The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform

Sheng Chen - One of the best experts on this subject based on the ideXlab platform.

  • Wiener System iDentification using B-spline functions with De Boor recursion
    2015
    Co-Authors: Xia Hong, R J Mitchell, Sheng Chen
    Abstract:

    Abstract—A simple and effective Algorithm is introduced for the system iDentification of Wiener system based on the observational input/output data. The B-spline neural network is used to approximate the nonlinear static function in the Wiener system. We incorporate the Gauss-Newton Algorithm with De Boor Algorithm (both curve and the first orDer Derivatives) for the parameter estimation of the Wiener moDel, together with the use of a parameter initialization scheme. The efficacy of the proposed approach is Demonstrated using an illustrative example. I

  • MoDelling and Inverting Complex-Valued Wiener Systems
    2014
    Co-Authors: Xia Hong, Sheng Chen, Chris J. Harris
    Abstract:

    Abstract — We Develop a complex-valued (CV) B-spline neural network approach for efficient iDentification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton Algorithm effectively estimates the moDel parameters that incluDe the CV linear dynamic moDel coefficients and B-spline neural network weights. The iDentification Algorithm naturally incorporates the efficient De Boor Algorithm with both the B-spline curve and first orDer Derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton Algorithm based on the estimated B-spline neural network moDel, with the aid of the De Boor recursions. The effectiveness of our approach for iDentification and inversion of CV Wiener systems is Demonstrated using the application of digital predistorter Design for high power amplifiers with memory. I

  • Complex-valued B-spline neural networks for moDeling and inverting Hammerstein systems
    IEEE Transactions on Neural Networks and Learning Systems, 2014
    Co-Authors: Sheng Chen, Xia Hong, Junbin Gao, Chris J. Harris
    Abstract:

    Many communication signal processing applications involve moDeling and inverting complex-valued (CV) Hammerstein systems. We Develop a new CV B-spline neural network approach for efficient iDentification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein moDel. In particular, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for iDentifying the CV linear dynamic moDel’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic moDel’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated moDel. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian Newton Algorithm, which naturally incorporates the efficient De Boor Algorithm with both the B-spline curve and first-orDer Derivative recursions. The effectiveness of our approach is Demonstrated using the application to equalization of Hammerstein channels.

  • Digital Predistorter Design Using B-Spline Neural Network and Inverse of De Boor Algorithm
    2013
    Co-Authors: Sheng Chen, Xia Hong, Senior Member, Yu Gong, Chris J. Harris
    Abstract:

    Abstract—This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed Design is based on direct learning using a data-driven B-spline Wiener system moDeling approach. The nonlinear HPA with memory is first iDentifiedbasedonthe B-spline neural network moDel using the Gauss-Newton Algorithm, which incorporates the efficient De Boor Algorithm with both B-spline curve and first Derivative recursions. The estimated Wiener HPA moDel is then used to Design the Hammerstein predistorter. In particular, the inverse of the amplituDe distortion of the HPA’s static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor Algorithm. A major advantage of this approach is that both the Wiener HPA iDentification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to Demonstrate the effectiveness of this novel digital predistorter Design. InDex Terms—B-spline neural network, De Boor Algorithm, Hammerstein moDel, memory high power amplifier, output saturation

  • Complex-Valued Neural Networks: Advances and Applications - Complex-Valued B-Spline Neural Networks for MoDeling and Inverse of Wiener Systems
    Complex-Valued Neural Networks, 2013
    Co-Authors: Xia Hong, Sheng Chen, Chris J. Harris
    Abstract:

    Many communication signal processing applications manifest as the problem of moDeling and inverse of complex-valued (CV) Wiener systems. This contribution Develops a CV B-spline neural network approach for efficient iDentification of the CV Wiener system as well as effective inverse of the estimated CV Wiener moDel. Specifically, the CV nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks. Following the use of a simple least squares parameter initialization, the Gauss-Newton Algorithm is applied for estimating the moDel parameters that incluDe the CV linear dynamic moDel coefficients and B-spline neural network weights. The iDentification Algorithm naturally incorporates the efficient De Boor Algorithm with both the B-spline curve and first-orDer Derivative recursions. Moreover, an accurate inverse of the CV Wiener system can readily be obtained using the estimated moDel. In particular, the inverse of the CV nonlinear static function in the Wiener system can be calculated effectively using the Gauss-Newton Algorithm based on the estimated B-spline neural network moDel with the aid of the inverse of De Boor Algorithm. The effectiveness of our approach is Demonstrated using the application of digital predistorter Design for high-power amplifiers with memory.

