Development of new training algorithms for neuro-wavelet systems on the robust control of induction servo motor drive

A robust wavelet neural network control (RWNNC) system is proposed to control the rotor position of an induction servo motor drive in this paper. In the proposed RWNNC system, a wavelet neural network controller is the main tracking controller that is used to mimic a computed torque control law, and a robust controller is designed to recover the residual approximation for ensuring the stable control performance. Moreover, to relax the requirement for a known bound on lumped uncertainty, which comprises a minimum approximation error, optimal network parameters and higher order terms in a Taylor series expansion of the wavelet functions, an RWNNC system with adaptive bound estimation was investigated for the control of an induction servo motor drive. In this control system, a simple adaptive algorithm was utilized to estimate the bound on lumped uncertainty. In addition, numerical simulation and experimental results due to periodic commands show that the dynamic behaviors of the proposed control systems are robust with regard to parameter variations and external load disturbance.     

Speed sensorless control of a bearingless induction motor with combined neural network and fractional sliding mode

To address the problem of speed and flux observation in sensorless control of a bearingless induction motor under the influence of parameter changes and external disturbances, a speed sensorless control strategy combining radial basis function (radial basis function, RBF) neural network and fractional sliding mode is proposed. According to the current error, fractional sliding mode control rate is designed to reduce the speed-observed chatter of the bearingless induction motor and its adverse effect on the rotor suspension stability. Then, combined with the theory of RBF neural network, the new optimal control rate is obtained by using its approximation ability. At the same time, the stability of two control rate is proved. Thus, the flux linkage and speed under normal operation, parameter change and external disturbance are observed and the new speed sensorless control is realized. The simulation and experimental results show that the proposed joint RBF neural network approximation algorithm and fractional sliding mode speed sensorless control system of the bearingless induction motor can not only effectively identify the flux and speed under three conditions of no-load, load disturbance and speed change, but also ensure the good suspension of the motor rotor in the x-axis and y-axis directions.     

A robust speed controller for induction motor drives

A speed controller considering the effects of parameter variations and external disturbance for indirect field-oriented induction motor drives is proposed in this paper. First a microprocessor-based indirect field-oriented induction motor drive is implemented and its dynamic model at nominal case is estimated. Based on the estimated model, an integral plus proportional (IP) controller is quantitatively designed to match the prescribed speed tracking specifications. Then a dead-time compensator and a simple robust controller are designed and augmented to reduce the effects of parameter variations and external disturbances. The desired speed tracking control performance of the drive can be preserved under wide operating range, and good speed load regulating performance can also be obtained. Theoretic basis and implementation of the proposed controller are detailedly described. Some simulated and experimental results are provided to demonstrate the effectiveness of the proposed controller     

交流电机的智能PID控制应用研究

针对交流调速传统控制调速过程中往往会出现转速波动大和超调量等问题,无法满足控制系统的高性能要求,提出了一种自适应神经网络PID控制算法,应用反向传播人工神经网络理论,对于系统模型参数未知的情况下,使用两个人工神经网络分别进行控制系统在线辨识与PID控制器参数在线调整。经与PID控制对比进行了试验验证,表明本控制算法能让系统在很短的时间内调整出优良的控制参数,能够很好的跟踪负载变化,动态响应快,速度跟随准确,具有很强的自适应性和鲁棒性。     

A Third-Order Sliding-Mode Controller for a Stepper Motor

This paper deals with the robust control problem of a stepper motor subject to parameter uncertainties and load torque perturbation. The developed algorithm is based on third-order sliding-mode control such that a desired angular motor position is accurately tracked. The proposed scheme requires the measurement or the estimation of the motor speed and acceleration for feedback. To avoid the use of tachometers and accelerometers which add cost and energy consumption, a robust second-order sliding-mode observer is presented. Experimental results illustrate the performance and the advantages of the proposed controller.      

