A self-organizing fuzzy sliding-mode controller design for a classof nonlinear servo systems

A self-organizing fuzzy controller to augment a sliding-mode control (SOFSMC) scheme for a class of nonlinear systems is proposed. The motivation behind this scheme is to combine the best features of self-organizing fuzzy control and sliding-mode control to achieve rapid and accurate tracking control of a class of nonlinear systems. The chatter encountered by most sliding-mode control schemes is greatly alleviated without sacrificing invariant properties. A stability analysis is presented; the design guidelines and the class of applicable systems are clearly identified. To verify the scheme, the authors performed experiments on its implementation in a magnetic levitation system. The results show that both alleviation of chatter and robust performance are achieved; the advantages of the scheme are indicated in comparison with the conventional sliding-mode design     

A hybrid computed torque controller using fuzzy neural network formotor-quick-return servo mechanism

The dynamic response of a hybrid computed torque controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this paper. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque control, a position controller is designed to control the position of a slider of the motor-quick-return servo mechanism. In addition, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a fuzzy neural network (FNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Moreover, a hybrid control system, which combines the computed torque controller, the FNN uncertainty observer, and a compensated controller, is developed based on Lyapunov stability to control the motor-quick-return servo mechanism. The computed torque controller with FNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rule numbers of the FNN. Finally, simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed hybrid computed torque control system are robust with regard to parametric variations and external disturbances     

Robust H/sub /spl infin// controller design with recurrent neural network for linear synchronous motor drive

In this paper, a robust controller design with H/sub /spl infin// performance using a recurrent neural network (RNN) is proposed for the position tracking control of a permanent-magnet linear synchronous motor. The proposed robust H/sub /spl infin// controller, which comprises a RNN and a compensating control, is developed to reduce the influence of parameter variations and external disturbance on system performance. The RNN is adopted to estimate the dynamics of the lumped plant uncertainty, and the compensating controller is used to eliminate the effect of the higher order terms in Taylor series expansion of the minimum approximation error. The tracking performance is ensured in face of parameter variations, external disturbance and RNN estimation error once a prespecified H/sub /spl infin// performance requirement is achieved. The synthesis of the RNN training rules and compensating control are based on the solution of a nonlinear H/sub /spl infin// control problem corresponding to the desired H/sub /spl infin// performance requirement, which is solved via a choice of quadratic storage function. The proposed control method is able to track both the periodic step and sinusoidal commands with improved performance in face of large parameter perturbations and external disturbance.     

Real-time IP position controller design with torque feedforwardcontrol for PM synchronous motor

A digital signal processor (DSP)-based permanent magnet (PM) synchronous motor (SM) drive with a proposed recursive least-square (RLS) estimator and real-time integral-proportional (IP) position controller is introduced in this study. First, the rotor inertia constant, the damping constant, and the disturbed load torque of the synchronous motor are estimated by the proposed RLS estimator, which is composed of an RLS estimator and a torque observer. Next, the IP position controller is real-time designed according to the estimated rotor parameters, to match the time-domain command tracking specifications. Then, the observed disturbance torque is fed forward, to increase the robustness of the synchronous motor drive     

Adaptive enhanced fuzzy sliding-mode control for electrical servo drive

The design and properties of an adaptive enhanced fuzzy sliding-mode control (AEFSMC) system for an indirect field-oriented induction motor (IM) drive to track periodic commands are addressed in this study. A newly designed EFSMC system, in which a translation-width idea is embedded into the FSMC, is introduced initially. Moreover, to confront the uncertainties existed in practical applications, an adaptive tuner, which is derived in the sense of the Lyapunov stability theorem, is utilized to adjust the EFSMC parameter for further assuring robust and optimal control performance. The indirect field-oriented IM drive with the AEFSMC scheme possesses the salient advantages of simple control framework, free from chattering, stable tracking control performance, and robust to uncertainties. In addition, numerical simulation and experimental results due to periodic sinusoidal commands are provided to verify the effectiveness of the proposed control strategy, and its advantages are indicated in comparison with FSMC and EFSMC systems.     

An adaptive fuzzy sliding-mode controller for servomechanismdisturbance rejection

A two-level spring-lumped mass servomechanism system was constructed for disturbance rejection control investigation. This dynamic absorber is similar to a model of the serial-type vehicle suspension system. The lower level is actuated by two DC servo motors, to provide the specified internal and external disturbances to the vibration control system. The upper level has another DC servo motor to control the main body balancing position. In order to tackle the system's nonlinear and time-varying characteristics, an adaptive fuzzy sliding-mode controller is proposed to suppress the main mass position variation due to external disturbance. This intelligent control strategy combines an adaptive rule with fuzzy and sliding-mode control technologies. It has online learning ability for responding to the system's time-varying and nonlinear uncertainty behaviors, and for adjusting the control rules and parameters. Only seven rules are required for this control system, and its control rules can be established and modified continuously by online learning. The experimental results show that this intelligent control approach effectively suppresses the vibration amplitude of the body, with respect to the external disturbance     

Comparison of sliding-mode and fuzzy neural network control formotor-toggle servomechanism

A comparative study of sliding-mode control and fuzzy neural network (FNN) control on the motor-toggle servomechanism is presented. The toggle mechanism is driven by a permanent-magnet synchronous servomotor. The rod and crank of the toggle mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the equation of motion. Then, based on the principles of the sliding-mode control, a robust controller is developed to control the position of a slider of the motor-toggle servomechanism. Furthermore, an FNN controller with adaptive learning rates is implemented to control the motor-toggle servomechanism for the comparison of control characteristics. Simulation and experimental results show that both the sliding-mode and FNN controllers provide high-performance dynamic characteristics and are robust with regard to parametric variations and external disturbances. Moreover, the FNN controller can result in small control effort without chattering     

A supervisory fuzzy neural network controller for slider-crank mechanism

A supervisory fuzzy neural network (FNN) controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position controller. The supervisory FNN controller comprises a sliding mode FNN controller and a supervisory controller. The sliding mode FNN controller combines the advantages of the sliding mode control with robust characteristics and the FNN with on-line learning ability. The supervisory controller is designed to stabilize the system states around a defined bound region. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results are provided to show that the proposed control system is robust with regard to plant parameter variations and external load disturbance.     