Xia Hong - One of the best experts on this subject based on the ideXlab platform.

  • Wiener System iDentification using B-spline functions with De Boor recursion
    2015
    Co-Authors: Xia Hong, R J Mitchell, Sheng Chen
    Abstract:

    Abstract—A simple and effective Algorithm is introduced for the system iDentification of Wiener system based on the observational input/output data. The B-spline neural network is used to approximate the nonlinear static function in the Wiener system. We incorporate the Gauss-Newton Algorithm with De Boor Algorithm (both curve and the first orDer Derivatives) for the parameter estimation of the Wiener moDel, together with the use of a parameter initialization scheme. The efficacy of the proposed approach is Demonstrated using an illustrative example. I

  • MoDelling and Inverting Complex-Valued Wiener Systems
    2014
    Co-Authors: Xia Hong, Sheng Chen, Chris J. Harris
    Abstract:

    Abstract — We Develop a complex-valued (CV) B-spline neural network approach for efficient iDentification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton Algorithm effectively estimates the moDel parameters that incluDe the CV linear dynamic moDel coefficients and B-spline neural network weights. The iDentification Algorithm naturally incorporates the efficient De Boor Algorithm with both the B-spline curve and first orDer Derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton Algorithm based on the estimated B-spline neural network moDel, with the aid of the De Boor recursions. The effectiveness of our approach for iDentification and inversion of CV Wiener systems is Demonstrated using the application of digital predistorter Design for high power amplifiers with memory. I

  • Complex-valued B-spline neural networks for moDeling and inverting Hammerstein systems
    IEEE Transactions on Neural Networks and Learning Systems, 2014
    Co-Authors: Sheng Chen, Xia Hong, Junbin Gao, Chris J. Harris
    Abstract:

    Many communication signal processing applications involve moDeling and inverting complex-valued (CV) Hammerstein systems. We Develop a new CV B-spline neural network approach for efficient iDentification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein moDel. In particular, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for iDentifying the CV linear dynamic moDel’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic moDel’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated moDel. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian Newton Algorithm, which naturally incorporates the efficient De Boor Algorithm with both the B-spline curve and first-orDer Derivative recursions. The effectiveness of our approach is Demonstrated using the application to equalization of Hammerstein channels.

  • Digital Predistorter Design Using B-Spline Neural Network and Inverse of De Boor Algorithm
    2013
    Co-Authors: Sheng Chen, Xia Hong, Senior Member, Yu Gong, Chris J. Harris
    Abstract:

    Abstract—This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed Design is based on direct learning using a data-driven B-spline Wiener system moDeling approach. The nonlinear HPA with memory is first iDentifiedbasedonthe B-spline neural network moDel using the Gauss-Newton Algorithm, which incorporates the efficient De Boor Algorithm with both B-spline curve and first Derivative recursions. The estimated Wiener HPA moDel is then used to Design the Hammerstein predistorter. In particular, the inverse of the amplituDe distortion of the HPA’s static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor Algorithm. A major advantage of this approach is that both the Wiener HPA iDentification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to Demonstrate the effectiveness of this novel digital predistorter Design. InDex Terms—B-spline neural network, De Boor Algorithm, Hammerstein moDel, memory high power amplifier, output saturation

  • Complex-Valued Neural Networks: Advances and Applications - Complex-Valued B-Spline Neural Networks for MoDeling and Inverse of Wiener Systems
    Complex-Valued Neural Networks, 2013
    Co-Authors: Xia Hong, Sheng Chen, Chris J. Harris
    Abstract:

