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.     

Decoupled stator-flux-oriented induction motor drive with fuzzyneural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results     

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     

Wavelet neural network control for induction motor drive using sliding-mode design technique

This paper addresses an adaptive observation system and a wavelet-neural-network (WNN) control system for achieving the favorable decoupling control and high-precision position tracking performance of an induction motor (IM) drive. First, an adaptive observation system with an inverse rotor time-constant observer is derived on the basis of model reference adaptive system theory to preserve the decoupling control characteristic of an indirect field-oriented IM drive. The adaptive observation system is implemented using a digital signal processor with a high sampling rate to make it possible to achieve good dynamics. Moreover, a WNN control system is developed via the principle of sliding-mode control to increase the robustness of the indirect field-oriented IM drive with the adaptive observation system for high-performance applications. In the WNN control system, a WNN is utilized to predict the uncertain system dynamics online to relax the requirement of uncertainty bound in the design of a traditional sliding-mode controller. In addition, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results.     

A discrete adaptive field-oriented induction motor drive

A discrete model reference adaptive controller (MRAC) is designed and implemented. This MRAC makes the performance of the field-oriented induction motor drives insensitive to parameter changes. Only the information of the reference model and the plant output are required. Hence, the proposed controller is easy to implement practically. For designing the proposed adaptive controller, the dynamic model of the drive system is estimated from the sampled input-output data using the stochastic modeling technique. The theoretical basis of the adaptive control is derived and simulation is made. The hardware of the drive system and the microprocessor-based adaptive controller are discussed. Some experimental results are given to demonstrate the effectiveness of the proposed controller     

Speed-sensorless vector control of an induction motor using neuralnetwork speed estimation

In this paper, a novel speed estimation method of an induction motor using neural networks (NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The estimated speed is then fed back in the speed control loop, and the speed-sensorless vector drive is realized. The proposed NN speed estimator has shown good performance in the transient and steady states, and also at either variable-speed operation or load variation. The validity and the usefulness of the proposed algorithm are thoroughly verified with experiments on fully digitalized 2.2 kW induction motor drive systems     

Adaptive positioning control for a LPMSM drive based on adapted inverse model and robust disturbance observer

The adaptive robust positioning control for a linear permanent magnet synchronous motor drive based on adapted inverse model and robust disturbance observer is studied in this paper. First, a model following two-degrees-of-freedom controller consisting of a command feedforward controller (FFC) and a feedback controller (FBC) is developed. According to the estimated motor drive dynamic model and the given position tracking response, the inner speed controller is first designed. Then, the transfer function of FFC is found based on the inverse model of inner speed closed-loop and the chosen reference model. The practically unrealizable problem possessed by traditional feedforward control is avoided by the proposed FFC. As to the FBC, it is quantitatively designed using reduced plant model to meet the specified load force regulation control specifications. In dealing with the robust control, a disturbance observer based robust control scheme and a parameter identifier are developed. The key parameters in the robust control scheme are designed considering the effect of system dead-time. The identification mechanism is devised to obtain the parameter uncertainties from the observed disturbance signal. Then by online adapting the parameters set in the FFC according to the identified parameters, the nonideal disturbance observer based robust control can be corrected to yield very close model following position tracking control. Meanwhile, the regulation control performance is also further improved by the robust control. In the proposed identification scheme, the effect of a nonideal differentiator in the accuracy of identification results is taken into account, and the compromise between performance, stability, and control effort limit is also considered in the whole proposed control scheme.     

An MRAC-based nonlinear speed control of an interior PM synchronous motor with improved maximum torque operation

A model reference adaptive control (MRAC)-based nonlinear speed control strategy of an interior permanent magnet (IPM) synchronous motor with an improved maximum torque operation is presented. In most servo systems, the controller is designed under the assumption that the electrical dynamics are neglected by the field-oriented control. This requires a high-performance inner-loop current control strategy. However, the separate designs for a high-performance current regulator and a robust speed controller need considerable effort. To overcome this limitation, an MRAC-based nonlinear speed control strategy for the IPM synchronous motor is presented, considering the whole nonlinear dynamics. Nonlinear speed control is achieved by an input–output linearization scheme. This scheme, however, gives an unsatisfactory performance under the mismatch of the system parameters and load conditions. For the robust output response, the controller parameters are estimated by an MRAC technique in which the disturbance torque and flux linkage are estimated. The adaptation laws are derived from Lyapunov stability theory. In view of the drive efficiency, the motor has to provide the maximum torque for a given input. To drive the IPM synchronous motor under improved maximum torque operation, the estimated flux linkage is employed for the generation of the d-axis current command. The robustness and output performance of the proposed control scheme are verified through simulation results.     

