.png){kind=icon}
返回
Efficient Nonlinear MPC by Leveraging LPV Embedding and Sequential Quadratic Programming
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ifacol.2025.10.115.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we present efficient solutions for the nonlinear program (NLP) associated with nonlinear model predictive control (NMPC) by leveraging the linear parameter-varying (LPV) embedding of nonlinear models and sequential quadratic programming (SQP). The corresponding quadratic program (QP) subproblem is systematically constructed and efficiently updated using the scheduling parameter from the LPV embedding, enabling fast convergence while adapting to the behavior of the controlled system. The method integrates the SQP approach into the LPV-MPC formulation based on inexact Hessians/Jacobians. Furthermore, the approach provides insight into the problem, its connection to SQP, and a clearer understanding of the differences between solving NMPC as an NLP and using the LPV-MPC approach, compared to similar methods in the literature. The efficiency of the proposed approach is demonstrated against state-of-the-art methods, including NLP algorithms, in control benchmarks and practical applications.Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Keyword:
Linear parametrically varying methodologies
Control of constrained systems
Nonlinear predictive control
Optimal control
Quadratic and nonlinear programming
期刊
I
IF:
0
论文数:
985
被引数:
0
机构
引用论文
CasADi: a software framework for nonlinear optimization and optimal controlCasADi: 非线性优化和最优控制的软件框架
PREAI
分享收藏Autonomous racing using Linear Parameter Varying-Model Predictive Control (LPV-MPC)使用线性参数变化模型预测控制 (lpv-mpc) 的自主赛车
OAAI
OAAI分享收藏This paper deals with model predictive control (MPC) for nonlinear systems using linear-parameter varying (LPV) embedding of the nonlinear dynamics (LPVMPC). The proposed LPVMPC can incorporate information of the future evolution of the scheduling parameter over the MPC prediction horizon with uncertainty bounds, which are used to construct anticipated scheduling tubes for robustification. Therefore, this approach is less conservative than the methods that only consider knowledge of the bounds on the scheduling parameter's rate of variation. The scheduling tubes are employed to synthesize online general polytopic invariant state tubes. The optimization problem of the proposed LPVMPC is a single quadratic program. Recursive feasibility is proven. A numerical example is presented for demonstrating the effectiveness of the proposed LPVMPC algorithm compared to nonlinear MPC and other standard approaches. © 2023 Elsevier Ltd. All rights reserved.本文使用非线性动力学 (LPVMPC) 的线性参数变化 (LPV) 嵌入来处理非线性系统的模型预测控制 (MPC)。拟议的LPVMPC可以在具有不确定性界限的MPC预测范围内合并调度参数的未来演变信息，这些信息可用于构建预期的调度管以进行鲁棒化。因此，与仅考虑调度参数的变化率的界限的知识的方法相比，该方法较不保守。调度管用于在线合成通用多主题不变状态管。所提出的LPVMPC的优化问题是单个二次规划。证明了递归的可行性。给出了一个数值示例，以证明与非线性MPC和其他标准方法相比，所提出的LPVMPC算法的有效性。©2023年爱思唯尔有限公司版权所有。.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏
查看更多内容