    Many communication signal processing applications manifest as the problem of moDeling and inverse of complex-valued (CV) Wiener systems. This contribution Develops a CV B-spline neural network approach for efficient iDentification of the CV Wiener system as well as effective inverse of the estimated CV Wiener moDel. Specifically, the CV nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks. Following the use of a simple least squares parameter initialization, the Gauss-Newton Algorithm is applied for estimating the moDel parameters that incluDe the CV linear dynamic moDel coefficients and B-spline neural network weights. The iDentification Algorithm naturally incorporates the efficient De Boor Algorithm with both the B-spline curve and first-orDer Derivative recursions. Moreover, an accurate inverse of the CV Wiener system can readily be obtained using the estimated moDel. In particular, the inverse of the CV nonlinear static function in the Wiener system can be calculated effectively using the Gauss-Newton Algorithm based on the estimated B-spline neural network moDel with the aid of the inverse of De Boor Algorithm. The effectiveness of our approach is Demonstrated using the application of digital predistorter Design for high-power amplifiers with memory.

Chris J. Harris - One of the best experts on this subject based on the ideXlab platform.

  • MoDelling and Inverting Complex-Valued Wiener Systems
    2014
    Co-Authors: Xia Hong, Sheng Chen, Chris J. Harris
    Abstract:

    Abstract — We Develop a complex-valued (CV) B-spline neural network approach for efficient iDentification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton Algorithm effectively estimates the moDel parameters that incluDe the CV linear dynamic moDel coefficients and B-spline neural network weights. The iDentification Algorithm naturally incorporates the efficient De Boor Algorithm with both the B-spline curve and first orDer Derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton Algorithm based on the estimated B-spline neural network moDel, with the aid of the De Boor recursions. The effectiveness of our approach for iDentification and inversion of CV Wiener systems is Demonstrated using the application of digital predistorter Design for high power amplifiers with memory. I

  • Complex-valued B-spline neural networks for moDeling and inverting Hammerstein systems
    IEEE Transactions on Neural Networks and Learning Systems, 2014
    Co-Authors: Sheng Chen, Xia Hong, Junbin Gao, Chris J. Harris
    Abstract:

    Many communication signal processing applications involve moDeling and inverting complex-valued (CV) Hammerstein systems. We Develop a new CV B-spline neural network approach for efficient iDentification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein moDel. In particular, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for iDentifying the CV linear dynamic moDel’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic moDel’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated moDel. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian Newton Algorithm, which naturally incorporates the efficient De Boor Algorithm with both the B-spline curve and first-orDer Derivative recursions. The effectiveness of our approach is Demonstrated using the application to equalization of Hammerstein channels.

  • Digital Predistorter Design Using B-Spline Neural Network and Inverse of De Boor Algorithm
    2013
    Co-Authors: Sheng Chen, Xia Hong, Senior Member, Yu Gong, Chris J. Harris
    Abstract:

    Abstract—This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed Design is based on direct learning using a data-driven B-spline Wiener system moDeling approach. The nonlinear HPA with memory is first iDentifiedbasedonthe B-spline neural network moDel using the Gauss-Newton Algorithm, which incorporates the efficient De Boor Algorithm with both B-spline curve and first Derivative recursions. The estimated Wiener HPA moDel is then used to Design the Hammerstein predistorter. In particular, the inverse of the amplituDe distortion of the HPA’s static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor Algorithm. A major advantage of this approach is that both the Wiener HPA iDentification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to Demonstrate the effectiveness of this novel digital predistorter Design. InDex Terms—B-spline neural network, De Boor Algorithm, Hammerstein moDel, memory high power amplifier, output saturation

  • Complex-Valued Neural Networks: Advances and Applications - Complex-Valued B-Spline Neural Networks for MoDeling and Inverse of Wiener Systems
    Complex-Valued Neural Networks, 2013
    Co-Authors: Xia Hong, Sheng Chen, Chris J. Harris
    Abstract:

