Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor

This paper focuses on designing a gain-scheduled (G-S) state feedback controller (SFC) for synchronous reluctance motor (SynRM) speed control with non-linear inductance characteristics. The augmented model of the drive with additional state variables is introduced to assure precise control of selected state variables (i.e. angular speed and d-axis current). Optimal, non-constant coefficients of the controller are calculated using a linear-quadratic optimisation method. Non-constant coefficients are approximated using an artificial neural network (ANN) to assure superior accuracy and relatively low usage of resources during implementation. To the best of our knowledge, this is the first time when ANN-based gain-scheduled state feedback controller (G-S SFC) is applied for speed control of SynRM. Based on numerous simulation tests, including a comparison with a signum-based SFC, it is shown that the proposed solution assures good dynamical behaviour of SynRM drive and robustness against q-axis inductance, the moment of inertia and viscous friction fluctuations

PSO based feedrate optimization with contour error constraints for NURBS toolpaths

Paper presented at MMAR 2016 conference (Międzyzdroje, Poland, 29 Aug.-1 Sept. 2016)Generation of a time-optimal feedrate profile for CNC machines has received significant attention in recent years. Most methods focus on achieving maximum allowable feedrate with constrained axial acceleration and jerk without considering manufacturing precision. Manufacturing precision is often defined as contour error which is the distance between desired and actual toolpaths. This paper presents a method of determining the maximum feedrate for NURBS toolpaths while constraining velocity, acceleration, jerk and contour error. Contour error is predicted during optimization by using an artificial neural-network. Optimization is performed by Particle Swarm Optimization with Augmented Lagrangian constraint handling technique. Results of a time-optimal feedrate profile generated for an example toolpath are presented to illustrate the capabilities of the proposed method

Neural Network Contour Error Predictor in CNC Control Systems

Paper presented as poster presentation at MMAR 2016 conference (Międzyzdroje,Poland, 29 Aug.-1 Sept. 2016)This article presents a method for predicting contour error using artificial neural networks. Contour error is defined as the minimum distance between actual position and reference toolpath and is commonly used to measure machining precision of Computerized Numerically Controlled (CNC) machine tools. Offline trained Nonlinear Autoregressive networks with exogenous inputs (NARX) are used to predict following error in each axis. These values and information about toolpath geometry obtained from the interpolator are then used to compute the contour error. The method used for effective off-line training of the dynamic recurrent NARX neural networks is presented. Tests are performed that verify the contour error prediction accuracy using a biaxial CNC machine in a real-time CNC control system. The presented neural network based contour error predictor was used in a predictive feedrate optimization algorithm with constrained contour error

A Performance Study on Synchronous and Asynchronous Update Rules for A Plug-In Direct Particle Swarm Repetitive Controller

In this paper two different update schemes for the recently developed plug-in direct particle swarm repetitive controller (PDPSRC) are investigated and compared. The proposed approach employs the particle swarm optimizer (PSO) to solve in on-line mode a dynamic optimization problem (DOP) related to the control task in the constant-amplitude constant-frequency voltage-source inverter (CACF VSI) with an LC output filter. The effectiveness of synchronous and asynchronous update rules, both commonly used in static optimization problems (SOPs), is assessed and compared in the case of PDPSRC. The performance of the controller, when synthesized using each of the update schemes, is studied numerically

Comparative Analysis of the Steady-State Model Including Non-Linear Flux Linkage Surfaces and the Simplified Linearized Model when Applied to a Highly-Saturated Permanent Magnet Synchronous Machine—Evaluation Based on the Example of the BMW i3 Traction Motor

This paper presents a finite element method (FEM)-based model, which describes the magnetic circuit of the BMW i3 traction machine. The model has been reconstructed based on data available in the public domain. The reader is provided with numerical data regarding flux linkage surfaces in d- and q-axes, as well as with all the information needed to develop a space-vector model of the machine in steady-state, taking into consideration the non-linearity of the magnetic circuit. Hence, the data of a highly-saturated machine from a renowned product are provided, which can serve as a reference design for research. After that, torque curve and partial load operation points are calculated. Finally, the machine model is linearized and the calculations are repeated with the simplified linearized model. The results from both models are then compared with each other. This comparison is intended to assess the magnitude of the expected inaccuracies, when simplified analytical tools are applied to highly-saturated machines (which are the backbone of automotive electrical drivetrains). It is especially important with regard to preliminary design of electrical drivetrains, as at this stage detailed machine geometry and materials are not known

Parallel computing applied to auto-tuning of state feedback speed controller for PMSM drive

Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors can be used to reduce computation time by using parallel computing. The parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive

High quality repetitive control system for a grid-tied converter under distorted grid voltage conditions – design and implementation

The paper features a grid-tied converter with a repetitive current controller. Our goal here is to demonstrate the complete design workflow for a repetitive controller, including phase lead, filtering and conditional learning. All key parameters, i.e., controller gain, filter and fractional phase lead, are designed in a single optimization procedure, which is a novel approach. The description of the design and optimization process, as well as experimental verification of the entire control system, are the most important contributions of the paper. Additionally, one more novelty in the context of power converters is verified in the physical system – a conditional learning algorithm to improve transient states to abrupt reference and disturbance changes. The resulting control system is tested experimentally in a 10 kW converter