A fuzzy neural network controller for parallel-resonant ultrasonicmotor drive

A newly designed driving circuit for the traveling-wave-type ultrasonic motor (USM), which consists of a push-pull DC-DC power converter and a current-source two-phase parallel-resonant inverter, is presented in this study. Moreover, since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, a fuzzy neural network (NN) controller is proposed to control the USM drive system. In the proposed controller, a fuzzy model-following controller is implemented to control the rotor position of the USM, and an online trained NN with variable learning rates is implemented to tune the output scaling factor of the fuzzy controller. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the desired variable learning rates. From the experimental results, accurate tracking response can be obtained by the proposed controller, and the influences of parameter variations and external disturbances on the USM drive also can be reduced effectively     

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.     

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     

Takagi–Sugeno fuzzy speed controller design for a permanent magnet synchronous motor

Based on Takagi–Sugeno (T–S) fuzzy approach we design a fuzzy speed control system for a permanent magnet synchronous motor (PMSM). We derive sufficient conditions for the existence of a T–S fuzzy speed regulator and acceleration observer in terms of linear matrix inequalities (LMIs). We parameterize the gain matrices using the LMI conditions. We implement the proposed T–S fuzzy speed control system by using a TMS320F28335 floating point DSP, and we give simulation and experimental results to verify that our method is practical and useful for controlling a PMSM under model parameter and load torque variations.     

多轴电机同步运动控制器设计

针对多轴电机同步控制问题,研究一种基于模糊-单神经元PID控制策略的同步控制器。使用研究的同步控制方法与常规PID算法进行对比分析。结果表明,相比基于常规PID算法的多轴电机同步运动控制器,在基于模糊-单神经元PID算法的多轴电机同步运动控制器作用下,各个电机之前的同步误差更小,当第二台电机发生载荷突变时,控制器能够有效抑制载荷突变对整个多轴电机系统的影响。基于模糊-单神经元PID算法的多轴电机同步运动控制器能够有效提升多轴电机同步运动系统的动态特性和稳定性。     

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     

网络遥操作机器人系统模糊变结构控制研究

针对网络遥操作机器人系统在实际应用中存在时延可能导致系统不稳定且难以控制的问题,通过对系统动力学建模和时延下系统理想性能分析,设计了一种新型的模糊滑模控制方案,在该方案中主机械手采用阻抗控制而从机械手采用模糊变结构控制。仿真结果表明,该控制方案能有效地抑制变结构控制中的抖振,系统能较好地实现位置比例跟踪和力比例跟踪。     

一种基于L-M算法的组合神经网络模糊控制器

提出了一种基于L-M算法的神经网络模糊控制器。用两个相同的三层前向神经网络①和②来生成E和EC的隶属度,用三层前向神经网络③来实现模糊控制规则并生成控制输出,经过研究和对比选择了对于这3个网络来说最好的训练算法Levenberg-Marquardt算法。仿真结果表明了该控制器极好的控制性能。     

A fuzzy scheduling controller for a computer disk filetrack-following servo

A fuzzy scheduling capability is superimposed on a computer disk drive track-following servocontroller to adjust for the plant variation as the actuator is locked onto different tracks on the disk. The fuzzy algorithm is found to best represent the complex relationship among the controllers for various tracks. Models of a Zentek 3100 disk drive actuator as it locks on a number of different tracks are experimentally identified to be the reference points. H_∞ design technique is employed to obtain a robust optimal controller for each reference point. The actual controller for the disk drive actuator is calculated using fuzzy interpolation. It is shown that with the controller scheduling action, the closed-loop performance is improved for the actuator at every track position. Error can be kept at a lower level than is the case when only a single controller is used     

无人机模糊小波神经网络轨迹线性化控制

针对系统存在不确定和有界干扰的情况,提出了一种基于模糊小波神经网络的轨迹线性化控制方法。利用模糊小波神经网络对非线性函数的逼近能力,减小不确定干扰对系统的影响,并与轨迹线性化方法结合设计了无人机飞控系统控制器。采用Lyapunov稳定性理论,证明了在所设计的控制器下,闭环系统所有信号一致最终有界。最后对系统存在不确定的情况下进行了仿真,并与没有加模糊小波神经网络的轨迹线性化控制器进行了对比,仿真结果证明了所提方法的有效性和鲁棒性。     

单隐含层模糊递归小波神经网络的观测器设计

根据模糊神经网络在非线性函数逼近方面的特性和小波变换具有良好的时频两维信号的分析能力,建立了结合两者优点的单隐含层模糊递归小波神经网络(Single hidden Layer Fuzzy Recurrent Wavelet Neural Network,SLFRWNN),并分析了SLFRWNN的结构、激活函数形式及激活函数对网络性能的影响.在此基础上,提出了一种基于SLFRWNN的自适应观测器设计方法,并通过引入Lyapunov函数,证明了这种观测器设计方法的稳定性,进而给出该网络观测器的初始化和最佳训练算法;仿真结果表明SLFRWNN观测器能很好地观测系统的状态.