| 引用本文: | 张逸刚,黄道平,刘乙奇.受概率约束的离散时变不确定系统的随机模型预测控制[J].控制理论与应用,2025,42(12):2459~2467.[点击复制] |
| ZHANG Yi-gang,HUANG Dao-ping,LIU Yi-qi.Stochastic model predictive control for discrete time-varying uncertain systems with chance constraint[J].Control Theory & Applications,2025,42(12):2459~2467.[点击复制] |
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| 受概率约束的离散时变不确定系统的随机模型预测控制 |
| Stochastic model predictive control for discrete time-varying uncertain systems with chance constraint |
| 摘要点击 118 全文点击 16 投稿时间：2024-09-30 修订日期：2025-09-12 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.40522 |
| 2025,42(12):2459-2467 |
| 中文关键词 随机模型预测控制 时变系统 概率约束 随机加性扰动 凸组合 |
| 英文关键词 stochastic model predictive control time-varying systems probabilistic constraints: random additive dis turbances convex combination |
| 基金项目 国家自然科学基金重大研究计划–培育项目(92467106),国家自然科学基金面上项目(62273151,62073145),广东省基础与应用基础研究基金 项目(2021B1515420003), 广东省普通高校创新团队项目(2023KCXTDO72),先进造纸联合实验室开放课题项目(20241645)资助. |
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| 中文摘要 |
| 在模型预测控制方法(MPC)中,模型的不确定性主要源于外部干扰、输入噪声和时变参数等因素,这些因
素可能导致预测误差,从而,降低控制性能.本文针对一类具有加性扰动的离散时变系统,提出了一种新型随机模型
预测控制(SMPC)算法. 该算法能够在特定约束条件下有效预测系统的未来行为,并优化控制输入,以实现预定的性
能目标.为实现本文所提出的控制方法,本文采用李雅普诺夫稳定性理论保证系统的闭环稳定性,运用凸组合技术
应对系统参数的时变性,并利用凸优化方法计算名义系统的控制增益.此外,使用逆累积分布函数将概率约束转化
为确定性约束.最后,文章通过数值仿真实验对本文所提出的方法进行验证.仿真结果表明,随机模型预测控制策
略能够更好地适应随机变化的环境,并在面对随机干扰时保持较高的控制精度,并且具有相对较低的保守性. |
| 英文摘要 |
| The model uncertainty in the model predictive control (MPC) method mainly arises from factors such as
external disturbances, input noise, and time-varying parameters, all of which may lead to prediction errors and thus reduce
control performance. This paper proposes a novel stochastic model predictive control (SMPC) algorithm for a class of
discrete time-varying systems with additive disturbances. The algorithm can effectively predict the system’s future behavior
and optimize the control input under specific constraints to achieve predetermined performance goals. To implement the
control method proposed in this paper, the Lyapunov stability theory is employed to ensure the closed-loop stability of the
system, convex combination techniques are used to address the time-varying nature of the system parameters, and convex
optimization methods are utilized to calculate the control gain of the nominal system. Additionally, the inverse cumulative
distribution function is used to transform probabilistic constraints into deterministic constraints. Finally, the paper verifies
the proposed method through numerical simulation experiments. The simulation results show that the SMPC strategy can
better adapt to the randomly changing environment, maintain high control accuracy when facing random disturbances, and
has relatively low conservatism. |
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