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Article
Publication date: 10 May 2018

Guo Yi, Jianxu Mao, Yaonan Wang, Hui Zhang and Zhiqiang Miao

The purpose of this paper is to consider the leader-following formation control problem for nonholonomic vehicles based on a novel biologically inspired neurodynamics approach.

Abstract

Purpose

The purpose of this paper is to consider the leader-following formation control problem for nonholonomic vehicles based on a novel biologically inspired neurodynamics approach.

Design/methodology/approach

The interactions among the networked multi-vehicle system is modeled by an undirected graph. First, a distributed estimation law is proposed for each follower vehicle to estimate the state including the position, orientation and linear velocity of the leader. Then, a distributed formation tracking control law is designed based on the estimated state of the leader, where a bio-inspired neural dynamic is introduced to solve the impractical velocity jumps problem. Explicit stability and convergence analyses are presented using Lyapunov tools.

Findings

The effectiveness and efficiency of the proposed control law are demonstrated by numerical simulations and physical vehicle experiments. Consequently, the proposed protocol can successfully achieve the desired formation under connected topologies while tracking the trajectory generated by the leader.

Originality/value

This paper proposes a neurodynamics-based leader–follower formation tracking algorithm for multiple nonholonomic vehicles.

Details

Assembly Automation, vol. 38 no. 5
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 27 May 2014

Huihuang Zhao, Yaonan Wang, Zhijun Qiao and Bin Fu

The purpose of this paper is to develop an improved compressive sensing algorithm for solder joint imagery compressing and recovery. The improved algorithm can improve the…

Abstract

Purpose

The purpose of this paper is to develop an improved compressive sensing algorithm for solder joint imagery compressing and recovery. The improved algorithm can improve the performance in terms of peak signal to noise ratio (PSNR) of solder joint imagery recovery.

Design/methodology/approach

Unlike the traditional method, at first, the image was transformed into a sparse signal by discrete cosine transform; then the solder joint image was divided into blocks, and each image block was transformed into a one-dimensional data vector. At last, a block compressive sampling matching pursuit was proposed, and the proposed algorithm with different block sizes was used in recovering the solder joint imagery.

Findings

The experiments showed that the proposed algorithm could achieve the best results on PSNR when compared to other methods such as the orthogonal matching pursuit algorithm, greedy basis pursuit algorithm, subspace pursuit algorithm and compressive sampling matching pursuit algorithm. When the block size was 16 × 16, the proposed algorithm could obtain better results than when the block size was 8 × 8 and 4 × 4.

Practical implications

The paper provides a methodology for solder joint imagery compressing and recovery, and the proposed algorithm can also be used in other image compressing and recovery applications.

Originality/value

According to the compressed sensing (CS) theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The findings provide fundamental guidelines to improve performance in image compressing and recovery based on compressive sensing.

Details

Soldering & Surface Mount Technology, vol. 26 no. 3
Type: Research Article
ISSN: 0954-0911

Keywords

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Article
Publication date: 21 June 2011

Feng Yin, Yaonan Wang and Shuning Wei

This paper aims to develop a new real‐time effective method for solving the inverse kinematics (IK) problem, especially for those manipulators with high‐dimensional…

Abstract

Purpose

This paper aims to develop a new real‐time effective method for solving the inverse kinematics (IK) problem, especially for those manipulators with high‐dimensional nonlinear kinematic equations.

Design/methodology/approach

The paper transforms the IKs problem into a minimization problem. Then, a novel meta‐heuristic algorithm, called the electromagnetism‐like method (EM), is used to solve this equivalent problem. Moreover, in order to further improve the computational efficiency and accuracy of EM, a hybrid method which combines EM with the Davidon‐Fletcher‐Powell (DFP) method is proposed.

Findings

The results showed that EM is a powerful yet easy algorithm for solving the IKs problem of robot manipulators. Its complexity is independent on the characteristics of the kinematic equations involving dimensionality and the degree of nonlinearity. Moreover, EM can be used as an accompanying algorithm for DFP method to get better precision at a lower iteration number.

Originality/value

The method developed in this paper is a generalized approach that is efficient enough to obtain IK solutions independent of robot geometry and the number of degrees of freedom.

Details

Industrial Robot: An International Journal, vol. 38 no. 4
Type: Research Article
ISSN: 0143-991X

Keywords

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Article
Publication date: 27 April 2012

Yaonan Wang and Xiru Wu

The purpose of this paper is to present the radial basis function (RBF) networks‐based adaptive robust control for an omni‐directional wheeled mobile manipulator in the…

Abstract

Purpose

The purpose of this paper is to present the radial basis function (RBF) networks‐based adaptive robust control for an omni‐directional wheeled mobile manipulator in the presence of uncertainties and disturbances.

Design/methodology/approach

First, a dynamic model is obtained based on the practical omni‐directional wheeled mobile manipulator system. Second, the RBF neural network is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using the learning ability of neural networks, RBFNARC can co‐ordinately control the omni‐directional mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the sliding mode control.

Findings

Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

Originality/value

In this paper, an adaptive robust control scheme using neural networks combined with sliding mode control is proposed for crawler‐type mobile manipulators in the presence of uncertainties and disturbances. RBF neural networks approximate the system dynamics directly and overcome the structured uncertainty by learning. Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

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Article
Publication date: 18 January 2016

Zhendong He, Yaonan Wang, Feng Yin and Jie Liu

When using a machine vision inspection system for rail surface defect detection, many complex factors such as illumination changes, reflection inequality, shadows, stains…

Abstract

Purpose

When using a machine vision inspection system for rail surface defect detection, many complex factors such as illumination changes, reflection inequality, shadows, stains and rust might inevitably deform the scanned rail surface image. This paper aims to reduce the influence of these factors, a pipeline of image processing algorithms for robust defect detection is developed.

