LPV Modeling of Boost Converter and Gain Scheduling MPC Control

Rego, Rosana C. B.

doi:10.1109/cobep/spec44138.2019.9065825

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The duty (D) cycle for switching is determined by the MPPT controller, according to the present climatic condition. The parameter values of the boost converter are calculated using the following expression (Navinkumar et al, 2017; Rosana, 2019)…”

Section: Description and Modeling Of The Pv Conversion Chainmentioning

confidence: 99%

A new MPPT controller based on a modified multiswarm PSO algorithm using an adaptive factor selection strategy for partially shaded PV systems

Ben Regaya,

Farhani,

Hamdi

et al. 2024

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Maximum power point tracking (MPPT) controller is the main element in photovoltaic (PV) systems, which is used to ensure maximum power extraction under different meteorological conditions. A MPPT controller can guarantee good performance criteria even in the presence of climatic changes. To achieve this goal, several techniques have been proposed in the literature to improve robustness of the PV system control, such as artificial intelligence and multiswarm particle swarm optimization (MSPSO) algorithm. Previous research on classical MSPSO has shown that the algorithm search behavior cannot find the optimal solution for certain problems. In this context, we investigate the design of a new MPPT controller based on a modified version of heterogeneous multiswarm particle swarm optimization algorithm using an adaptive factor selection strategy (FMSPSO) for PV systems. The proposed FMSPSO can improve the tracking capability with high accuracy, less oscillations, and high robustness. To validate the proposed solution, a simulation and experimental benchmarking of a PV system are presented and analyzed. The obtained results show the effectiveness of the proposed solution compared with the classical MSPSO, fuzzy logic, and perturb and observe (P&O) control presented in other recent works.

show abstract

Section: Description and Modeling Of The Pv Conversion Chainmentioning

confidence: 99%

A new MPPT controller based on a modified multiswarm PSO algorithm using an adaptive factor selection strategy for partially shaded PV systems

Ben Regaya,

Farhani,

Hamdi

et al. 2024

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Discrete control techniques can perform well based on the switching properties of the Boost converter. The following methods are some of the most popular controllers: Sliding-mode (SM) control [7,8], Model Predictive Control(MPC) [9][10][11], Fuzzybased strategies [12,13], dead beat control [14,15], and Internal Model Control (IMC) [16,17]. The main advantages of Fuzzy and MPC are its robustness in load uncertainty and minimum distortion of the output signal; yet, it suffers from the high computational burden and slow dynamics.…”

Section: Introductionmentioning

confidence: 99%

Self‐tuning regulator adaptive controller design for DC‐DC boost converter with a novel robust improved identification method

Mollaee

Ghamari

Khavari

2022

IET Power Electronics

View full text Add to dashboard Cite

The design of a self-tuning regulator adaptive controller is presented for Boost converter using Pulse Width Modulation, which has been improved in dynamic with a novel Robust Improved Exponential Regressive Least Square identification scheme. Regarding the negative influences of harmful disturbances on the converters, the Minimum Degree Pole Placement technique is designed using an adaptive mechanism with an online identification technique, which is simple in structure and can provide more accurate parametric estimation and better efficiency in challenging situations, particularly against noise. Based on the right half-plane zero structure of the Boost converter, the open-loop system is called a non-minimum phase system which creates some challenging constraints for the controller design. The primary objective of designing a feedback loop controller is to ensure the stability of the system and appropriate regulation within a pre-determined range of operating conditions. This controller considers the system as a black-box structure; thus, the mathematical model of the system is not needed. This benefit can decrease the computational burden, and provides faster dynamics along with ease of implementation. Finally, the strength of this control strategy is tested by experimental and simulation results in different conditions.

show abstract

Energy-Feedback Load Simulation Algorithm Based on Fuzzy Control

Cai

Xie

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

In order to solve the problem that the traditional Proportional Integral (PI) controller cannot take into account both response speed and input current overshoot and cannot adapt to complex working conditions, an energy-feedback DC load simulation control algorithm based on fuzzy control was proposed. By adding a fuzzy adaptive PI setting to the traditional load simulation control algorithm, the dynamic response performance of the system and the accuracy of load simulation were improved. A small-signal model of a load analog boost circuit was established based on the state average method. Based on the phase margin, the PI parameter setting of series correction was completed, and the corresponding fuzzy control rules were formulated to identify the response state. Simulation results show that the regulation time of constant current load simulation based on a fuzzy control strategy is shortened by 75.2%, the steady-state error is reduced by 60%, and the overshoot is reduced from 28.3% to 7.47%. The prototype test results show that the maximum relative error of input current load simulation is only 4.50%, and the system response speed can meet the requirements of variable load simulation. The results show that the load simulation control algorithm based on fuzzy control has the characteristics of high precision and excellent dynamic characteristics.

show abstract

LPV Modeling of Boost Converter and Gain Scheduling MPC Control

Cited by 3 publications

References 11 publications

A new MPPT controller based on a modified multiswarm PSO algorithm using an adaptive factor selection strategy for partially shaded PV systems

A new MPPT controller based on a modified multiswarm PSO algorithm using an adaptive factor selection strategy for partially shaded PV systems

Self‐tuning regulator adaptive controller design for DC‐DC boost converter with a novel robust improved identification method

Energy-Feedback Load Simulation Algorithm Based on Fuzzy Control

Contact Info

Product

Resources

About