    Many communication signal processing applications manifest as the problem of moDeling and inverse of complex-valued (CV) Wiener systems. This contribution Develops a CV B-spline neural network approach for efficient iDentification of the CV Wiener system as well as effective inverse of the estimated CV Wiener moDel. Specifically, the CV nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks. Following the use of a simple least squares parameter initialization, the Gauss-Newton Algorithm is applied for estimating the moDel parameters that incluDe the CV linear dynamic moDel coefficients and B-spline neural network weights. The iDentification Algorithm naturally incorporates the efficient De Boor Algorithm with both the B-spline curve and first-orDer Derivative recursions. Moreover, an accurate inverse of the CV Wiener system can readily be obtained using the estimated moDel. In particular, the inverse of the CV nonlinear static function in the Wiener system can be calculated effectively using the Gauss-Newton Algorithm based on the estimated B-spline neural network moDel with the aid of the inverse of De Boor Algorithm. The effectiveness of our approach is Demonstrated using the application of digital predistorter Design for high-power amplifiers with memory.

  • Digital Predistorter Design Using B-Spline Neural Network and Inverse of De Boor Algorithm
    IEEE Transactions on Circuits and Systems I: Regular Papers, 2013
    Co-Authors: Sheng Chen, Xia Hong, Yu Gong, Chris J. Harris
    Abstract:

    This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed Design is based on direct learning using a data-driven B-spline Wiener system moDeling approach. The nonlinear HPA with memory is first iDentified based on the B-spline neural network moDel using the Gauss-Newton Algorithm, which incorporates the efficient De Boor Algorithm with both B-spline curve and first Derivative recursions. The estimated Wiener HPA moDel is then used to Design the Hammerstein predistorter. In particular, the inverse of the amplituDe distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor Algorithm. A major advantage of this approach is that both the Wiener HPA iDentification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to Demonstrate the effectiveness of this novel digital predistorter Design.

Yu Gong - One of the best experts on this subject based on the ideXlab platform.

  • Digital Predistorter Design Using B-Spline Neural Network and Inverse of De Boor Algorithm
    2013
    Co-Authors: Sheng Chen, Xia Hong, Senior Member, Yu Gong, Chris J. Harris
    Abstract:

    Abstract—This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed Design is based on direct learning using a data-driven B-spline Wiener system moDeling approach. The nonlinear HPA with memory is first iDentifiedbasedonthe B-spline neural network moDel using the Gauss-Newton Algorithm, which incorporates the efficient De Boor Algorithm with both B-spline curve and first Derivative recursions. The estimated Wiener HPA moDel is then used to Design the Hammerstein predistorter. In particular, the inverse of the amplituDe distortion of the HPA’s static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor Algorithm. A major advantage of this approach is that both the Wiener HPA iDentification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to Demonstrate the effectiveness of this novel digital predistorter Design. InDex Terms—B-spline neural network, De Boor Algorithm, Hammerstein moDel, memory high power amplifier, output saturation

  • Digital Predistorter Design Using B-Spline Neural Network and Inverse of De Boor Algorithm
    IEEE Transactions on Circuits and Systems I: Regular Papers, 2013
    Co-Authors: Sheng Chen, Xia Hong, Yu Gong, Chris J. Harris
    Abstract:

    This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed Design is based on direct learning using a data-driven B-spline Wiener system moDeling approach. The nonlinear HPA with memory is first iDentified based on the B-spline neural network moDel using the Gauss-Newton Algorithm, which incorporates the efficient De Boor Algorithm with both B-spline curve and first Derivative recursions. The estimated Wiener HPA moDel is then used to Design the Hammerstein predistorter. In particular, the inverse of the amplituDe distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor Algorithm. A major advantage of this approach is that both the Wiener HPA iDentification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to Demonstrate the effectiveness of this novel digital predistorter Design.

  • b spline neural network based digital baseband predistorter solution using the inverse of De Boor Algorithm
    International Joint Conference on Neural Network, 2011
    Co-Authors: Xia Hong, Yu Gong, Sheng Chen
    Abstract:

    In this paper a new nonlinear digital baseband predistorter Design is introduced based on direct learning, together with a new Wiener system moDeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly DepenDent on the input signal amplituDe the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplituDe distortion and another for the phase shift. The Gauss-Newton Algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first orDer Derivatives. Secondly, we Derive the predistorter Algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener moDels. The inverse of the amplituDe and phase shift distortion are then computed and compensated using the iDentified phase shift moDel. Numerical examples have been employed to Demonstrate the efficacy of the proposed approaches.