Design/methodology/approach

First, a new inverse Perona-Malik (P-M) diffusion model is presented for image enhancement, which takes the reciprocal of gradient as feature to adjust the diffusion coefficients, and a distinct nearest-neighbor difference scheme is introduced to select proper defect boundaries during discretized implementation. As a result, the defect regions are sufficiently smoothened, whereas the faultless background remains unchanged. Then, by subtracting the diffused image from the original image, the defect features will be highlighted in the difference image. Subsequently, an adaptive threshold binarization, followed by an attribute opening like filter, can easily eliminate the noisy interferences and find out the desired defects.

Findings

Using data from our developed inspection apparatus, the experiments show that the proposed method can attain a detection and measurement precisions as high as 93.6 and 85.9 per cent, respectively, while the recovery accuracy remains 93 per cent. Additionally, the proposed method is computationally efficient and can perform robustly even under complex environments.

Originality/value

A pipeline of algorithms for rail surface detection is proposed. Particularly, an inverse P-M diffusion model with a distinct discretization scheme is introduced to enhance the defect boundaries and suppress noises. The performance of the proposed method has been verified with real images from our own developed system.

Details

Sensor Review, vol. 36 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 9 March 2015

Fouad Allouani, Djamel Boukhetala, Fares Boudjema and Gao Xiao-Zhi

The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which…

Abstract

Purpose

The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which is a stochastic optimization algorithm recently developed, with the ant colony optimization (ACO) algorithm. Second, design of a new indirect adaptive recurrent fuzzy-neural controller (IARFNNC) for uncertain nonlinear systems using the developed optimization method (GHSACO) and the concept of the supervisory controller.

Design/methodology/approach

The novel optimization method introduces a novel improvization process, which is different from that of the GHS in the following aspects: a modified harmony memory representation and conception. The use of a global random switching mechanism to monitor the choice between the ACO and GHS. An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the IARFNNC global structure.

Findings

First, to analyze the performance of GHSACO method and shows its effectiveness, some benchmark functions with different dimensions are used. Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search (HS), GHS, improved HS (IHS) and conventional ACO algorithm. In addition, simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS, its variants, particle swarm optimization, and genetic algorithms applied to the same problem.

Originality/value

The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS. The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 20 June 2016

Lei Wang, Yongde Zhang, Shuanghui Hao, Baoyu Song, Minghui Hao and Zili Tang

To eliminate the angle deviation of magnetic encoder, this paper aims to propose a compensation method based on permanent magnet synchronous motor (PMSM) sensorless…

Abstract

Purpose

To eliminate the angle deviation of magnetic encoder, this paper aims to propose a compensation method based on permanent magnet synchronous motor (PMSM) sensorless control. The paper also describes the experiments performed to verify the validity of this proposed method.

Design/methodology/approach

The proposed method uses PMSM sensorless control method to get high precision virtual angle value, and then get the deviation value between virtual position and magnetic angle which is used as compensation table. Oversampling linear interpolation tabulation method has been proposed to eliminate the noise signals. Finally, a magnetic encoder with precision (repeatability) 0.09° and unidirectional motion precision 0.03 is realized. The control system with an encoder running at 14,000 and 0.01 r/min showing high motion resolution is also realized.

Findings

Higher value of current in PMSM leads to a magnetic encoder with higher precision. When using oversampling linear interpolation to tabulate the compensation table, it is understood that more oversampling does not lead to a better result. Finally, validated by experiments, using eight intervals to calculate the mean value of angle deviation leads to the best result.

Practical implications

The angle deviation compensation method proposed in this paper has a great practical implication and a good commercial application. The method proposed in this paper could be effectively used to self-correct the magnetic encoder using arctangent method and also correct any rotary encoder sensor.

Originality/value

This paper originally proposes an adaptive correction method for a rotary encoder based on PMSM sensorless control. To eliminate the noise signals in an angle compensation table, over-sampling linear interpolation tabulation method has been proposed which also guarantees the precision of the compensation table.

Details

Sensor Review, vol. 36 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 1 February 2016

Naiming Xie, Chuanzhen Hu and Songming Yin

The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model…

Abstract

Purpose

The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc.

Design/methodology/approach

Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree.

Findings

The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved.

Practical implications

The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model.

Originality/value

In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.

Details

Grey Systems: Theory and Application, vol. 6 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

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Article
Publication date: 22 November 2017

Obrad Anicic, Srdjan Jovic, Srdan Tasic, Aleksa Vulovic and Milivoje Jovanovic

This study aims to detect the temperature distribution in the cutting zone during the machining process. Furthermore, temperature influence in the cutting zone on the…

Abstract

Purpose

This study aims to detect the temperature distribution in the cutting zone during the machining process. Furthermore, temperature influence in the cutting zone on the forms of chip shapes during the turning of Steel 30CrNiMo8 was evaluated. It is very important to use optimal machining parameters to get the best production results or for high control of the machining process.

Design/methodology/approach

Temperature distribution in the cutting zone during the machining process could affect the forms of chip shapes. Forms of chip shapes could be considered as the most important indicator for the quality of the machining process.

Findings

Therefore, in this study, the forms of chip shapes based on the temperature distribution in the cutting zone were examined.

Originality/value

It was found that the snarled chip type and the loose chip type have the highest temperature variation during the machining process.

Details

Sensor Review, vol. 38 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

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