Search

In this article, a predictive position control method based on a novel input-constrained nonlinear dynamic model (NDM) is proposed for time-varying position tracking of planar motors. The motivation lies in the possible utility of this method for motion systems. This method uses NDM subject to input constraint to deal with actuator saturation rather than uses a constrained optimization problem, such that it differs from conventional model predictive control. The NDM is represented in state–space equations (SSEs) to describe dynamic behaviors of the system constituted by the planar motor and an input saturation module. In contrast to linear SSEs, this model has the same linear vector-matrix form; the difference is that it applies saturation functions of states to replace states of state equation in linear SSEs for representing nonlinearity. By employing a self-designed neural network, the parameters of this model are determined via experimental sample data. With this model, a nonlinear multistep predictive model subject to input constraint is developed. Additionally, an explicitly analytical state feedback control law is approximately deduced by solving an unconstrained optimization problem subject to the nonlinear predictive model. Finally, simulation and experimental results show the effectiveness of the proposed method.

This article proposes a new position estimation method based on an unsaturated flux linkage model for planar switched reluctance motors (PSRMs). The bipolar-voltage-injection method is used to measure the flux linkage, and it is found that ripples caused by iron loss and aluminum eddy current with different amplitudes appear at the troughs of the flux curve. The effect of the losses on the flux linkage measurement is revealed in this article, and the loss compensation models are built based on the analysis. The improved flux linkage model is proposed with the loss compensation models. Then, the position estimation method is given with the improved flux linkage model. Flux linkage measurement result shows that the model with loss compensation has higher accuracy than the conventional model. Initial and planar-motion position estimation experiments are carried out for the PSRM. The initial position estimation error is from −0.4 to 0.4 mm. The planar-motion position estimation errors are from −1.1 to 1.1 mm in the ${X}$ -axis and from −1.2 to 1.0 mm in the ${Y}$ -axis, and the effectiveness of the proposed flux linkage model is verified.

This article proposes a predictive position control of long-stroke planar motors to achieve micrometer-scale positioning under long-stroke time-varying trajectory tracking for use in high-precision positioning applications. The motivation consists in the potential application of model predictive control in long-stroke planar motors for high-precision positioning systems. This control is the first of its kind for planar motors. A dynamic model of a long-stroke planar motor developed in the laboratory is built, and then a predictive model is established to predict future positions of the motor. An additional term is introduced to a cost function to improve the positioning accuracy, which provides an output feedback control action to the motor for reducing the model error of the predictive model. By minimizing the cost function, an analytically explicit solution of the optimal control sequence is obtained, which makes the computational burden of the controller low. The stability of the control system is discussed based on the closed-loop state-space model. Moreover, the selection of stable control parameters is theoretically given. Simulation and experimental results demonstrate the effectiveness of the proposed control method of long-stroke planar motors for use in high-precision positioning applications.

This article proposes a predictive position control method of planar motors using trajectory gradient soft constraint with attenuation coefficients in the weighting matrix to achieve high-precision, time-varying, and long-stroke positioning. Based on a built dynamics model of a planar motor developed in the laboratory, a predictive model is established to predict the future positions of the motor. To improve the positioning precision, a soft constraint defined by a trajectory gradient difference between the gradients of the reference position and predictive position sequences is introduced to the cost function. Then, an explicitly analytical solution of the optimal control is obtained by minimizing the cost function. To highlight the stronger effects of the trajectory gradients closer to the current time, the attenuation coefficients are applied to the weighting matrix of the added soft constraint. The stability of the control system is proved employing the linear quadratic optimal control method and the Lyapunov stability theory. Moreover, the time complexity is discussed based on the analytical control action to show low computational burden of the proposed method. Finally, the given simulation and experimental results demonstrate the effectiveness of the proposed method to achieve high-precision time-varying positioning for planar motors.

An accurate mover position is important for a planar switched reluctance motor (PSRM) to achieve precision positioning; however, position sensors are susceptible to the influence from harsh conditions and are relatively expensive. In this article, an assistant-mover-based position estimation method is proposed to increase the reliability and reduce the cost of a PSRM system. The method is based on a mapping relationship between the flux density and position of the assistant mover. First, the configuration of the assistant movers is presented. Then, an analytical model of the flux density of the assistant mover is developed by using an equivalent magnetic circuit method. Based on the analytical model, the parameters of the assistant mover are determined. According to the flux density characteristics, a position estimation algorithm with the advantages of not requiring previous experimental data, simple computation, and small memory consumption is developed. Moreover, considering the deviation in the characteristics of each assistant mover, a compensation strategy is proposed to obtain better position estimation accuracy. Finally, the proposed method is implemented with the PSRM system. The effectiveness of the proposed position estimation method is verified experimentally.

