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 describes the tuning process of the proportional-resonant controller, taking into account the significant computational delay from the digital control system. Different structures of the controller and related contradicting results are discussed. Particular attention is paid to the stability domain and its dependence on different parameters of the proportional-resonant controller. The main outcome of this paper consists in the tuning approach guidelines for selection of controller parameters in case of significant digital system delay. An optimal controller structure and start-up current optimization are proposed. All results are confirmed by simulation and experimental setup.

Optimization and Implementation of the Proportional-Resonant Controller for Grid-Connected Inverter With Significant Computation Delay

Fourier-based image correlation is a powerful area-based image registration technique, which involves aligning images based on a translation model or similarity model by means of the image information and operation in the frequency domain. In recent years, Fourier-based image correlation has made significant progress and attracted extensive research interest in a variety of applications, especially in the field of photogrammetry and remote sensing, leading to the development of a number of subpixel methods that have improved the accuracy and robustness. However, to date, a detailed review of the literature related to Fourier-based image correlation is still lacking. In this review, we aim at providing a comprehensive overview of the fundamentals, developments, and applications of image registration with Fourier-based image correlation methods. Specifically, this review introduces the principal laws underlying these methods, presents a survey of the existing subpixel methods calculated both in the spatial domain and in the frequency domain, summarizes the major applications from three aspects, and discusses the challenges and possible directions of future research. This review is expected to be beneficial for researchers working in the relevant fields to obtain an insight into the current state of the art, to develop new variants, to explore potential applications, and to suggest promising future trends of image registration with Fourier-based image correlation.

Image Registration With Fourier-Based Image Correlation: A Comprehensive Review of Developments and Applications

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 presents a new control method to suppress current harmonics for permanent magnet synchronous linear motor (PMSLM) that is applied in the miniature microsecond laser cutting system. In the control method, the resonant–two-degree-of-freedom (R–2DOF) proportional–integral–derivative (PID) controller is proposed by combining a resonant controller and a two-degree-of-freedom (2DOF) PID controller. The current harmonic components are first analyzed. The resonant controller is subsequently added to the current loop in parallel to the traditional PI controller to suppress the current harmonic components. However, with the current harmonics suppression, the resonant controller can result in the overshoot in the current loop response. The 2DOF PID controller is adopted to reduce the overshoot. Thus, an R–2DOF PID controller is developed by combining the resonant controller and 2DOF PID controller. Meanwhile, the stability of the proposed controller is analyzed. Compared with the traditional PID controller and the Kalman filter, the proposed controller not only can suppress the current harmonics but can reduce the overshoot and thrust ripple as well. Finally, the simulation and experimental comparison results confirm the validity of the proposed control algorithm.

Combined Resonant Controller and Two-Degree-of-Freedom PID Controller for PMSLM Current Harmonics Suppression

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 is dedicated to studying a new position measurement method of a linear motor mover based on machine vision, which provides a novel idea for the detection of mover position. A rapid-precise measurement system based on a fence image and a phase correlation algorithm (PCA) is established. Multiline sampling and parallel computing methods are used to simplify the fence image directly; these methods can convert the 2-D image motion estimation into a simpler, 1-D signal process. To rapidly detect the mover position with high precision, a 1-D extended phase correlation algorithm (EPCA) is proposed. During position measurement, three key steps for the implementation are applied as follows. 1) Capture objective fence stripes using a high-speed camera installed beside the mover; 2) suppress the spectral leakage of discrete Fourier transform by a Hanning window function; and 3) calculate the displacement vector of the linear motor mover using a 1-D PCA and the least square method. Finally, the actual displacement of the mover can be obtained according to the system calibration. Experimental results demonstrate that the proposed 1-D EPCA implemented at measuring mover position is strongly robust and speedy with a detecting accuracy of 0.02 mm.

A New Rapid-Precision Position Measurement Method for a Linear Motor Mover Based on a 1-D EPCA

High-precision position measurement is very important for precision motion control of a linear motor. This paper presents an accurate mover position detection method with high precision stability based on temporal image correlation and fence image for the linear motors. First, sequence fence images with the information of mover position are recorded by a position measurement system for linear motor, and the integer pixel displacement of each image is obtained by image correlation method. Then, according to the continuity of the motion, the subpixel displacement can be extracted by fitting the integer pixel displacement. Finally, the displacement and velocity curves can be calculated according to system calibration. Compared to digital image measurement used in position detection of linear motors, the proposed method can detect mover position accurately with longer measurement range and smaller uncertainty. The moving least square (MLS) algorithm is used to ensure the stability of measurement uncertainty under different motion modes. Simulations and mover position detection experiments with different motion conditions were performed to demonstrate the effectiveness of the proposed method in linear motors. In this paper, the average measurement error of displacement is 0.006 mm with the maximum displacement of 120 mm. The maximum and average relative errors are less than 0.06% and 0.017%, respectively.

Precision Position Measurement of Linear Motors Mover Based on Temporal Image Correlation

The extended joint transformation correlation (EJTC) algorithm is proposed in this paper to meet the high-precision and anti-interference requirements of linear motor mover position measurement. An image measurement system for linear motor mover position is established. Stripe image movement sequences are captured using a high-speed camera. First, the image is preprocessed using a bilateral filtering method. Second, the joint power spectral function of adjacent stripe images is obtained on the basis of the joint transform correlation method. The zero-order diffraction peaks are eliminated, and interference from cross-correlated peak side lobes is avoided. Third, subpixel positioning is obtained using the oversampled discrete Fourier transform. Finally, the actual displacement of the stripe images can be obtained by combining the system calibration coefficients. An experimental measurement platform for a linear motor mover is established to verify the validity of the EJTC algorithm. The EJTC shows high measurement accuracy and strong anti-interference ability.

Hui Wang

Papers

Combined Resonant Controller and Two-Degree-of-Freedom PID Controller for PMSLM Current Harmonics Suppression

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

A New Rapid-Precision Position Measurement Method for a Linear Motor Mover Based on a 1-D EPCA

Precision Position Measurement of Linear Motors Mover Based on Temporal Image Correlation

Precise Position Detection of Linear Motor Movers Based on Extended Joint Transformation Correlation