A Predictive Control Method for DC Converter

Abstract

The invention discloses a DC converter predictive control method. Firstly, the output state differential equation is established according to the DC converter circuit model, and then the discretization process is performed to obtain a predictive model. According to the optimal control amount of the current control cycle, according to the set rule List a number of control quantities that may appear in the next control cycle, bring them into the prediction model to obtain a number of predicted output results, and obtain the optimal output results through cost function comparison, and lock the optimal control quantity at the next moment; the transmission power equation of the DC converter The inductance observation model is obtained through the discretization process, and the real-time observation of the actual circuit inductance is carried out to realize the real-time optimization and update of the inductance value of the prediction model. While retaining the excellent dynamic performance of the traditional predictive control method, the present invention does not need to perform complicated square root calculations and reduces the computational burden; through real-time observation of the inductance value, the problem of the mismatch between the actual resonant inductance and the predictive model inductance value is solved, and the method is improved. accuracy and system robustness.

Description

technical field [0001] The invention belongs to the technical field of power electronics, and relates to a direct current converter predictive control method, in particular to a direct current converter predictive control method based on resonant inductance parameter identification. Background technique [0002] With the development of power electronic technology and new control theory, DC converters are widely used in aerospace, energy supply and other fields, and higher requirements are put forward for the robustness and fast response characteristics of DC converters. The traditional PI control strategy will make the system have good robustness, but due to its inherent hysteresis, the dynamic characteristics of the system are poor; while the Model Predictive Control (MPC) is more dynamic than the PI control in terms of dynamic response. It has great advantages, but it relies too much on the model, and the mismatch between the prediction model and the actual circuit will le...

AI Technical Summary

Problems solved by technology

The traditional PI control strategy will make the system have good robustness, but due to its inherent hysteresis characteristics, the dynamic characteristics of the system are poor; while the model predictive control (Model Predictive Control, MPC) is better than the PI control in terms of dynamic response. It has great advantages, but it relies too much on the model, and the mismatch between the prediction model and the actual circuit will lead to poor robustness of the system, resulting in a certain error between the output result and the target result

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