Load torque estimation problem for electric machinery servo systems by an inverse method

Abstract This study analyzes a load torque estimation problem for an electric machinery servo system using the recursive input estimation (RIE) algorithm. This study also presents four novel estimation models, which have different numbers of sensors, considers the process and the measurement noise to estimate load torque without a torque sensor. The proposed algorithm is a novel estimation technique for solving force feedback and state estimation problems for an electric machinery servo control system. In this work, the DC servomotor system is utilized as an electric machinery servo system, and four important RIE characteristics were verified by numerical simulation results: (i) Adaptive forgetting factor in the RIE algorithm can estimate load torque more effectively than constant forgetting factor; (ii) The significant variation of control input and/or load torque impact affects the estimation precision; (iii) The high‐performance load torque estimation model can be established based only on the given control input and techogenerator sensor; (iv) Using different degrees of model errors, the estimation of the performance tendency toward good or bad for control input and load torque are the same. This characteristic shows that good estimated performance for an unknown load torque can be confirmed based on the strength of good control input estimated performance. In other words, model error can be identified via the deterministic control input and its estimation results. The proposed models may have practical applications for solving disturbance compensation problems in electric machinery servo systems.

[1]  T. Du,et al.  Design and application of extended observers for joint state and parameter estimation in high-performance AC drives , 1995 .

[2]  Ronnie Belmans,et al.  Improved control of induction motor drives without a shaft sensor , 2001 .

[3]  V. Mikhailov.  Question of the convergence of iteration methods of solving the inverse heat-conduction problem , 1983 .

[4]  F. Kratz,et al.  Disturbance rejection using output and input estimation. Application to the friction compensation of a DC motor , 1997 .

[5]  O. M. Alifanov,et al.  Solution of an inverse problem of heat conduction by iteration methods , 1974 .

[6]  Sou-Chen Lee,et al.  An efficient on-line thermal input estimation method using kalman filter and recursive least square algorithm , 1997 .

[7]  Carl J. Kempf,et al.  Disturbance observer and feedforward design for a high-speed direct-drive positioning table , 1999, IEEE Trans. Control. Syst. Technol..

[8]  Takamasa Hori,et al.  Control of redundant manipulators considering order of disturbance observer , 2000, IEEE Trans. Ind. Electron..

[9]  J. Chang.  The Lyapunov-Based Estimator of Rotor Resistance for an ac Induction Motor , 2003, 2003 4th International Conference on Control and Automation Proceedings.

[10]  Bhaskar Dasgupta,et al.  The Stewart platform manipulator: a review , 2000 .

[11]  Nobuyuki Matsui,et al.  Disturbance observer-based nonlinear friction compensation in table drive system , 1998, AMC 1998.

[12]  James V. Beck,et al.  Analysis of Order of the Sequential Regularization Solutions of Inverse Heat Conduction Problems , 1989 .

[13]  Tsung-Chien Chen,et al.  Adaptive-Weighting Input-Estimation Approach to Nonlinear Inverse Heat-Conduction Problems , 2005 .

[14]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[15]  L. Guvenc,et al.  Robustness of disturbance observers in the presence of structured real parametric uncertainty , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[16]  Masayoshi Tomizuka,et al.  A sensitivity optimization approach to design of a disturbance observer in digital motion control systems , 2000 .

[17]  K. Miller.  Least Squares Methods for Ill-Posed Problems with a Prescribed Bound , 1970 .

[18]  Maurizio Cirrincione,et al.  Sensorless direct torque control of an induction motor by a TLS-based MRAS observer with adaptive integration , 2005, Autom..

[19]  O. M. Alifanov,et al.  Regularized numerical solution of nonlinear inverse heat-conduction problem , 1975 .

[20]  Wen-Hua Chen,et al.  Disturbance observer based control for nonlinear systems , 2004, IEEE/ASME Transactions on Mechatronics.

[21]  Sou-Chen Lee,et al.  Improved Trajectory Estimation of Reentry Vehicles from Radar Measurements Using On-Line Adaptive Input Estimator (Special Section on Nonlinear Theory and Its Applications) , 1998 .

[22]  Ichiro Hagiwara,et al.  Optimal Estimation of Dynamic Loads for Multiple-Input System , 1999 .

[23]  Pan-Chio Tuan,et al.  ADAPTIVE ROBUST WEIGHTING INPUT ESTIMATION METHOD FOR THE 1-D INVERSE HEAT CONDUCTION PROBLEM , 1998 .

[24]  O. M. Alifanov,et al.  Solution of the nonlinear inverse thermal conductivity problem by the iteration method , 1978 .

[25]  Dong-Cherng Lin,et al.  INPUT FORCES ESTIMATION OF BEAM STRUCTURES BY AN INVERSE METHOD , 2003 .

[26]  K. Fujiyama,et al.  Digital controller design for recordable optical disk player using disturbance observer , 2000, 6th International Workshop on Advanced Motion Control. Proceedings (Cat. No.00TH8494).

[27]  Ahmed Rubaai,et al.  Adaptation learning control scheme for a high performance permanent magnet stepper motor using online random training of neural networks , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).