Physics-guided neural networks for feedforward control: From consistent identification to feedforward controller design

Model-based feedforward control improves tracking performance of motion systems, provided that the model describing the inverse dynamics is of sufficient accuracy. Model sets, such as neural networks (NNs) and physics-guided neural networks (PGNNs) are typically used as flexible parametrizations that enable accurate identification of the inverse system dynamics. Currently, these (PG)NNs are used to identify the inverse dynamics directly. However, direct identification of the inverse dynamics is sensitive to noise that is present in the training data, and thereby results in biased parameter estimates which limit the achievable tracking performance. In order to push performance further, it is therefore crucial to account for noise when performing the identification. To address this problem, this paper proposes the use of a forward system identification using (PG)NNs from noisy data. Afterwards, two methods are proposed for inverting PGNNs to design a feedforward controller for high-precision motion control. The developed methodology is validated on a real-life industrial linear motor, where it showed significant improvements in tracking performance with respect to the direct inverse identification

Identification and parameter-varying decoupling of a 3-DOF platform with manipulator

The paper describes identification and a new parameter-varying decoupling method for a 3-degree-of-freedom (DOF) platform with a manipulator on top of it, which is magnetically levitated by 9 voice-coil actuators. The identification has been performed in closed-loop using two different indirect approaches. In the first approach time-domain data of the system were processed using Ho-Kalman algorithm. The second approach was based on frequency-response measurements. The 3 DOFs of the platform are coupled and the coupling is even varying as the manipulator on top is moving. In order to design separate SISO controllers for each DOF of the platform, a new decoupling method has been developed which uses frequency response measurements of the system obtained for different positions of the manipulator

Identification and parameter-varying decoupling of a 3-DOF platform with manipulator

The paper describes identification and a new parameter-varying decoupling method for a 3-degree-of-freedom (DOF) platform with a manipulator on top of it, which is magnetically levitated by 9 voice-coil actuators. The identification has been performed in closed-loop using two different indirect approaches. In the first approach time-domain data of the system were processed using Ho-Kalman algorithm. The second approach was based on frequency-response measurements. The 3 DOFs of the platform are coupled and the coupling is even varying as the manipulator on top is moving. In order to design separate SISO controllers for each DOF of the platform, a new decoupling method has been developed which uses frequency response measurements of the system obtained for different positions of the manipulator

Optimal Design of Special High Torque Density Electric Machines based on Electromagnetic FEA

Electric machines with high torque density are essential for many low-speed direct-drive systems, such as wind turbines, electric vehicles, and industrial automation. Permanent magnet (PM) machines that incorporate a magnetic gearing effect are particularly useful for these applications due to their potential for achieving extremely high torque density. However, when the number of rotor polarities is increased, there is a corresponding need to increase the number of stator slots and coils proportionally. This can result in manufacturing challenges. A new topology of an axial-flux vernier-type machine of MAGNUS type has been presented to address the mentioned limitation. These machines can attain high electrical frequency using only a few stator coils and teeth, which can simplify construction and manufacturing under certain conditions. Additionally, the inclusion of auxiliary small teeth within the stator main teeth can generate a noteworthy increase in output torque, making it a unique characteristic of this motor. By analyzing the operating principle of the proposed VTFM PM machine, possible pole-slot combinations have been derived. The process of designing an electric machine is complicated and involves several variables and factors that must be balanced by the designer, such as efficiency, cost, and performance requirements. To achieve a successful design, it is crucial to employ multi-objective optimization. Using a 3D FEA model can consider the impact of magnetic saturation, leakage flux, and end effects, which are not accounted for in 2D. Optimization using a 3D parametric model can offer a more precise analysis. Validating the machine\u27s performance requires prototyping a model and testing it under different operating conditions, such as speed and load, which is a crucial step. This approach provides valuable insights into the machine\u27s behavior, allowing the identification of any areas for improvement or weaknesses. A large-scale multi-objective optimization study has been conducted for an axial-flux vernier-type PM machine with a 3-dimensional (3D) finite element analysis (FEA) to minimize the material cost and maximize the electromagnetic efficiency. A detailed study for torque contribution has indicated that auxiliary teeth on each stator main teeth amplify net torque production. A prototype of optimal design has been built and tested

Electromechanical actuator bearing fault detection using empirically extracted features

Model parameter estimation when coupled with Principal Component Analysis (PCA) and Bayesian classification techniques form a potentially effective fault detection scheme for Electromechanical Actuators (EMAs). This work uses parameter estimation algorithms based on linear system identification methods, derives a novel feature extraction algorithm based on PCA and analyzes its performance through simulations and experiments. A Bayesian classifier is used to create well defined EMA health classes from the extracted features. Research contributions on fault detection in EMAs are significant because EMA faults and their detection are not yet well understood. Potential future applications - such as in primary flight control actuation in aircraft - require that quality fault detection systems be in place. Therefore, fault detection of EMAs is a vast area of ongoing research where highly capable solutions are gradually becoming available. Prior work in parameter estimation methods for feature extraction in DC motor drives - which includes EMAs - are amongst those available. While PCA is a popular feature extraction solution in a number of frequency-based fault detection approaches, the use of PCA for feature extraction from model parameters for detecting bearing faults in EMAs has not been previously reported. In this work, a linear difference model is applied to the EMA system data such that fault information is distributed amongst the estimated model parameters. A direct comparison of the parameter estimates from healthy and degraded systems offers little insight into health conditions because of the weak effects of faults on the signal data. However, the application of PCA to uncorrelate the linearly correlated model parameters while minimizing the loss of variance information from the data effectively brings out fault information. The present algorithm is successfully applied to data collected from a Moog MaxForce EMA. The results are consistent and display effective fault detection characteristics, making the developed approach a suitable starting point for future work

Study of a Bulk Superconducting Synchronous Machine（バルク超電導同期機に関する研究）

東京海洋大学博士学位論文 2019年度(2020年3月) 応用環境システム学 課程博士 甲第550号指導教員:和泉充全文公表年月日:2020-06-22東京海洋大学201

Electromagnetic muscle actuators

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 181-194).Actuator performance represents a key constraint on the capability of many engineered devices. Performance of these devices is often exceeded by their muscle-powered natural counterparts, inspiring the development of new, "active material" actuators. This thesis reconsiders a traditional actuator, the linear permanent magnet motor, as a form of active material actuator, and presents new, unified scaling and magnetic field models for its performance. This active material motor model predicts that motors composed of large numbers of very small, actively-cooled repeat units, similar to the architecture of biological muscles, can provide greatly enhanced force density over extant designs. Our model is constructed by considering the motor winding as an active material, with its performance limited by the diffusion of waste heat. This allows a quantitative scaling model for the motor constant and force-to-mass ratio to be built for the case of a winding immersed in a homogeneous magnetic field. This model is then modified with a small set of dimensionless parameters to describe the performance penalties imposed by the use of practical sources of magnetic field, specifically periodic arrays of permanent magnets. We explain how to calculate these parameters for a variety of different types of magnet arrays using analytical magnetic field and heat transfer models, and present a new field model for tubular linear motors having improved numerical stability and accuracy. We illustrate the use of our modeling approach with two design case studies, a motor for flapping-wing flying and an actuator for high-performance controllable needle-free jet injection. We then validate our predictions by building and testing a novel water-cooled motor using a tubular double-sided Halbach array of magnets, with a mass of 185 g, a stroke of 16 mm, and a magnetic repeat length of 14.5 mm. This motor generates a continuous force density of 140 N/kg, and has a motor constant of nearly 6 N/[square root]W, both higher than any previously reported motor in this size class.by Bryan Paul Ruddy.Ph.D