To improve the ability of anti-interference and robustness of planar motors, this article proposed a proportional-derivative (PD) position control method using a nonlinear extended state observer (NESO). Firstly, the dynamic model of a planar motor is given. And then, the lumped disturbance of the planar motor system is observed by using a developed nonlinear extended state observer, in which the observer parameters are committed to ensuring the stability of the NESO. The observed disturbance is compensated to the output of the PD controller, to enhance the ability of disturbance suppression and robustness of the planar motor control system. Moreover, simulation results prove that the proposed method can effectively suppress the disturbance of the planar motor to achieve satisfactory control performance and verifies the effectiveness and feasibility.

Aiming at the chattering problem of sliding mode control in the trajectory tracking of lower limb exoskeleton rehabilitation robot (LLERR), this paper proposes a fast-terminal sliding mode control based on dynamic boundary layer (DFTSMC) for LLERR. First, based on the fast-terminal sliding mode surface and the double power reaching law, the compensation term of the control law is designed. Then, a dynamic boundary layer is designed to suppress the chattering of the system state. Finally, based on the virtual prototype of LLERR, a co-simulation is carried out with Matlab and Recurdyn multi-body dynamics software. The traditional PID control based on the computed torque method, the traditional fast-terminal sliding mode control (FTSMC) and the DFTSMC were compared. The control performance of fast terminal sliding mode control and the simulation results show that the DFTSMC improves the convergence speed of the system state, suppress the chattering of the sliding mode control and enhances the robust performance of LLERR.

In order to decouple the electromagnetic thrust and levitation force of the 6 degree-of-freedom (DOF) maglev motor developed in the laboratory, a new four-phase current distribution strategy of the 6-DOF maglev is proposed in this paper. First, the motor structure is given, in which the mover is composed of four Halbach permanent magnet arrays, and the stator coils consist of a printed circuit board (PCB). Then, the mathematical models of electromagnetic thrust and levitation force of the motor are established based on the Lorentz force law. Moreover, the four-phase current of the stator coils is deduced by coordinate transformation and minimum norm constraint, and the relationship among the electromagnetic thrust force, levitation force, ac axis current, and dc axis current are formulated. The decoupling model of electromagnetic thrust and levitation force of the motor is built. Finally, the simulation results show the effectiveness of the proposed strategy.

In order to reduce the position measurement deviation caused by the machining and sensor installation errors, the paper proposes a zero-position correction method for a novel 6-DOF magnetic levitation motor using excitation current. A 6-DOF measurement system for the motor is introduced. The excitation current model is derived in terms of the mechanical structure and electromagnetic characteristics of the motor. Based on the relationship between the excitation current and the position of the mover when the mover is stably levitated, the zero-position offsets could be calculated to correct the zero-position of the measurement system. The experimental results verify the effectiveness of the proposed method.

This paper develops a Sobol sensitivity analysis model of thrust force of linear switched reluctance motors (LSRMs), to quantitatively analyze the coupled influence of parameters on the thrust force ripple. A nonlinear thrust force model of a developed LSRM is first given. Using Sobol sensitivity analysis method, the local and global sensitivity indexes are deduced according to thrust force model. In addition, applying the deduced sensitivity indexes, local and global sensitivity results of thrust force are obtained, and the quantitatively coupled influence of parameters on the thrust force ripple is presented and analyzed.

The identification of dynamic model parameters of the permanent magnet synchronous linear motors (PMSLMs) is the foundation of high-performance motor control. To address the low accuracy of modelling of PMSLMs, a parameter identification method of the PMSLM is proposed by using multi-innovation least squares (MILS) approach in this paper. The linear transfer function of the PMSLM is first built. Then the MILS approach based on the recursive least squares (RLS) algorithm is developed to identify the parameters of the built model. Finally, the simulation results of MILS and RLS algorithms are shown, and the effectiveness of the proposed method is verified.