Direct torque control of an induction motor using a single current sensor

A novel scheme for the direct torque control (DTC) of an induction motor (IM) is proposed, which uses a single sensor of current inserted in the inverter dc link. The rationale behind the proposal is to develop a low-cost but high performance IM drive. The scheme exploits a simple and robust algorithm to reconstruct the stator currents needed to estimate the motor flux and torque. The algorithm operates in two stages: first, it predicts the stator currents from a model of the motor and then adjusts the prediction on the basis of the sensed dc-link current. Experimental results are given to demonstrate the ability of the scheme in reproducing the performance of a traditional DTC IM drive.     

Design and analysis of a plug-in robust compensator: an application to indirect-field-oriented-control induction machine drives

It is well known that the system performance for an indirect-field-oriented-control induction motor drive degrades under the variation of rotor resistance and in the presence of external load torque. In this paper, a plug-in robust compensator for speed and position control enhancement of an indirect-field-oriented-control induction machine drive is developed. In the case where a controller for the induction machine already exists or is in operation with satisfactory nominal tracking performance, this plug-in compensator, designed using the H/sub /spl infin// loop-shaping techniques, can be plugged into the existing controller without affecting the already satisfactory nominal tracking performance of the existing closed-loop system but with the capability to improve the system performance under plant parameter variations and in the presence of external disturbances. Simulation and experimental results are given to validate the proposed plug-in robust compensator.     

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     

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.     

Fuzzy supervisory sliding-mode and neural-network control for robotic manipulators

Highly nonlinear, highly coupled, and time-varying robotic manipulators suffer from structured and unstructured uncertainties. Sliding-mode control (SMC) is effective in overcoming uncertainties and has a fast transient response, while the control effort is discontinuous and creates chattering. The neural network has an inherent ability to learn and approximate a nonlinear function to arbitrary accuracy, which is used in the controllers to model complex processes and compensate for unstructured uncertainties. However, the unavoidable learning procedure degrades its transient performance in the presence of disturbance. A novel approach is presented to overcome their demerits and take advantage of their attractive features of robust and intelligent control. The proposed control scheme combines the SMC and the neural-network control (NNC) with different weights, which are determined by a fuzzy supervisory controller. This novel scheme is named fuzzy supervisory sliding-mode and neural-network control (FSSNC). The convergence and stability of the proposed control system are proved by using Lyapunov's direct method. Simulations for different situations demonstrate its robustness with satisfactory performance.     

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     

基于模糊神经网络的永磁同步电机伺服系统研究

永磁同步电机具有非线性、强耦合的特性,常规的矢量控制方法难以对其进行精确控制。此外,电机系统易受负载扰动影响,从而产生转速和电磁转矩波动。针对转速环参数固定会导致系统响应速度慢、超调量大的问题,文中提出了一种模糊径向基神经网络PID控制策略,用以替代矢量控制系统中转速环PID控制。将神经网络和模糊控制相结合,基于增量式PID控制方式,利用梯度下降优化算法动态调整转速环中的PID参数。系统模型仿真结果表明,模糊神经网络PID控制的电机系统超调量较小,相较于常规PID控制,新模型在低速和高速运行的启动时间分别缩短了66.7%和75.9%,动态响应更快,具有更好的鲁棒性和抗干扰能力。利用DSP搭建了实验平台,实验结果也证明了该控制方法的有效性。     

An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model

This paper presents a new model reference adaptive system (MRAS) speed observer for high-performance field-oriented control induction motor drives based on adaptive linear neural networks. It is an evolution and an improvement of an MRAS observer presented in the literature. This new MRAS speed observer uses the current model as an adaptive model discretized with the modified Euler integration method. A linear neural network has been then designed and trained online by means of an ordinary least-squares (OLS) algorithm, differently from that in the literature which employs a nonlinear backpropagation network (BPN) algorithm. Moreover, the neural adaptive model is employed here in prediction mode, and not in simulation mode, as is usually the case in the literature, with a consequent quicker convergence of the speed estimation, no need of filtering the estimated speed, higher bandwidth of the speed loop, lower estimation errors both in transient and steady-state operation, better behavior in zero-speed operation at no load, and stable behavior in field weakening. A theoretical analysis of some stability issues of the proposed observer has also been developed. The OLS MRAS observer has been verified in numerical simulation and experimentally, and in comparison with the BPN MRAS one presented in the literature.     

