Review of Method for System Identification on Motors

Arifin, Bustanul; Nugroho, Agus Adhi; Suprapto, BhaktiYudo; Prasetyowati, Sri Arttini Dwi; Nawawi, Zainuddin

doi:10.23919/eecsi53397.2021.9624259

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…The features and modeling accuracy of these methods are not the same. Among them, the artificial neural network is often used in system identification or modeling of complex problems [2] and is suitable for accurate modeling of the nonlinear characteristics of SRMs.…”

Section: Introductionmentioning

confidence: 99%

Modeling of nonlinear characteristics of switched reluctance motor based on optimized RBF neuron network

Li,

Ma,

2023

J. Phys.: Conf. Ser.

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The strong nonlinearity of switched reluctance motor (SRM) makes it difficult to establish an accurate mathematical model of its electromagnetic characteristics. In this paper, an artificial neural network modeling method is adopted to estimate the nonlinear flux linkage characteristics of a four-phase SRM. Based on the samples obtained by experimental measurement, a radial basis function neural network is trained and further optimized using the K-Means algorithm. The modeling accuracy of the optimized flux linkage model is analyzed in detail and also verified by dynamic simulation of a direct instantaneous torque control system of the motor.

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Section: Introductionmentioning

confidence: 99%

Modeling of nonlinear characteristics of switched reluctance motor based on optimized RBF neuron network

Li,

Ma,

2023

J. Phys.: Conf. Ser.

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“…This reveals the transfer function that describes the input-output relationship. Thus, the transfer function is the base for developing a control system for the DC motor, and the system identification is a way to find a mathematical model for the corresponding dynamic system [5]. DC motors are suitable for mechatronic systems, including those miniaturized.…”

Section: Introductionmentioning

confidence: 99%

Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control

et al. 2022

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Brushed (B) and Brushless (BL) DC motors constitute the cornerstone of mechatronic systems regardless their sizes (including miniaturized), in which both position and speed control tasks require the application of sophisticated algorithms. This manuscript addresses the initial step using time series analysis to forecast Back EMF values, thereby enabling the elaboration of real-time adaptive fine-tuning strategies for PID controllers in such a control system design problem. An Auto-Regressive Moving Average (ARMA) model is developed to estimate the DC motor parameter, which evolves in time due to the system’s imperfection (i.e., unpredictable duty cycle) and influences the closed-loop performance. The methodology is executed offline; thus, it highlights the applicability of collected BDC motor measurements in time series analysis. The proposed method updates the PID controller gains based on the Simulink ™ controller tuning toolbox. The contribution of this approach is shown in a comparative study that indicates an opportunity to use time series analysis to forecast DC motor parameters, to re-tune PID controller gains, and to obtain similar performance under the same perturbation conditions. The research demonstrates the practical applicability of the proposed method for fine-tuning/re-tuning controllers in real-time. The results show the inclusion of the time series analysis to recalculate controller gains as an alternative for adaptive control.

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“…To include all effects due to friction, gear transmissions, temperature and non-linearities, additional measurements of the motor need to be taken, i.e. speed step responses, to estimate parameters [9] or derive motor transfer functions [10]. Nevertheless, these approximations often lack accuracy, especially when both constant high speeds and high torques are required in the applications.…”

Section: Introductionmentioning

confidence: 99%

Practical Approach to Characterize Realistic Motor Dynamics for Robotic Simulation Independent of the Use Case

Schmidt

Gumpert

Schreiber

et al. 2022

2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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Incorporating realistic actuator dynamics in robotic simulations is key to a successful simulation-to-reality transfer. But real actuation chains are often complex and impossible to model with analytical methods alone. Although it is feasible to reverse-engineer the actuator dynamics from hardware measurements, this requires the completed robotic system to be already available. To enable the inclusion of realistic actuator dynamics in robot models also during the design phase or for initial controller tuning, this work presents an alternative hands-on approach for actuator characterization. Based on actuator measurements taken independently of the overall system integration, a model expression for the actuator is derived. This can be added to the simulation of any robotic system. To showcase this concept, we present the workflow for a robotic leg with a Series Elastic Actuation chain. We create a simulation of the leg incorporating the derived actuator model and show its validity through comparison with analogous hardware. The observed motor and link dynamics of both cases show close correspondence without increasing the needed computation times with respect to a simulation without actuation. Thus, the proposed method offers a promising approach to include realistic actuator dynamics during the design and development process of robotic applications.

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Review of Method for System Identification on Motors

Cited by 7 publications

References 34 publications

Modeling of nonlinear characteristics of switched reluctance motor based on optimized RBF neuron network

Modeling of nonlinear characteristics of switched reluctance motor based on optimized RBF neuron network

Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control

Practical Approach to Characterize Realistic Motor Dynamics for Robotic Simulation Independent of the Use Case

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