Lin Yang;Jiarong Wu;Liping Luo;Weilin Wu;Hailong Ma
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引用次数: 0
Abstract
Single-inductor dual-output (SIDO) buck converter has been widely applied in fields of portable electronic products and smart homes due to their advantages of small size and multiple outputs. However, cross regulation seriously deteriorates the stability of the converter. In addition, parasitic parameters of circuit components reduce the conversion efficiency of the converter and have a significant impact on cross regulation. To suppress the cross regulation and improve the conversion efficiency of the nonideal SIDO buck converter, a multiobjective optimization control strategy is proposed. Considering the parasitic parameters of circuit components, a large signal model of the converter is established, and a predictive equation is constructed based on the model predictive control theory. Furthermore, an objective function for optimizing output error is established based on weighting the output voltage error and inductor current error. Then, a method is proposed to achieve real-time correction of the state variable reference values. The power loss of the system is analyzed and weighted onto the objective function to improve the conversion efficiency. Compared with the common mode voltage-differential mode voltage control method, simulation and experimental results show that the proposed strategy provides smaller cross regulation and better dynamic performance. The conversion efficiency is improved by 6.6%.
期刊介绍:
Journal Name: IEEE Transactions on Industrial Electronics
Publication Frequency: Monthly
Scope:
The scope of IEEE Transactions on Industrial Electronics encompasses the following areas:
Applications of electronics, controls, and communications in industrial and manufacturing systems and processes.
Power electronics and drive control techniques.
System control and signal processing.
Fault detection and diagnosis.
Power systems.
Instrumentation, measurement, and testing.
Modeling and simulation.
Motion control.
Robotics.
Sensors and actuators.
Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems.
Factory automation.
Communication and computer networks.