This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the gray wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d -axis current, the discretized state-space model of the PMSHM is augmented with the integral of rotor speed error and integral of d -axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in linear quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWO-based state feedback controller (SFC) with and without the penalty term, the conventional SFC, and the genetic algorithm enhanced proportional–integral controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed SFC with the penalty term in fast response.

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State Feedback Control for a PM Hub Motor Based on Gray Wolf Optimization Algorithm

This paper proposes a comprehensive framework for multiobjective design optimization of switched reluctance motors (SRMs) based on a combination of the design of experiments and particle swarm optimization (PSO) approaches. First, the definitive screening design was employed to perform sensitivity analyses to identify significant design variables without bias of interaction effects between design variables. Next, optimal third-order response surface (RS) models were constructed based on the Audze-Eglais Latin hypercube design using the selected significant design variables. The constructed optimal RS models consist of only significant regression terms, which were selected by using PSO. Then, a PSO-based multiobjective optimization coupled with the constructed RS models, instead of the finite-element analysis, was performed to generate the Pareto front with a significantly reduced computational cost. A sample SRM design with multiple optimization objectives, i.e., maximizing torque per active mass, maximizing efficiency, and minimizing torque ripple, was conducted to verify the effectiveness of the proposed optimal design framework.

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Multiobjective Optimization of Switched ReluctanceMotors Based on Design of Experiments and ParticleSwarm Optimization

This article investigates the efficient robust design optimization of a five-phase permanent magnet (PM) hub motor for electric vehicles. Besides the requirement of high-performance, like high torque density, low torque ripple and efficiency, fault-tolerant operation capability are also considered in the design optimization. To ensure that the motor performance is not sensitive to the variations of manufacturing tolerances, robust design optimization is employed to the investigated motor. To improve the fault tolerant capability of the motor, the motor performances under fault operation are also considered in the optimization. A Fuzzy-based sequential Taguchi robust optimization method is proposed to improve the comprehensive performance and save computing time. The proposed method is efficient because it holds the advantages of Taguchi method, fuzzy theory, and sequential optimization strategy. The motor performance is improved significantly by using the proposed method. Experimental results verify the accuracy of the model used in this study.

Robust Design Optimization of a Five-Phase PM Hub Motor for Fault-Tolerant Operation Based on Taguchi Method

With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

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Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Metamodel-based simulation optimization: A systematic literature review

This paper focuses on the multiobjective design optimization of the permanent magnet synchronous linear motors (PMSLMs), which are applied to a high-precision laser engraving machine. A novel efficient multiobjective design optimization method for a PMSLM is proposed to achieve optimal performances as indicated by high average thrust, low thrust ripple, and low total harmonic distortion at different running speeds. First, based on the finite-element analysis (FEA) data, a regression machine learning algorithm, called an extreme learning machine (ELM), is introduced to solve the calculation modeling problem by mapping out the nonlinear and complex relationship between input structural factors and output motor performances. Comparative simulation experiments conducted using the traditional analytical modeling method and another machine learning modeling method, i.e., support vector machine, confirm the superiority of the ELM. Then, a new bionic intelligent optimization algorithm, called the gray wolf optimizer algorithm, is used to search the best optimization performances and structural parameters by performing iteration optimization calculation for multiobjective functions. Finally, FEA and prototype motor experiments prove the effectiveness and validity of the proposed method.

An Efficient Multiobjective Design Optimization Method for a PMSLM Based on an Extreme Learning Machine

This paper proposes a high-performance current regulation scheme to enhance the current bandwidth and robustness of the permanent magnet synchronous linear motor (PMSLM). First, the disturbance caused by parameter variations and thrust ripple is considered in the modeling of PMSLM. Based on the model, a two-degree-of-freedom (2-DOF) controller consisting of improved predictive current control (PCC) and adaptive internal model control is derived. The improved PCC is used to overcome time delay and increase current control bandwidth, whilst the adaptive internal model is utilized to estimate the disturbance caused by parameter variations to compensate PCC. Second, a new discrete-time Jacobian linearization observer is designed to estimate the thrust ripple. The actual thrust ripple can be suppressed by injecting the estimated value into the control system. Subsequently, a comparatively smoother output thrust and higher precision position tracking is obtained. Finally, the precise test platform based on semiphysical system AD5435 is established. The experimental results verify the validity of the proposed approach.