DSP-based self-tuning IP speed controller with load torquecompensation for rolling mill DC drive

This paper describes the design and the implementation of a self-tuning integral-proportional (IP) speed controller for a rolling mill DC motor drive system, based on a 32-bit floating point digital signal processor (DSP)-TMS 320C30. To get a better transient response than conventional proportional-integral (PI) and/or integral-proportional (IP) speed control in the presence of transient disturbance and/or parameter variations, an adaptive self-tuning IP speed control with load torque feedforward compensation was used. The model parameters, related to motor and load inertia and damping coefficient, were estimated online by using recursive extended least squares (RELS) estimation algorithm. On the basis of the estimated model parameters and a pole-placement design, a control signal was calculated. Digital simulation and experimental results showed that the proposed controller possesses excellent adaptation capability under parameter change and a better transient recovery characteristic than a conventional PI/IP controller under load change     

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.     

Integrated design of speed-sensorless and adaptive speed controller for a brushless DC motor

The study develops a design of an integrated new speed-sensorless approach that involves a torque observer and an adaptive speed controller for a brushless dc motor (BLDCM). The system is based on the vector control drive strategy. The speed-sensorless approach first employs a load observer to estimate the disturbed load torque, and then the estimated load torque is substituted into the mechanical dynamic equation to determine the rotor speed, and thus develop a speed-sensorless algorithm. Additionally, the mechanical rotor inertia constant and the friction coefficient, which are the inputs of the load observer, are estimated using the recursive least-square rule. Therefore, the proposed speed-sensorless approach is unaffected by the time-variant motor parameters nor is affected by the integrator drift problem. It also has a simpler computing algorithm than the extended Kalman filter for estimating the speed. The modified model reference adaptive system algorithm, an adaptive control algorithm, is adopted as a speed controller of the BLDCM to improve the performance of the speed-sensorless approach. Simulation and experimental results confirm that the performance of the design of a new integrated speed-sensorless approach and the adaptive speed controller is good.     

On Sensorless Induction Motor Drives: Sliding-Mode Observer and Output Feedback Controller

In this paper, a sensorless output feedback controller is designed in order to drive the induction motor (IM) without the use of flux and speed sensors. First, a new sliding-mode observer that uses only the measured stator currents is synthesized to estimate the speed, flux, and load torque. Second, a current-based field-oriented sliding-mode control is developed so as to steer the estimated speed and flux magnitude to the desired references. A stability analysis based on the Lyapunov theory is also presented in order to guarantee the closed-loop stability of the proposed observer-control system. Two experimental results for a 1.5-kW IM are presented and analyzed by taking into account the unobservability phenomena of the sensorless IM.      

Comparative study of adaptive and inherently sensorless observers for variable-speed induction-motor drives

State observers are key components of modern ac drives. The paper presents a comparative analysis of two state observers for induction-motor (IM) drives: the speed-adaptive observer and the inherently sensorless observer. The adaptive observer employs the time-variable full-order motor model with the rotor speed as the adaptive quantity. Thus, the speed estimation accuracy significantly impacts on the flux observer. It is shown that the popular model reference adaptive system (MRAS) speed estimator displays reduced bandwidth, and does not deliver adequate performance for the flux estimation. The inherently sensorless observer employs a full-order dual reference-frame model in order to eliminate the speed adaptation. In this way, it becomes decoupled from the speed estimator and its performance is superior to that of its adaptive counterpart. Theoretical aspects and comparative simulation results are discussed for both observers. Comparative experimental results for both observers are presented. Very low-speed-operation (3 r/min) capability of the drive with the sensorless observer is demonstrated.     

Kalman filter and LQ based speed controller for torsional vibrationsuppression in a 2-mass motor drive system

In this paper, a high-performance speed control for torsional vibration suppression in a 2-mass motor drive system, like a rolling mill which has a long shaft and large loadside mass or a robot arm which has flexible coupling, was studied. The speed control method which has better control response than a typical one in command following, torsional vibration suppression, disturbance rejection, and robustness to parameter variation, was proposed. The performance of command following, torsional vibration suppression, and robustness to parameter variation was satisfied by using a Kalman filter and LQ based speed control with an integrator. Also, disturbance rejection performance was improved through load torque compensation. Through various experiments of a real 22 kW field oriented controlled AC motor drive system having 2-mass mechanical system, the characteristics of the proposed speed controller and typical PI speed controller were compared and analyzed     

Implementation of Artificial Neural Network-Based Tracking Controller for High-Performance Stepper Motor Drives