This paper proposes a drive circuit for single-phase brushless direct current (BLDC) fan motors to reduce their acoustic noise. First, the torque ripple of a single-phase BLDC motor is analysed theoretically. Then, a drive circuit that is capable of regulating the DC-link voltage during commutation to weaken the negative effect of current lag is proposed to reduce the acoustic noise of single-phase BLDC fan motors. Furthermore, the characteristics of the proposed drive circuit are discussed theoretically using a deduced analytical model of the proposed drive circuit. Additionally, simulations based on the finite element method and experiments are performed. The torque ripple and acoustic noise generated by the conventional and proposed drive circuits are compared. The simulation results show that the torque ripple is reduced by 16.1% when the proposed drive circuit is used, and the experimental results illustrate that the acoustic noise generated by the second harmonic of the torque ripple is reduced by 7.3%. Ultimately, the effectiveness of the proposed drive circuit is verified.

This paper proposes a novel wireless-moving-levitating system (WMLS) using multiple magnetic fields to explore the effectiveness of wireless-moving-levitating manipulation in hard-to-reach environm...

Electric distribution cabinets are critical components in the power distribution pipeline. Surface defect detection plays an important role in the production process. It not only guarantees product quality but also affects the brand reputation. In particular, the boundaries of metallic cabinets are more vulnerable to be damaged than other surface areas. Thus, boundary defect detection is a bottleneck problem that needs to be solved. To deal with this issue, a method based on image moment feature anomaly is developed to detect the defects on cabinet surfaces. The boundary edges from an image of the produced cabinet are first extracted using a hybrid of edge detection and boundary skeleton extraction. Then, the boundary areas are divided into small and identical size image blocks. A Gaussian distribution model of normal image blocks without defects is established. Finally, the anomaly features of image blocks with defects are extracted to identify the defect image blocks based on the Gaussian distribution model and a segmentation threshold. Two experiments are carried out. One is to determine the optimal illumination intensity for image acquisition and the optimal threshold of defect detection. The other is to evaluate the performance of the defect detection method. This developed approach can be applied in the non-destructive and effective inspection of electric distribution cabinets and provides a feasible solution for metallic product quality assurance.

This paper presents the design, control, and experimental performance evaluation of a long-stroke planar switched reluctance motor (PSRM) for positioning applications. Based on comprehensive consideration of the electromagnetic and mechanical characteristics of the PSRM, a motor design is first developed to reduce the force ripple and deformation. A control scheme with LuGre friction compensation is then proposed to improve the positioning accuracy of the PSRM. Furthermore, this control scheme is proven to ensure the stable motion of the PSRM system. Additionally, the response speed and steady-state error of the PSRM system with this control scheme are theoretically analyzed. Finally, the experimental results are presented and analyzed. The effectiveness of the precision long-stroke motion of the PSRM and its promise for use in precision positioning applications are verified experimentally.

This paper proposes a position estimation method for a planar switched reluctance motor (PSRM). In the method, a second-order sliding mode observer (SMO) is used to achieve sensorless control of a PSRM for the first time. A sensorless closed-loop control strategy based on the SMO without a position sensor for the PSRM is constructed. The SMO mainly consists of a flux linkage estimation, an adaptive current estimation, an observing error calculation, and a position estimation section. An adaptive current observer is applied in the current estimation section to minimize the error between the measured and estimated currents and to increase the accuracy of the position estimation. The flux linkage is estimated by the voltage equation of the PSRM, and the estimated flux linkage is then used to estimate the phase current in the adaptive current observer. To calculate the observing error of the SMO using the measured and estimated phase currents, the observing error of the thrust force is introduced to replace the immeasurable state error of the position and speed of the mover. The sliding surface is designed based on the error of the thrust force, and stability analysis is given. Once the sliding surface is reached, the mover position is then estimated accurately. Finally, the effectiveness of the proposed method for the PSRM is verified experimentally.

To achieve satisfactory positioning performance considering actuator saturation, this article proposes a constrained predictive position control (CPPC) approach using the threshold function for the planar switched reluctance motor (PSRM). For the PSRM designed in our lab, a dynamic model expressed as a discrete state-space model is established. The input thrust force of the position controller is constrained by applying a nonlinear threshold function. Based on the constrained thrust force, a prediction model with constraints, which can predict motor positions in the future, is built. With the cost function developed from the prediction model, the control input is derived through minimizing the cost function. Finally, the results of experiments demonstrate the presented CPPC can availably restrain the input thrust force as well as realize the satisfactory positioning performance of the PSRM.

