基于最小衰减率多面体不变集的鲁棒模型预测控制

Robust Model Predictive Control Based on Polyhedral Invariant Sets with Minimum Decay Rates

  • 摘要: 针对一类具有输入输出约束的多胞体结构线性变参数系统,提出了一种基于最小衰减率多面体不变集的鲁棒模型预测控制算法,算法分为在线和离线两个部分.为增强系统控制效果,提高系统响应速度,离线算法首先采用寻求状态变量的最小衰减率的方法优化出一系列状态变量及相应的状态反馈控制律,然后构建出相应的多面体不变集序列;在线算法根据当前实测状态变量,在多面体不变集序列内确定状态变量所处的最小多面体不变集,通过在线优化得出系统的控制输入.给出了鲁棒模型预测控制算法的详细步骤和系统的闭环稳定性证明.仿真结果验证了本算法的有效性,表明本算法使系统的闭环响应更为快速和稳定.

     

    Abstract: We propose a robust model predictive control algorithm based on polyhedral invariant sets with minimum decay rates for a class of polytopic linear variable parameter systems with input and output constraints. The proposed algorithm is divided into an online part and offline part. To enhance the system control effect and improve the system response speed, the offline algorithm first seeks a minimum decay rate of the state variables to optimize a series of state variables and the corresponding state feedback control laws, and then constructs the corresponding polyhedral invariant set sequences. Based on the current measured state variables, the online algorithm determines the minimum polyhedral invariant set of the state variables in polyhedral invariant set sequences, and the control input of the system is obtained through online optimization. The detailed steps of the robust model predictive control algorithm and the closed-loop stability proof of the system are given. The simulation results verify the effectiveness of the proposed algorithm and indicate that the proposed algorithm makes the closed-loop response of the system more rapid and stable.

     

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