Neural Network Augmented Physics Models for Systems with Partially Unknown Dynamics: Application to Slider-Crank Mechanism

Dynamic models of mechatronic systems are abundantly used in the context of motion control and design of complex servo applications. In practice, these systems are often plagued by unknown interactions, which make the physics-based relations of the system dynamics only partially known. This paper presents a neural network augmented physics (NNAP) model as a combination of physics-inspired and neural layers. The neural layers are inserted in the model to compensate for the unmodeled interactions, without requiring direct measurements of these unknown phenomena. In contrast to traditional approaches, both the neural network and physical parameters are simultaneously optimized, solely by using state and control input measurements. The methodology is applied on experimental data of a slider-crank setup for which the state dependent load interactions are unknown. The NNAP model proves to be a stable and accurate modeling formalism for dynamic systems that ab initio can only be partially described by physical laws. Moreover, the results show that a recurrent implementation of the NNAP model enables improved robustness and accuracy of the system state predictions, compared to its feedforward counterpart. Besides capturing the system dynamics, the NNAP model provides a means to gain new insights by extracting the neural network from the converged NNAP model. In this way, we discovered accurate representations of the unknown spring force interaction and friction phenomena acting on the slider mechanism

Modelling and Analysis of Dynamic Servo Error of Heavy Vertical Machining Centre Considering Nonlinear Factors

Data Availability Statement: Not applicable.Copyright © 2023 by the authors. The dynamic servo error of heavy-duty vertical machining centres is one of the decisive factors affecting the machining accuracy of large and complex parts. Due to the characteristics of large mass, large load, and the large travel distance of the machine tool, non-linear factors such as friction, backlash, and lateral shift are more likely to cause unstable behaviours such as stick-slip and oscillation of the servo feed system of the machine tool, and reduce the performance and servo accuracy of the motion axis. In this paper, to consider the influence of non-linear factors such as friction, backlash, and lateral shift, an appropriately simplified representation of the mechanical transmission system of the ball screw has been used. According to the control structure of the Siemens 840D numerical control system, a theoretical model of the servo feed system for the heavy-duty vertical machining centre was established based on three-loop control. Then, the single-axis and double-axis closed-loop simulation models of the servo feed system were built in Simulink, and the influence pattern of control parameters and nonlinear factors on the dynamic servo error was obtained through simulation analysis. Finally, the validity of the theoretical model for the servo feed system was verified through a comprehensive comparison of simulation and experimental outcomes. This encompasses an analysis of the control system Bode plots, critical stick-slip velocity, and tracking errors in the X-axis with linear motion. The validation provides theoretical guidance for parameter design and mechanical adjustments of the servo feed system in heavy-duty vertical machining centres.This research is supported by Construction of High-level University—Leading Program of First-class Graduate Education (No. 10-23-304-011, 10-23-304-012) and 2023 Shanghai Education Commission Young Teacher Training Subsidy Program

Adaptive control of the interior permanent magnet synchronous motors

Thesis contains: pages – 117, drawings – 38, tables – 23. The goal of the of the thesis lies in development of the control methods of the IPMSM with the purpose of its research and improvement of efficiency and performance of the electromechanical system. In this thesis, analytical review of the inductance determination methods for the IPMSM is presented. After that two tests for inductance determination of the interior permanent magnet synchronous motors are proposed, analyzed and experimentally verified. Four methods are proposed to use to obtain static and dynamic inductances from the tests data. Speed and position control algorithms are derived basing on the non saturated model of the motor and its effectiveness was researched by means of experiment and simulation for small saturated motors. After that position control algorithm with adaptation to the mechanical parameters is designed and tested via simulation. Stability is proved using the second Lyapunov method. Derived algorithms provide asymptotic tracking of the controlled coordinates, and decoupling of the direct current component and mechanic coordinate control subsystems.Магістерська дисертація містить: 117 сторінок, 38 рисунків, 23 таблиці. Метою роботи є розробка та розвиток методів керування явнополюсними синхронними двигунами з постійними магнітами, спрямований на покращення ефективності електромеханічної системи. В роботі представлено аналітичний огляд методів визначення індуктивностей IPMSM. Запропоно та експериментально впроваджено два тести для визначення індуктивностей. Отримані в тестах данi пропонується обробити чотирьма методами для отримання значень статичної та динамічної індуктивностей. Розроблено алгоритми керування швидкістю та подоженням на основі моделі, що не враховує насичення. Ефективність алгоритмів досліджена шляхом моделювання та експериментально для двигуна з низьким рівнем насичення. Після цього синтезовано алгоритм керування положенням з адаптацією до механічних параметрів. Стабільність системи доведена за допомогою другого методу Ляпунова. Отримані алгоритми забезпечують асимптотичне відпрацювання контрольованих координат та розв’язку підсистеми керування прямою компонентою струму та підсистемою керування механічними координатами