robust control, reduction of the external disturbance effect on the system output is attempted by applying an appropriate input control to the system. For linear systems, this aim is achieved by minimizing the norm of the transfer function between disturbance and the system output Akhormeh (2020). However, for nonlinear systems, the desired disturbance rejection can be achieved by minimizing γ in the optimization problem (16)it is shown that the inequalityimplies (16) Boyd et al (1994).…”

Section: Fuzzy Model-based Controlmentioning

confidence: 99%

Fuzzy model-based disturbance rejection control for atomic force microscopy with input constraint

Mahmoudabadi

Tavakoli-Kakhki

HosseinNia

2022

Journal of Vibration and Control

View full text Add to dashboard Cite

Accurate representation of the atomic force microscopy (AFM) system is not only necessary to achieve control objectives, but it is also beneficial for detecting the nanomechanical properties of the samples. To this end, this paper addresses the issue of controller design for the AFM system based on an accurate nonaffine nonlinear distributed-parameters model in which flexibility and distributed mass effects of the microcantilever beam are considered properly. First, a T-S fuzzy model is derived for this dynamical model of the AFM system in order to simplify the procedure of controller design. Then, a fuzzy model-based controller is designed to suppress the chaos and attenuate the disturbance in the AFM system through the linear matrix inequality (LMI) formulation. Moreover, by considering some criteria for disturbance rejection and transient performance, and some constraints on control input and states, new stabilization conditions are proposed based on a fuzzy Lyapunov function. Finally, simulation results are represented to demonstrate the effectiveness of the proposed method.

show abstract

“…An optimal control sequence is achieved by adopting recently existing information on the system but only the first control law is implemented 3 . Since industrial processes are inherently nonlinear, 4 the application of Nonlinear Model Predictive Control (NMPC) is significant. As a result of adopting a nonlinear model, the NMPC must solve a complex non‐convex optimization problem online.…”

Section: Introductionmentioning

confidence: 99%

Robust fuzzy model predictive control for nonlinear discrete‐time systems

Mahmoudabadi,

Naderi Akhormeh

2023

Adaptive Control & Signal

View full text Add to dashboard Cite

SummaryThis paper studies the issue of Robust Fuzzy Model Predictive Control (RFMPC) of nonlinear discrete‐time systems in the presence of disturbance. To this end, Takagi‐Sugeno (T‐S) fuzzy model is adopted in order to characterize uncertain nonlinear systems and facilitate providing controller for such systems. Non‐parallel distributed compensation (non‐PDC) technique is applied to design improved RFPMC with better performance. The optimization problem of RFMPC is represented in terms of linear matrix inequalities (LMIs) so as to decrease online computational burden. Besides, feasibility and stability which are decisive factors in MPC theory are investigated for the proposed method. Ultimately, two examples including cart‐damper‐spring system are simulated to evaluate the results of this research work.

show abstract

Robust output voltage control of a high gain DC–DC converter under applied load and input voltage uncertainties

Cited by 5 publications

References 30 publications

A move blocking based direct voltage model predictive control to enhance the dynamic performance of DC microgrids containing constant power loads

A move blocking based direct voltage model predictive control to enhance the dynamic performance of DC microgrids containing constant power loads

Fuzzy model-based disturbance rejection control for atomic force microscopy with input constraint

Robust fuzzy model predictive control for nonlinear discrete‐time systems

Contact Info

Product

Resources

About