This article proposes a predictive position control (PPC) approach with stable control parameters of planar switched reluctance motors (PSRMs) for 2-D positioning systems. The motivation is to determine the controller parameters for guaranteeing a stable closed-loop PSRM control system. For the PSRM designed in our lab, the dynamic behaviors are described employing a discrete-time state-space model. Then, a prediction model is built for the position prediction in the future on account of the established state-space model. By minimizing the defined cost function based on the built prediction model, the optimal control sequence is derived. Moreover, the stability analysis is presented and the stable control parameters are determined by using the closed-loop state-space model. Finally, the experimental results verify the validity of PPC approach.

In the field of welding robotics, visual sensors, which are mainly composed of a camera and a laser, have proven to be promising devices because of their high precision, good stability, and high safety factor. In real welding environments, there are various kinds of weld joints due to the diversity of the workpieces. The location algorithms for different weld joint types are different, and the welding parameters applied in welding are also different. It is very inefficient to manually change the image processing algorithm and welding parameters according to the weld joint type before each welding task. Therefore, it will greatly improve the efficiency and automation of the welding system if a visual sensor can automatically identify the weld joint before welding. However, there are few studies regarding these problems and the accuracy and applicability of existing methods are not strong. Therefore, a weld joint identification method for visual sensor based on image features and support vector machine (SVM) is proposed in this paper. The deformation of laser around a weld joint is taken as recognition information. Two kinds of features are extracted as feature vectors to enrich the identification information. Subsequently, based on the extracted feature vectors, the optimal SVM model for weld joint type identification is established. A comparative study of proposed and conventional strategies for weld joint identification is carried out via a contrast experiment and a robustness testing experiment. The experimental results show that the identification accuracy rate achieves 98.4%. The validity and robustness of the proposed method are verified.

This paper proposes a nonlinear flux linkage model in 2-D plane for the planar switched reluctance motor (PSRM). The inputs of the proposed model are the 2-D positions and the current, and the output is the flux linkage. The proposed model is established via a cascade-forward backpropagation neural network (CFNN). The designed CFNN consists of four layers: one input layer, two hidden layers, and one output layer. The first hidden layer has 20 neurons with a tan-sigmoid transfer function, and the second hidden layer has 20 neurons with a log-sigmoid transfer function. The output layer is a pure linear layer. The sample set with 179 755 samples is obtained experimentally in a dSPACE-based PSRM system by applying the dc excitation method. The sample set is divided into three sets. 35% and 30% of the samples are randomly chosen as the training sample set and validation sample set, respectively, and the remaining samples are utilized as the test sample set to assess the generalization performance of the CFNN-based model. According to the results of the test sample set, the maximum relative error is 11.05% and the mean relative error is 0.42% when the current ranges from 1 to 9 A. The CFNN has the capability to build a multi-input nonlinear model. The CFNN-based model is capable of reflecting the variations of flux linkage in 2-D plane caused by manufacturing tolerances. The effectiveness of the CFNN-based model is finally verified.

In this paper, a new rotary-linear switched-reluctance motor with a magnetic decoupled structure is introduced. The rotational motion of the motor, managed by a sliding-mode controller, is coordinated with the proportional-derivation linear motion by using a fuzzy logic block. Consequently, both rotational and linear motions can be realized simultaneously. In addition, the decoupled magnetic paths of the motor in two directions are analyzed by finite-element method, with the force and torque measurements suggesting the feasibility of the motor. Both the simulation and experimental results show that the motor is totally decoupled in the linear and rotary directions. The linear movement and the rotation at the same time validate the effectiveness of the motor design and the proposed control method, with highly accurate responses for the two degrees of freedom movement.

In this paper, a planar flux-switching permanent magnet (PFSPM) motor, which is the first of its kind for planar motors, is designed and analyzed. First, the topology structure, modeling, and operating principle of the PFSPM motor are clarified. Then, the initial dimension of the PFSPM motor is determined in terms of its topology structure. Based on the initial dimension, the width of the iron core and the length of the permanent magnet are optimized to increase the thrust force and reduce the cogging force. In addition, a 3-D finite-element analysis (FEA) is performed. Finally, the experiments are conducted based on a developed prototype of the PFSPM motor with the optimized dimension. The results demonstrate that the thrust force is increased by 21%, and the cogging force is reduced by 56% via FEA; the thrust force of the experimental measurement is largely consistent with that determined via the FEA.

