适用于Boost变换器的自适应模型预测控制算法

An Adaptive Model Predictive Control for DC-DC Boost Converters

相比于传统的PI控制,模型预测控制（model predictive control,MPC）具有动态响应快、避免调整控制参数,以及可增加系统约束等优点,因此被广泛应用到电力电子控制领域。然而,系统模型参数的不匹配通常会导致控制系统产生稳态误差,对于Boost变换器电流控制尤为严重。因此,该文针对Boost变换器提出一种简单有效的模型预测控制方法,可解决因未知的电感电阻和输入电压引起的模型不匹配问题,并且只需一步预测即可实现控制目标。另外,所提出的方法是属于连续控制集模型预测控制（continuouscontrol set model predictive control,CCS-MPC）,使用固定的开关频率。仿真和实验结果证明了所提出方法的有效性。

Compared with the traditional PI control method, model predictive control （MPC） strategy is widely used in the field of power electronic control due to its advantages such as fast dynamic response, avoiding tuning control parameters and additive system state variables constraints. However, in general, the uncertainty of system model parameters makes an error in steady state, especially for DC-DC Boost converters＇ current control. A simple MPC strategy for DC-DC Boost converters is proposed in this work, which can solve the problem of model mismatch effectively, including unknown inductor resistance and input voltage, and it requires only one-step prediction horizon to govern the converter. Moreover, the proposed controller belongs to the continuous control set MPC （CCS-MPC） family, which provides a constant switching frequency. Simulation and experiment results prove the effectiveness of the proposed approach.