Application of fuzzy type II multi-layer Kalman filter for parameters identification of two-mass drive system

The paper describes a novel online identification algorithm for a two-mass drive system. The multi-layer extended Kalman Filter (MKF) is proposed in the paper. The proposed estimator has two layers. In the first one, three single extended Kalman filters (EKF) are placed. In the second layer, based on the incoming signals from the first layer, the final states and parameters of the two-mass system are calculated. In the considered drive system, the stiffness coefficient of the elastic shaft and the time constant of the load machine is estimated. To improve the quality of estimated states, an additional system based on II types of fuzzy sets is proposed. The application of fuzzy MKF allows for a shorter identification time, as well as improves the accuracy of estimated parameters. The identified parameters of the two-mass system are used to calculate the coefficients of the implemented control structure. Theoretical considerations are supported by simulations and experimental tests

Application of Multilayer Observer for a Drive System with Flexibility

This paper proposes a new estimation algorithm based on the Luenberger observer methodology and multilayer concept. The proposed multi-layer Luenberger observer (MLO) is implemented in the control structure designated for a two-mass system. Two types of aggregation mechanism are evaluated in the paper. The MLO ensures better estimation quality of the mechanical state variables: motor speed, shaft torque, load speed and load torque, as compared to the classical single observer. The more accurate estimated states, the more precise closed-loop control is guaranteed. MLO is designated for the system where initial conditions of the plant are not known or the state variables can change rapidly (load torque in the considered case). The estimation algorithm and control strategy is evaluated through simulation and experimental tests. The obtained results confirm efficiency of the proposed MLO