基于观测器的多线性电机牵引系统分层分布式模型预测控制。

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

摘要

本文提出了一种基于观测器的分层分布式模型预测控制(MPC)策略,以确保多线性电机牵引系统的速度一致性。首先,考虑了通信拓扑结构,以确保信息交换。其次,每个代理的控制架构分为上层和下层。上层利用分布式 MPC 方法跟踪领导者的速度。下层采用分散式 MPC 方法跟踪上层控制器发送的指令信号。此外,为了消除干扰的负面影响,还设计了一个非线性干扰观测器。然后,我们通过适当设计 Lyapunov 方程证明了整个系统的渐进稳定性。最后,通过多次仿真验证了所提策略的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Observer-based hierarchical distributed model predictive control for multi-linear motor traction systems

This paper proposes an observer-based hierarchical distributed model predictive control (MPC) strategy for ensuring speed consistency in multi-linear motor traction systems. First, a communication topology is considered to ensure information exchange. Secondly, the control architecture of each agent is divided into upper layers and lower layers. The upper layer utilizes a distributed MPC method to track the leader’s speed. The lower layer uses a decentralized MPC method to track the command signals sent by its upper layer controller. In addition, to eliminate the negative impact of disturbance, a nonlinear disturbance observer is designed. We then prove the asymptotic stability of the entire system by properly designing the Lyapunov equation. Finally, the feasibility of the proposed strategy is verified based on several simulations.

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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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