Robust High Bandwidth Current Regulation for Permanent Magnet Synchronous Linear Motor Drivers by Using Two-Degree-of-Freedom Controller and Thrust Ripple Observer

This paper focuses on the thrust ripple reduction in permanent magnet synchronous linear motors (PMSLMs), which are applied to laser cutting machines. To meet the high-precision performance requirements of laser cutting machines, a novel auxiliary suppression device, tuned viscoelastic damper (TVD), is designed to reduce the thrust ripple of PMSLM. First, TVD is designed with the structure and motion characteristics of PMSLM. The PMSLM with TVD is an integrated structure. Then, the equivalent magnetization method is used to analyze the PMSLM thrust, which is the excitation of TVD. Under the excitation of the PMSLM thrust, the damping force of TVD is expressed by the Kelvin model and validated by finite-element analysis (FEA). Subsequently, the thrust of PMSLM with TVD is obtained by adding the PMSLM thrust and TVD damping force. Finally, the thrust ripple rate is selected as a performance index of TVD in reducing the thrust ripple. Simulation is performed to confirm the theoretical analysis. A prototype of PMSLM with TVD is manufactured and experiments are conducted to validate the effectiveness of the proposed auxiliary suppression device.

Thrust Ripple Reduction in Permanent Magnet Synchronous Linear Motor Based on Tuned Viscoelastic Damper

This paper focuses on the optimal design of permanent magnet linear synchronous motor (PMLSM) with trapezoidal permanent magnets (TPMs) at multispeed that are applied in laser-engraving machines. Particle swarm optimization (PSO) and signal-to-noise (SN) ratio method are proposed to minimize the thrust ripple and maintain average thrust. First, an analytical model based on equivalent magnetic charge method is presented to analyze the PMLSM with TMPs, and the accuracy of the proposed analytical method is validated by finite-element method. Then, the PSO is applied to optimize the pole pitch $(\tau)$ , air gap length $(\delta)$ , bottom angle of TPM $(\theta)$ , and magnet height $(h)$ to obtain the optimized structure parameters at a single speed. Application of PSO is repeated at different speeds to obtain their optimal structure parameters. Finally, the SN ratio method is used to synthetically optimize the parameters obtained at different speeds to acquire the optimized parameters at multispeed. Experimental results of prototype can confirm the validity and superiority of the method.

Optimal Design of Permanent Magnet Linear Synchronous Motors at Multispeed Based on Particle Swarm Optimization Combined With SN Ratio Method

In this article, a water-cooled structure with water-cooled plates between the primary double-layer windings field of the primary structure under short-term high overload conditions and periodic operations is calculated. Second, the heat dissipation effects of two different waterway topologies are designed and compared. Based on the thermal network analysis model and multiphysical field coupling model, the heat dissipation effect of the water-cooled motor is obtained. In order to obtain the maximum sustainable working current that the motor can withstand under water-cooled conditions, a fast calculation model—response surface model—is established and the electromagnetic performance of the water-cooled motor is obtained. The analysis results show that the temperature rise of the water-cooled motor decreases greatly, and the average thrust increases by 2.73 times. Finally, the calculated and tested results of the prototype are compared and analyzed.

Zhongyan He

Papers

An Efficient Multiobjective Design Optimization Method for a PMSLM Based on an Extreme Learning Machine

Robust High Bandwidth Current Regulation for Permanent Magnet Synchronous Linear Motor Drivers by Using Two-Degree-of-Freedom Controller and Thrust Ripple Observer

Thrust Ripple Reduction in Permanent Magnet Synchronous Linear Motor Based on Tuned Viscoelastic Damper

Optimal Design of Permanent Magnet Linear Synchronous Motors at Multispeed Based on Particle Swarm Optimization Combined With SN Ratio Method

Temperature Field Calculation and Water-Cooling Structure Design of Coreless Permanent Magnet Synchronous Linear Motor