Integrated fault-tolerant scheme for a DC speed drive

In this paper, an active fault-tolerant control (FTC) scheme is presented with disturbance compensation. Fault-detection and compensation are merged together to propose a robust algorithm against model uncertainties. The GIMC control architecture is used as a feedback configuration for the active fault-tolerant scheme. The synthesis procedure for the parameters of the fault-tolerant scheme is carried out by using tools of robust control theory. A detection filter is designed for fault isolation taking into account uncertainties and disturbances in the mathematical model. Finally, the fault compensation strategy incorporates an estimate of the disturbances into the system to improve the performance of the closed-loop systems after the fault is detected. In order to illustrate these ideas, the speed regulation of a dc motor is selected as a case study, and experimental results are reported.     

Cascade Control of PM DC Drives Via Second-Order Sliding-Mode Technique

This paper presents a novel scheme for the speed/position control of permanent-magnet (PM) dc motor drives. A cascade-control scheme, based on multiple instances of a second-order sliding-mode-control (2-SMC) algorithm, is suggested, which provides accurate tracking performance under large uncertainty about the motor and load parameters. The overall control scheme is composed of three main blocks: 1) a 2-SMC-based velocity observer which uses only position measurements; 2) a 2-SMC-based velocity control loop that provides a reference command current; and 3) a 2-SMC-based current control loop generating the reference voltage. The proposed scheme has been implemented and tested experimentally on a commercial PM dc motor drive. The experimental results confirm the precise and robust performance and the ease of tuning and implementation, featured by the proposed scheme.      

Digital Simulation of Field-Oriented Control of Induction Motor

This paper describes the simulation of a control scheme using the principle of field orientation for the control of a voltage source inverter-fed induction motor. The control principle is explained, followed by an algorithm to simulate various components of the system in the digital computer. The dynamic response of the system for the load disturbance and set-point variations have been studied. Also, the results of the simulation showing the behavior of field coordinates for such disturbances are given.     

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     

Development of intelligent position control system using optimal design technique

This paper addresses the application of an intelligent optimal control system (IOCS) to control an indirect field-oriented induction servo motor drive for tracking periodic commands via a wavelet neural network. With the field orientation mechanism, the dynamic behavior of an induction motor is rather similar to a linear system. However, the uncertainties, such as mechanical parametric variation, external load disturbance and unmodeled dynamics in practical applications, influence the designed control performance seriously. Therefore, an IOCS is proposed to confront these uncertainties existing in the control of the induction servo motor drive. The control laws for the IOCS are derived in the sense of the optimal control technique and Lyapunov stability theorem, so that system-tracking stability can be guaranteed in the closed-loop system. With the proposed IOCS, the controlled induction servo motor drive possesses the advantages of good tracking control performance and robustness to uncertainties under wide operating ranges. The effectiveness of the proposed control scheme is verified by both simulated and experimental results. Moreover, the advantages of the proposed control system are indicated in comparison with the sliding-mode control system.     

Robust control using neural network uncertainty observer for linearinduction motor servo drive

A robust controller, that combines the merits of integral-proportional (IP) position control and neural network (NN) observed technique, is designed for a linear induction motor (LIM) servo drive in this study. First, the secondary flux of the LIM is estimated using a sliding-mode flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust and the flux amplitude of the LIM. Then, the IP position controller is designed according to the estimated mover parameters to match the time-domain command tracking specifications. Moreover, a robust controller is formulated using the NN uncertainty observer, which is implemented to estimate the lumped uncertainty of the controlled plant, as an inner-loop force controller to increase the robustness of the LIM servo drive system. Furthermore, in the derivation of the online training algorithm of the NN, an error function is used in the Lyapunov function to avoid the real-time identification of the system Jacobian. In addition, to increase the speed and accuracy of the estimated flux, the sliding-mode flux observer is implemented using a 32 bit floating-point digital signal processor (DSP) with a high sampling rate. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results     

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.