Krzysztof Szabat

Vibration Suppression in a Two-Mass Drive System Using PI Speed Controller and Additional Feedbacks—Comparative Study

In this paper, an analysis of control structures for the electrical drive system with elastic joint is carried out. The synthesis of the control structure with proportional-integral controller supported by different additional feedbacks is presented. The classical pole-placement method is applied. Analytical equations, which allow for calculating the control structure parameters, are given. The limitation of the design due to the number of degrees of freedom of the considered drive systems is shown. In order to damp the torsional vibration effectively, the application of the feedback from one selected state variable is necessary. In the literature, a large number of possible feedbacks have been reported. However, in this paper, it is shown that all systems with one additional feedback can be divided into three different groups, according to their dynamical characteristics. In addition, the system with two additional feedbacks is investigated. The comparison between considered structures is carried out. The simulation results are confirmed experimentally in the laboratory setup

Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive Without Mechanical Sensors

In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive speed controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy controller is used as the speed controller, whose connective weights are trained online according to the error between the estimated motor speed and the speed given by the reference model. The speed of the vector-controlled IM is estimated using the MRASCC rotor speed and a flux estimator. Such a control structure is proposed to damp torsional vibrations in a two-mass system in an effective way. It is shown that torsional oscillations can be successfully suppressed in the proposed control structure, using only one basic feedback from the motor speed given by the proposed speed estimator. Simulation results are verified by experimental tests over a wide range of motor speed and drive parameter changes.

Performance Improvement of Industrial Drives With Mechanical Elasticity Using Nonlinear Adaptive Kalman Filter

This paper deals with the application of the adaptive control structure for torsional vibration suppression in the drive system with an elastic coupling. The proportional-integral speed controller and gain factors of two additional feedback loops, from the shaft torque and load side speed, are tuned on-line according to the changeable load side inertia. This parameter, as well as other mechanical variables of the drive system (load side speed, torsional and load torques), are estimated with the use of the developed nonlinear extended Kalman filter (NEKF). The initial values of the Kalman filter covariance matrices are set using the genetic algorithm. Then, to ensure the smallest state and parameter estimation errors, the on-line adaptation law for the chosen element of the state covariance matrix of the NEKF is proposed. The described control strategy is tested in an open and a closed-loop control structure. The simulation results are confirmed by laboratory experiments.

Control of the Drive System With Stiff and Elastic Couplings Using Adaptive Neuro-Fuzzy Approach

In the paper a robust control system with the fuzzy-neural network is proposed. A model reference adaptive control system is applied to the one- and two-mass systems. Different aspects of application of the examined control structure are discussed. The influence of the number of neuro-fuzzy controller (NFC) rules to the drive system performance is shown. The impact of the electromagnetic torque limit to the adaptive structure stability is discussed. Further, the comparison of the dynamical characteristics of the different NFC structures is done. The control structure with constant and changeable parameters of the adaptive rule is also examined. The torsional vibration suppression in the two-mass system is obtained in the developed adaptive structure with only one basic feedback from the motor speed

Constrained Model Predictive Control of the Drive System With Mechanical Elasticity

In this paper, the application of model predictive control (MPC) for high-performance speed control and torsional vibration suppression in the drive system with flexible coupling is demonstrated. The control methodology presented in this paper relies on incorporating the drives safety and physical limitations directly into the control problem formulation so that future constraint violations are anticipated and prevented. In order to reduce the computational complexity, the standard MPC controller is replaced by its explicit form. The resulting explicit controller achieves the same level of performance as the conventional MPC, but requires only a fraction of the real-time computational machinery, thus leading to fast and reliable implementation. The simulation results are confirmed by laboratory experiments.

Neural-Network Application for Mechanical Variables Estimation of a Two-Mass Drive System

This paper deals with the application of neural networks (NNs) to the mechanical state estimation of the drive system with elastic joint. The torsional vibrations of the two-mass system are damped using the control structure with additional feedbacks from the torsional torque and the load-side speed. These feedbacks signals are obtained using NN estimators. The learning procedure of the NNs is described, and the influence of the input vector size to the accuracy of the state-variable estimation is investigated. The neural estimators of the torsional torque and the load machine speed are tested with open-loop and closed-loop control structures. The simulation results are confirmed by laboratory experiments

Implementation of a Sliding-Mode Controller With an Integral Function and Fuzzy Gain Value for the Electrical Drive With an Elastic Joint

This paper presents a modified sliding-mode structure implemented for the speed control of a two-mass drive. A characteristic feature of the presented control method is the higher rank of the switching function caused by the application of an integral element (sliding mode with an integral function control). The proposed control system is a combination of a sliding-mode controller and a linear controller. Furthermore, to eliminate the chattering phenomenon related to the sliding-mode control, a switching function with a variable slope based on the fuzzy system is implemented. This solution ensures the robustness and dynamics of a two-mass drive better than with a linear speed controller. The main stages of the design methodology of the presented speed control structure are described in the initial sections of this paper. In the subsequent sections, simulation and experimental tests for the proposed control structure are presented and discussed.

Damping of Torsional Vibrations in Two-Mass System Using Adaptive Sliding Neuro-Fuzzy Approach

In this paper, a new robust control system with the adaptive sliding neuro-fuzzy speed controller for the drive system with the flexible joint is proposed. A model reference adaptive control structure (MRAC) is used in this drive system. The torsional vibrations are successfully suppressed in the control structure with only one basic feedback from the motor speed. The damping ability of the proposed system has been confirmed for a wide range of the system parameters and compared with the other control concepts, like the adaptive Pi-type neuro-fuzzy controller and the classical cascade PI structure.

Application of the Kalman Filters to the High-Performance Drive System With Elastic Coupling

In this paper, issues related to parameter identification and high-performance control of the drive system with an elastic joint are discussed. After a brief introduction, the mathematical model of the drive and the proposed high-performance control structure are presented. The effect of the location of closed-loop poles and cancellation of zeros of the structure is examined. Then, the mathematical models of the Kalman filters used in this study are presented, and the results of the identification procedure are described. The effectiveness of the proposed structure has been examined under simulation and experimental study.

Optimization of fuzzy-logic speed controller for DC drive system with elastic joints

This paper deals with the analysis of a DC drive system with elastic joints and different speed controllers. The control structure with one and two speed feedbacks was analyzed. The dynamics of the drive system with classical proportional-integral (PI) and fuzzy-logic (FL) speed controllers was compared. Parameters of the classical PI and FL speed controllers were optimized using the same control indexes. Controllers were parameterised using the hybrid genetic-gradient algorithm. The simulation results for different parameters and operation modes of the drive system were demonstrated and compared.