Machine vision-based object detection techniques have been widely used in product inspection, defect detection, and dimension measurement. These techniques have largely improved the efficiency of industrial production and increased the level of production autonomy. However, demands on advance hardware design and image processing algorithms are needed for the quality inspection of a large-complex-surface. In order to solve this problem, a hybrid surface defect detection method is developed. An image of the product surface is first divided into two areas: background with similar features and special pattern area, such as product trademarks. For the background area, defects have significant differences in gray intensity from the normal area. Fault detection is conducted using a gradient threshold segmentation method that can limit segmentation errors arising from uneven illuminations. For the special pattern area, image registration and image difference are adopted to detect defects, which are adaptive to irregular image contents with discontinuous shapes and appearances. Experimental results indicate that the proposed method achieves about 1.21 times and 2.94 times higher accuracy, in F-measure , for large-complex-surface defect detection than the traditional methods of gradient threshold segmentation and template matching, respectively. The proposed image processing technique can be applied in product quality inspections.

This paper proposes a novel multiple-stress-based predictive model (MSBPM) to rapidly assess the lifetime of light-emitting diodes (LEDs). The MSBPM addresses the lifetime estimation of LEDs with respect to temperature, humidity, and current; these three stresses are rarely considered simultaneously in the assessment of reliability. Using several degradation data sets from accelerated life tests (ALTs) without using extrapolation method, a designed adaptive genetic algorithm is employed to identify five unknown parameters of the MSBPM. A simulation of the proposed MSBPM is presented as validation. By applying the degradation data from the ALTs under high stresses, an MSBPM for the LEDs is established. Under the nominal conditions of 25 °C/22.5% RH and a current of 0.35 A, the lifetime of the LED is estimated using the established MSBPM. The effectiveness of the proposed MSBPM is further verified through the estimated results.

This paper proposes a multi-segment sliding mode (MSSMC) method for the planar switched reluctance motor (PSRM) developed in the laboratory to achieve precision positioning. The mechanical structure, the mathematic model and the control strategy are first given. Based on the mathematic model, the MSSMC strategy is then designed, which consists of an acceleration segment, a deceleration segment, and a positioning segment. Additionally, the stability analysis is discussed for the closed-loop system with the designed MSSMC strategy. Simulation is carried out based on MATLAB/Simulink. Compared to the conventional sliding mode control (SMC), the MSSMC is capable of weakening and better steady-state and dynamic performances were achieved. The effectiveness of the proposed MSSMC method is verified through simulation.

This paper presents an analytical nonlinear model of a magnetic levitation system (MLS)developed in the laboratory for applications of contactless operations at a microscale. Cylindrical permanent magnets and iron-core electromagnetics are used to build a desired hybrid magnetic field of the MLS. Three linear Hall-effect sensors are employed to detect the position of the levitation object, in which two sensors are used to detect the two-dimensional horizontal position of the levitation object and the third sensor is used to detect the levitation object whether it is in the working space. The nonlinear magnetic flux density model of the MLS is first deduced and then the magnetic force of the levitation object is derived by using the deduced model. Based on the deduced model, the mathematical relation of the current supplied to the electromagnet, the magnetic force and position of the levitation object is obtained and analyzed. Furthermore, the installation of the linear Hall-effect sensors was determined theoretically. Finally, the effectiveness of the nonlinear modeling of the MLS developed in the laboratory is validated by finite element analysis.

A model predictive position control (MPPC) method based on a parametric regression model is proposed in this paper, to achieve high-precision positioning for a planar switched reluctance motor (PSRM) developed in the laboratory. First, the mechanism model of the PSRM system represented by a discrete-time state space model is given. To reduce modeling error caused by the uncertainty, a two-order parametric regression model is then used to describe the PSRM. With the thrust force input signal and the position output signal, the parameters of this model are obtained by using a recursive least squares method with forgetting factor. Based on the built model, a predictive model is established to predict the future position. By defining a cost function, an optimized control action sequence is obtained with the predictive model. Additionally, a comparison is performed experimentally. The experimental results verify the effectiveness of the proposed MPPC for high-precision positioning.

This paper proposes a position estimation method for the planar switched reluctance motor (PSRM). This method estimates the mover position of the PSRM using a sliding mode observer (SMO). The proposed method directly estimate the position of the mover according to the terminal measurement of phase voltage and current, without a need for additional detection devices or internal memory, achieving a more compact and cost-effective PSRM drive. Sensorless closed-loop control strategy without position sensor for the PSRM is proposed based on the SMO. A simulation model of the SMO is built, and the effectiveness of the position estimation method for the PSRM is verified by simulation.