  • A Wiener moDel for memory high power amplifiers using B-spline function approximation
    2011 17th International Conference on Digital Signal Processing (DSP), 2011
    Co-Authors: Xia Hong, Yu Gong, Sheng Chen
    Abstract:

    In this paper we introduce a new Wiener system moDeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener moDel is mainly DepenDent on the input signal amplituDe, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplituDe distortion and another for the phase shift, respectively. The Gauss-Newton Algorithm is applied for the parameter estimation, which incorporates the De Boor Algorithm, including both the B-spline curve and the first orDer Derivatives recursion. An illustrative example is utilized to Demonstrate the efficacy of the proposed approach.

  • IJCNN - B-spline neural network based digital baseband predistorter solution using the inverse of De Boor Algorithm
    The 2011 International Joint Conference on Neural Networks, 2011
    Co-Authors: Xia Hong, Yu Gong, Sheng Chen
    Abstract:

    In this paper a new nonlinear digital baseband predistorter Design is introduced based on direct learning, together with a new Wiener system moDeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly DepenDent on the input signal amplituDe the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplituDe distortion and another for the phase shift. The Gauss-Newton Algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first orDer Derivatives. Secondly, we Derive the predistorter Algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener moDels. The inverse of the amplituDe and phase shift distortion are then computed and compensated using the iDentified phase shift moDel. Numerical examples have been employed to Demonstrate the efficacy of the proposed approaches.

Hong-tzong Yau - One of the best experts on this subject based on the ideXlab platform.

  • Universal real-time NURBS interpolator on a PC-based controller
    The International Journal of Advanced Manufacturing Technology, 2014
    Co-Authors: Jun-bin Wang, Hong-tzong Yau
    Abstract:

    It is complex and divergent for the conventional motion controllers to process various G coDes using different interpolation Algorithms. This impairs programming efficiency and robustness of the controller. In this paper, we propose the universal non-uniform rational B-splines (NURBS)-based interpolator which can simplify the architecture of interpolation in spite of interpreting different kinds of inputs. Direct conversion of long G01 and G02/G03 numerical control (NC) segments to NURBS segment is first implemented. The fitting of multiple short segments into a continuous and smooth NURBS segment is then carried out. More importantly, the universal NURBS-based interpolator utilizes the Cox–De Boor Algorithm which is highly efficient and can take advantage of the parallel computing scheme to accelerate the processing speed. Furthermore, due to the construction of real-time environment, the proposed Algorithm enables interpretation, look-ahead functions, and motion control to work simultaneously. A 2D NC program possessing hundreds of short G01 segments and long segments (i.e., G02, G03, and long G01) is tested on an in-house Developed XY-table with a PC-based motion controller. The results have shown the effectiveness and feasibility of the proposed real-time NURBS-based interpolator.

  • Real-time NURBS interpolation using FPGA for high speed motion control
    Computer-Aided Design, 2006
    Co-Authors: Hong-tzong Yau, Ming-tzong Lin, Meng-shiun Tsai
    Abstract:

    MoDern motion control adopts NURBS (Non-Uniform Rational B-Spline) interpolation for the purpose of achieving high-speed and high-accuracy performance. However, in conventional control architectures, the computation of the basis functions of a NURBS curve is very time-consuming due to serial computing constraints. In this paper, a novel FPGA (Field Programmable Gate Array) based motion controller utilizing its high-speed parallel computing power is proposed to realize the Cox-De Boor Algorithm for second and higher Degrees NURBS interpolation. The motion control Algorithm is also embedDed in the FPGA chip to implement real-time control and NURBS interpolation simultaneously for multi-axis servo systems. The proposed FPGA-based motion controller is capable of performing the Cox-De Boor Algorithm and the IIR (Infinite Impulse Response) control Algorithm in about 46 clock cycles, as compared to the 1303 clock cycles by the traditional approach. Numerical simulations and experimental tests using an X-Y table verify the outstanding computation performance of the FPGA-based motion controller. The result indicates that shorter sampling time (10 @ms) can be achieved for NURBS interpolation which is highly critical to the success of high-speed and high-accuracy motion control.