Two distinct multilayer perception neural networks (NNs) are implemented via laboratory experiment to simultaneously identify and adaptively control the trajectory tracking of a hybrid step motor assumed to operate in a high-performance drives environment. That is, a neural network identifier (NNI) which captures the nonlinear dynamics of the stepper motor drive system (SMDS) over any arbitrary time interval in its range of operation, and a neural network controller (NNC) to provide the necessary control actions as to achieve trajectory tracking of the rotor speed. The exact form of the control law is unknown, and must be estimated by the NNC. Consequently, the NNC is constructed as a nonlinear unknown function depending on the current state of the drive system supplies by the NNI and the reference trajectory we wish the outputs to follow. The two NNs are online trained using dynamic back-propagation algorithm. The composite structure is used as a speed controller for the SMDS. Performance of the composite controller is evaluated through a laboratory experiment. Experimental results show the effectiveness of this approach, and demonstrate the usefulness of the proposed controller in high-performance drives     

Robust decoupled control of direct field-oriented induction motor drive

This paper focuses on the development of a decoupling mechanism and a speed control scheme based on total sliding-mode control (TSMC) theory for a direct rotor field-oriented (DRFO) induction motor (IM). First, a robust decoupling mechanism including an adaptive flux observer and a sliding-mode current estimator is investigated to decouple the complicated flux and torque dynamics of an IM. The acquired flux angle is utilized for the DRFO object such that the dynamic behavior of the IM is like that of a separately excited dc motor. However, the control performance of the IM is still influenced seriously by the system uncertainties including electrical and mechanical parameter variation, external load disturbance, nonideal field-oriented transient responses, and unmodeled dynamics in practical applications. In order to enhance the robustness of the DRFO IM drive for high-performance applications, a TSMC scheme is constructed without the reaching phase in conventional sliding-mode control (CSMC). The control strategy is derived in the sense of Lyapunov stability theorem such that the stable tracking performance can be ensured under the occurrence of system uncertainties. In addition, numerical simulations as well as experimental results are provided to validate the effectiveness of the developed methodologies in comparison with a model reference adaptive system flux observer and a CSMC system.     

A Linearization Technique for RF Receiver Front-End Using Second-Order-Intermodulation Injection

A linearization technique is proposed in which low-frequency second-order-intermodulation $({rm IM}_{2})$ is generated and injected to suppress the third-order intermodulation $({rm IM}_{3})$. The proposed linearization technique is applied to both a low-noise amplifier (LNA) and a down-conversion mixer in an RF receiver front-end (RFE) working at 900 MHz. Fabricated in a 0.18$ mu{hbox{m}}$ CMOS process and operated at 1.5 V supply with a total current of 13.1 mA, the RFE delivers 22 dB gain with 5.3 dB noise figure (NF). The linearization technique achieves around 20 dB ${rm IM}_{3}$ suppression and improves the RFE's ${rm IIP}_{3}$ from $-$ 10.4 dBm to 0.2 dBm without gain reduction and noise penalty while requiring only an extra current of 0.1 mA.      

Design and implementation of an adaptive controller for current-fedinduction motor

A model reference adaptive speed controller for a current-fed induction motor drive is proposed. The controller uses a proportional-integral (PI) adaptation to satisfy the hyperstability condition for load and machine parameter changes of the drive. Only the available information on the states and output of the reference model as well as the plant output are required. No explicit parameter identification is needed. The controller can be designed simply by using a reduced reference model without particularly degrading the performance, so it is easy to implement practically. The hardware implementation is detailed, and some experimental results are given to demonstrate its effectiveness     

Novel Fuzzy Adaptive Sensorless Induction Motor Drive

Investigations were carried out on a novel sensorless drive for induction motors, based on the combination of an open-loop (OL) estimator and a steady-state (SS) estimator. The novelty of this new sensorless structure is obtained by an intelligent mixing of the OL estimator response with the SS one. A fuzzy system weights the two estimated speed values according to the motor operating point. Then, the final speed value is obtained averaging the previously weighted speed values. Moreover, the OL estimator response is improved by means of using a fuzzy-controlled adaptive filter that selects the optimum cutoff frequency. The aim of this paper is to obtain a moderate performance sensorless drive for induction motors that could be easily implemented for industrial applications without a high computational effort. Simulation and experimental results illustrate the operation and performance of the proposed fuzzy-logic-based sensorless drive.     

Model reference adaptive speed control for induction motor driveusing neural networks

A model reference adaptive speed control scheme using neural networks is presented. The robust observer-based model reference tracking control technique is used to establish the training patterns. Then, the trained neural networks are used as an adaptive speed controller to robustly track a reference model for an induction motor drive