Aim Interactive multiple model(IMM) algorithm was introduced into two? stage estimation to improve the estimating accuracy for system position and velocity.Methods The state estimation was carried out in mixed coor...Aim Interactive multiple model(IMM) algorithm was introduced into two? stage estimation to improve the estimating accuracy for system position and velocity.Methods The state estimation was carried out in mixed coordinates according to the nonlinear measure equation, a generalized interactive acceleration compensation(IAC) algorithm in mixed coordinate was presented. Results Simulation result shows the estimation accuracy is improved through changing measure equation in polar coordinates. Conclusion The estimation accuracy for position and velocity estimation, has been improved greatly, and the proposed algorithm has the advantage of less calculating time comparing with other multiple model methods.展开更多
Machined surface roughness will affect parts?service performance.Thus,predicting it in the machining is important to avoid rejects.Surface roughness will be affected by system position dependent vibration even under c...Machined surface roughness will affect parts?service performance.Thus,predicting it in the machining is important to avoid rejects.Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing.Aiming at surface roughness prediction in the machining process,this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis.To reduce the stochastic error of measuring the machined surface profile height,the surface area is repeatedly measured three times,and Pauta criterion is adopted to eliminate abnormal points.The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model.Seven acceleration features are extracted,and valley,which has the highest/^-square proving the effectiveness of the filtering features,is selected as the input of the prediction model by mutual information coefficients.Finally,by comparing the measured and predicted surface roughness curves,they have the same trends,with the average error of 16.28%and the minimum error of 0.16%.Moreover,the prediction curve matches and agrees well with the actual surface state,which verifies the accuracy and reliability of the model.展开更多
文摘Aim Interactive multiple model(IMM) algorithm was introduced into two? stage estimation to improve the estimating accuracy for system position and velocity.Methods The state estimation was carried out in mixed coordinates according to the nonlinear measure equation, a generalized interactive acceleration compensation(IAC) algorithm in mixed coordinate was presented. Results Simulation result shows the estimation accuracy is improved through changing measure equation in polar coordinates. Conclusion The estimation accuracy for position and velocity estimation, has been improved greatly, and the proposed algorithm has the advantage of less calculating time comparing with other multiple model methods.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.52022082 and 52005413)the 111 Project(Grant No.B13044).
文摘Machined surface roughness will affect parts?service performance.Thus,predicting it in the machining is important to avoid rejects.Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing.Aiming at surface roughness prediction in the machining process,this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis.To reduce the stochastic error of measuring the machined surface profile height,the surface area is repeatedly measured three times,and Pauta criterion is adopted to eliminate abnormal points.The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model.Seven acceleration features are extracted,and valley,which has the highest/^-square proving the effectiveness of the filtering features,is selected as the input of the prediction model by mutual information coefficients.Finally,by comparing the measured and predicted surface roughness curves,they have the same trends,with the average error of 16.28%and the minimum error of 0.16%.Moreover,the prediction curve matches and agrees well with the actual surface state,which verifies the accuracy and reliability of the model.
