Self-optimizing and model-adaptive control of electrical drive systems with predictive planning of pulse patterns

通过脉冲模式的预测规划对电力驱动系统进行自优化和模型自适应控制

• 批准号: 405351394
• 负责人: Professor Dr.-Ing. Joachim Böcker
• 金额: --
• 依托单位: Fachgebiet Leistungselektronik und Elektrische Antriebstechnik (LEA)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/405351394?language=en
• 关键词: Self; optimizing; model; adaptive; control

Electric drives are used in many purposes, e.g. in automated production lines, power plants, centrifuges, hoists and cranes as well as in road and rail vehicles. Meanwhile, most drives are operated in a controlled fashion where, besides the primary control objective as e.g. the speed, further aspects as minimization of power losses or wear play a role. In the past decades, great research efforts have been made dedicated to issues e.g. of control strategies, modulation and identification methods. Most approaches, however, are focusing on isolated problems or specific applications. A generalizable drive or control concept, which independently configures itself to the respective application-specific requirements and adapts continuously, does not exist.This proposal presents a concept of a multi-level control and operational management for electric drives which is based on the principle of self-optimization. The concept focuses on permanent-magnet synchronous motors with 2-level inverters. However, its modular structure allows to be extended to other configurations with low effort (e.g. induction motors or multi-level inverters). The concept is subdivided into three levels. At the lowest level, the classic control for currents, torque, speed and position is allocated. The parameters of this control are continuously adapted by a superimposed optimization level which makes use of electrical, thermal and mechanical models in order to predict the drive behavior and to intervene in the subordinate control level in case of need. The top level includes functions of self-optimization and adaptation. The latter examines the residuals between model estimation and measurements within the subordinate layer and matches at runtime, i.e. both during commissioning and over the entire drive life, the model parameters. Additionally, by means of self-optimization, the optimization goals are dynamically adjusted due to changing external or internal conditions. In that way it will be ensured that the control concept can be applied for a wide range of applications without manual adjustment.After simulative studies, the proposed control concept will be validated on several test benches for three exemplary applications: 1st: traction drive (rail and road vehicles), 2nd: high-speed continuous drives (pumps, fans, etc.), and 3rd: servo drives (e.g. machine tools). As the validation scenarios include very different requirement profiles, the generalizability of the proposed concept should be demonstrated. After successful completion of the project, the transfer to further drive configurations is planned.

电动驱动器用于许多目的，例如在自动生产线中，发电厂，离心机，起重机以及道路和铁路车辆。同时，大多数驱动器都是以受控方式操作的，除了主要控制目标之外，例如速度，进一步的方面随着功率损失或磨损的最小化发挥作用。在过去的几十年中，已经为问题提供了巨大的研究工作，例如控制策略，调制和识别方法。但是，大多数方法都专注于孤立的问题或特定应用。不存在一个可概括的驱动或控制概念，该概念将自己独立地配置为特定于应用程序的需求并连续调整。该提案提出了基于自我选择原理的电动驱动器多级控制和操作管理的概念。该概念侧重于带有2级逆变器的永久性同步电动机。但是，其模块化结构允许将其扩展到其他努力（例如感应电动机或多级逆变器）的其他配置。该概念被细分为三个层次。在最低水平上，分配了电流，扭矩，速度和位置的经典控制。该控件的参数通过叠加的优化水平不断适应，该水平利用电气，热和机械模型来预测驱动行为并在需要的情况下干预下属控制水平。最高级别包括自我优化和适应的功能。后者检查了下属层中模型估计与测量值之间的残差以及在运行时的匹配，即在调试期间和整个驱动寿命（模型参数）。此外，通过自我优化，由于外部或内部条件的变化，优化目标会动态调整。以这种方式，将确保可以将控制概念用于无手动调整的各种应用程序。在模拟研究后，将在几个测试工作台上验证拟议的控制概念，用于三个示例应用：第一：牵引力：牵引力驱动器（轨道和路线车辆），第二：第二：高速连续驱动器（高速连续驾驶员，泵，粉丝，粉丝，粉丝等工具）和3级机器，以及3级机构：ED。由于验证方案包含非常不同的要求概况，因此应证明该概念的普遍性。成功完成项目后，计划将转移到进一步的驱动配置。