The morbidity problem of the GM（1,1） power model in parameter identification is discussed by using multiple and rotation transformation of vectors. Firstly we consider the morbidity problem of the special matrix and...The morbidity problem of the GM（1,1） power model in parameter identification is discussed by using multiple and rotation transformation of vectors. Firstly we consider the morbidity problem of the special matrix and prove that the condition number of the coefficient matrix is determined by the ratio of lengths and the included angle of the column vector, which could be adjusted by multiple and rotation transformation to turn the matrix to a well-conditioned one. Then partition the corresponding matrix of the GM（1,1） power model in accordance with the column vector and regulate the matrix to a well-conditioned one by multiple and rotation transformation of vectors, which completely solve the instability problem of the GM（1,1） power model. Numerical results show that vector transformation is a new method in studying the stability problem of the GM（1,1） power model.展开更多
A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established...A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.展开更多
Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs ...Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.展开更多
This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework base...This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.展开更多
To avoid the machine problems of excessive axial force, complex process flow and frequent tool changing during robotic drilling holes, a new hole-making technology (i.e., helical milling hole) was introduced for desig...To avoid the machine problems of excessive axial force, complex process flow and frequent tool changing during robotic drilling holes, a new hole-making technology (i.e., helical milling hole) was introduced for designing a new robotic helical milling hole system, which could further improve robotic hole-making ability in airplane digital assembly. After analysis on the characteristics of helical milling hole, advantages and limitations of two typical robotic helical milling hole systems were summarized. Then, vector model of helical milling hole movement was built on vector analysis method. Finally, surface roughness calculation formula was deduced according to the movement principle of helical milling hole, then the influence of main technological parameters on surface roughness was analyzed. Analysis shows that theoretical surface roughness of hole becomes poor with the increase of tool speed ratio and revolution radius. Meanwhile, the roughness decreases according to the increase of tool teeth number. The research contributes greatly to the construction of roughness prediction model in helical milling hole.展开更多
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China(20120143110001)the General Education Program Requirements in the Humanities and Social Sciences of China(11YJC630155)the Youth Foundation of Hubei Province of China(Q20121203)
文摘The morbidity problem of the GM（1,1） power model in parameter identification is discussed by using multiple and rotation transformation of vectors. Firstly we consider the morbidity problem of the special matrix and prove that the condition number of the coefficient matrix is determined by the ratio of lengths and the included angle of the column vector, which could be adjusted by multiple and rotation transformation to turn the matrix to a well-conditioned one. Then partition the corresponding matrix of the GM（1,1） power model in accordance with the column vector and regulate the matrix to a well-conditioned one by multiple and rotation transformation of vectors, which completely solve the instability problem of the GM（1,1） power model. Numerical results show that vector transformation is a new method in studying the stability problem of the GM（1,1） power model.
文摘A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.
基金Project(2002CB312200) supported by the National Key Fundamental Research and Development Program of China project(60574019) supported by the National Natural Science Foundation of China
文摘Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.
文摘This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.
基金Foundation item： Projects（50975141, 51005118） supported by the National Natural Science Foundation of China Projects（20091652018, 2010352005） supported by Aviation Science Fund of China Project（YKJ11-001） supported by Key Program of Nanjing College of Information Technology Institute, China
文摘To avoid the machine problems of excessive axial force, complex process flow and frequent tool changing during robotic drilling holes, a new hole-making technology (i.e., helical milling hole) was introduced for designing a new robotic helical milling hole system, which could further improve robotic hole-making ability in airplane digital assembly. After analysis on the characteristics of helical milling hole, advantages and limitations of two typical robotic helical milling hole systems were summarized. Then, vector model of helical milling hole movement was built on vector analysis method. Finally, surface roughness calculation formula was deduced according to the movement principle of helical milling hole, then the influence of main technological parameters on surface roughness was analyzed. Analysis shows that theoretical surface roughness of hole becomes poor with the increase of tool speed ratio and revolution radius. Meanwhile, the roughness decreases according to the increase of tool teeth number. The research contributes greatly to the construction of roughness prediction model in helical milling hole.
