基于模糊控制的飞行仿真电动伺服加载系统

飞行模拟器上利用电动伺服加载系统替代传统的电液伺服加载后,需要进一步提高加载系统快速性、扩宽系统频带.建立了电动伺服加载模型,基于不变性原理对多余力矩进行补偿仍不能满足要求的情况下,利用模糊控制的快速响应特性,针对该系统设计出PI和模糊控制相结合的分段复合控制器.通过Matlab仿真结果表明使该系统既保证了控制无静差,静态稳定性好,同时动态性能有了较大提高.     

导弹电液伺服机构的自适应模糊滑模跟踪控制

针对导弹电液伺服机构的跟踪控制问题,提出了一种自适应模糊滑模的设计方案.使用具有参数在线调节的自适应模糊控制,逼近滑模控制中的等效控制部分,并确定非线性控制项以保证系统的稳定性.根据滑模控制原理给出四条模糊规则,以平滑不连续控制,达到削弱抖振的目的.仿真结果表明了该方案的有效性.     

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     

Total sliding-mode controller for PM synchronous servo motor driveusing recurrent fuzzy neural network

In this paper, the dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode-controlled permanent-magnet (PM) synchronous servo motor are described. First, a newly designed total sliding-mode control system, which is insensitive to uncertainties, including parameter variations and external disturbance in the whole control process, is introduced. The total sliding-mode control comprises the baseline model design and the curbing controller design. In the baseline model design, a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design, an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system, the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, i.e., the assumption of known uncertainty bounds and the chattering phenomena in the control effort, an RFNN sliding-mode control system is investigated to control the PM synchronous servo motor. In the RFNN sliding-mode control system, an RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties     

Nonlinear control for linear induction motor servo drive

This paper describes a newly designed nonlinear control strategy to control a linear induction motor servo drive for periodic motion. Based on the concept of the nonlinear state feedback theory and optimal technique, a nonlinear control strategy, which is composed of an adaptive optimal control system and a sliding-mode flux observation system, is developed to improve the drawbacks in previous works concerned with complicated intelligent control. The control and estimation methodologies are derived in the sense of Lyapunov theorem so that the stability of the control system can be guaranteed. The sliding-mode flux observation system is implemented using a digital signal processor with a high sampling rate to make it possible to achieve good dynamics. Computer simulations and experimental results have been conducted to validate the effectiveness of the proposed control scheme under the occurrence of possible uncertainties and different reference trajectories. The merits of the proposed control system are indicated in comparison with a traditional optimal control system.     

A new switching surface sliding-mode speed control for inductionmotor drive systems

In this paper, a sliding-mode speed controller based on a new switching surface is proposed for induction motor systems. With this variable structure control switching surface, the exponential stability is guaranteed for the speed servo control and insensitivity to uncertainties and disturbances are obtained as well. Moreover, an adaptive variable structure speed control is studied to relax the need for the bound of disturbance in variable structure control. The insensitivity or robustness of the proposed method for general speed servo systems is maintained, and the dynamic performances are improved as well. Finally, the validity of proposed scheme is demonstrated by computer simulations and experimentations     

Nonlinear sliding-mode torque control with adaptive backsteppingapproach for induction motor drive

In this paper, the nonlinear sliding-mode torque and flux control combined with the adaptive backstepping approach for an induction motor drive is proposed. Based on the state-coordinates transformed model representing the torque and flux magnitude dynamics, the nonlinear sliding-mode control is designed to track a linear reference model. Furthermore, the adaptive backstepping control approach is utilized to obtain the robustness for mismatched parameter uncertainties. With the proposed control of torque and flux amplitude, the controlled induction motor drive possesses the advantages of good transient performance and robustness to parametric uncertainties, and the transient dynamics of the induction motor drive can be regulated through the design of a linear reference model which has the desired dynamic behaviors for the drive system. Finally, some experimental results are demonstrated to validate the proposed controllers     

Robust adaptive sliding-mode control using fuzzy modeling for aninverted-pendulum system

In this paper, a new robust adaptive control architecture is proposed for operation of an inverted-pendulum mechanical system. The architecture employs a fuzzy system to adaptively compensate for the plant nonlinearities and forces the inverted pendulum to track a prescribed reference model. When matching with the model occurs, the pendulum will be stabilized at an upright position and the cart should return to its zero position. The control scheme has a sliding control input to compensate for the modeling errors of the fuzzy system. The gain of the sliding input is automatically adjusted to a necessary level to ensure the stability of the overall system. Global asymptotic stability of the algorithm is established via Lyapunov's stability theorem. Experiments on an inverted-pendulum system are given to show the effectiveness of the proposed control structure     

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 novel motor drive design for incremental motion system via sliding-mode control method

This paper proposes a particular motor position control drive design via a novel sliding-mode controller. The newly designed controller is especially suitable for the motor incremental motion control which is specified by a trapezoidal velocity profile. The novel sliding-mode controller is designed in accordance with the trapezoidal velocity profile to guarantee the desired performance. A motor control system associated PC-based incremental motion controller with permanent-magnet synchronous motor is built to verify the control effect. The validity of the novel incremental motion controller with sliding-mode control method is demonstrated by simulation and experimental results.     

基于自适应模糊理论的某型无人机起飞控制方法

基于合理简化的无人机纵向模型,设计了一种自适应模糊控制器,该控制器将Takagi-Sugeno模糊系统与等效控制器相结合,以增强系统的鲁棒性.只要求预先知道系统的相对阶以及未知函数的上下界即可,不需要精确的数学模型.Lyapunov合成方法证明了跟踪误差能趋近于零且其余的控制信号均有界.最后,结合优先级按比例分配的控制分配器,给出了存在扰动情况下飞行控制系统的仿真结果,表明即使在模型部分未知的情况下,该系统仍然能够达到飞行控制的指标性能和品质要求,验证了该方法的有效性.     

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     

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     

LQG/LTR control of an AC induction servo drive

A new design method based on the linear-quadratic-Gaussian with loop-transfer-recovery (LQG/LTR) theory has been developed for the design of high performance AC induction servomotor drives using microcomputer-based digital control. The principle of field orientation is employed to achieve the current decoupling control of an induction motor. An equivalent model representing the dynamics of the decoupled induction motor has been developed. Based on the developed model with specified parameter uncertainties and given performance specifications, a frequency domain loop-gain-shaping method based on the LQG/LTR theory is proposed for the design of the servo loop controller. A microcomputer-based induction servomotor drive has been constructed to verify the proposed control scheme. Simulation and experimental results are given to illustrate the effectiveness of the proposed design method     

Adaptive fuzzy neural network control for motor-toggle servomechanism

In this study, the dynamic responses of an adaptive fuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the motor-toggle servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Moreover, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed adaptive and adaptive FNN controllers are robust with regard to uncertainties.     

Adaptive fuzzy control for the ship steering problem

Adaptive fuzzy H_∞ control is proposed for the ship steering problem. The objective is to keep the influence of the modelling errors and the external disturbances on the tracking error below an arbitrary desirable level. The method is compared to nonlinear backstepping control. Simulation tests show the method’s efficiency.     

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