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.     

Lateral stabilization of a four wheel independent drive electric vehicle on slippery roads

In this paper, a new controller is proposed for lateral stabilization of four wheel independent drive electric vehicles without mechanical differential. The proposed controller has three levels including high, medium and low control levels. Desired vehicle dynamics such as reference longitudinal speed and reference yaw rate are determined by higher level of controller. Moreover, using a neural network observer and a fuzzy logic controller, a novel reference longitudinal speed generator system is presented. This system guarantees the vehicle’s stable motion on the slippery roads. In this paper, a new sliding mode controller is proposed and its stability is proved by Lyapunov stability theorem. This sliding mode control structure is faster, more accurate, more robust, and with smaller chattering than classic sliding mode controller. Based on the proposed sliding mode controller, the medium control level is designed to determine the desired traction force and yaw moment. Therefore, suitable wheel forces are calculated. Finally, the effectiveness of the introduced controller is investigated through conducted simulations in CARSIM and MATLAB software environments.     

Robust fuzzy neural network sliding mode control scheme for IPMSM drives

This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.     

自适应模糊滑模控制器设计

针对传统滑模控制的抖振问题,利用线性化反馈技术,将模糊自适应和滑模控制相结合,设计一种新型的模糊滑模控制器。通过模糊推理和基于Lyapunov函数的稳定性分析,获得模糊控制规则的自适应律,构成自适应模糊滑模控制器,有效解决了传统滑模控制中,需要确定参数摄动和外部干扰上确界不确定性问题,倒立摆上的运行结果表明该方法的有效性。     

直流微电网双向AC/DC变换器鲁棒前馈控制策略研究

针对双向AC/DC功率变换器在直流微电网母线电压稳定性方面的问题,文中提出了一种结合LESO和滑模理论的前馈鲁棒控制策略。通过建立直流微电网三相AC/DC双向功率变换器的动态数学模型,架构了三阶线性扩张状态观测器,并将三阶LESO的观测值用于滑模控制器的设计。该控制策略能够在不需要额外电流传感器的情况下实现前馈控制,并确保系统具有良好的动态性能。该策略还能够有效降低滑模控制的实现难度,提高系统的鲁棒性。仿真分析验证了文中所提控制策略的有效性。     

Fuzzy neural networks for identification and control of ultrasonicmotor drive with LLCC resonant technique

This paper demonstrates the applications of fuzzy neural networks (FNNs) in the identification and control of the ultrasonic motor (USM). First, the USM is derived by a newly designed high-frequency two-phase voltage-source inverter using LLCC resonant technique. Then, two FNNs with varied learning rates are proposed to control the rotor position of the USM. The USM drive system is identified by a fuzzy neural network identifier (FNNI) to provide the sensitivity information of the drive system to a fuzzy neural network controller (FNNC). A backpropagation algorithm is used to train both the FNNI and FNNC on-line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNNs. In addition, the effectiveness of the FNN-controlled USM drive system is demonstrated by experimental results. Accurate tracking response can be obtained due to the powerful on-line learning capability of the FNNs. Furthermore, the influence of parameter variations and external disturbances on the USM drive system can be reduced effectively     

Continuous variable structure controller for BLDDSM positioncontrol with prescribed tracking performance

The continuous, accurate, and robust sliding mode tracking controller based on a disturbance observer for a brushless direct drive servo motor (BLDDSM) is presented. Although the conventional sliding mode control (SMC) or variable structure control (VSC) can give the desired tracking performance, there exists an inevitable chattering problem in control which is undesirable for a direct drive system. With the proposed algorithm, not only are the chattering problems removed, but also the prescribed tracking performance can be obtained by using the efficient compensation of the disturbance observer. The design of the sliding mode tracking controller for the prescribed, accurate, and robust tracking performance without the chattering problem is given based on the results of the detailed stability analysis. The usefulness of the proposed algorithm is demonstrated through the computer simulations for a BLDDSM under load variations     

Sliding mode controller by use of neural networks

This paper proposes a method to design a robust controller by use of a neural network. The trained neural network functions as a sliding mode controller which is robust against uncertainties. From the analysis of the neural network, it is proved that the switching surface is not the same as the sliding surface like conventional sliding mode control theory. The neural network shows that the switching surface should be a nonlinear surface because of a hard limitation on control inputs, even if the designed sliding surface is linear. From the result of estimating the robustness of neural networks, we propose that generalization of neural networks which are used as controllers should be measured by the robustness. Numerical simulations show that the controller is robust against uncertainties and robustness can be improved by the proposed method.     

新型三相三线制并联有源滤波器的滑模控制方法

为了提高有源滤波器的谐波补偿效果,设计了一种新型滑模控制器,用于三相三线制并联有源滤波器的参考电流跟踪控制.谐波电流检测方法采用基于瞬时无功功率理论的谐波电流检测方法,能快速、准确的检测出负载电流中的谐波分量.直流侧电压控制方法采用PI控制方法实现.Simulink仿真结果显示,与传统的滞环比较控制方法相比,所设计的新型滑模控制方法能够有效的降低跟踪误差,提高有源滤波器的谐波补偿效果.     

Adaptive sliding controller with self-tuning fuzzy compensation for vehicle suspension control

Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate dynamic model for a model-based sliding mode control design. Here, a novel model-free adaptive sliding controller is proposed to suppress the position oscillation of the sprung mass in response to road surface variation. This control strategy employs the functional approximation technique to establish the unknown function for releasing the model-based requirement. In addition, a fuzzy scheme with online learning ability is introduced to compensate the functional approximation error for improving the control performance and reducing the implementation difficulty. The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function. The update laws for the coefficients of the Fourier series functions and the fuzzy tuning parameters are derived from a Lyapunov function to guarantee the control system stability. The experimental results show that the proposed control scheme effectively suppresses the oscillation amplitude of the vehicle sprung mass corresponding to the road surface variation and external uncertainties, and the control performance is better than that of a traditional model-based sliding mode controller.     

Sensorless Sine‐Wave Controller IC for PM Brushless Motor Employing Automatic Lead‐Angle Compensation

This paper presents an advanced sensorless permanent magnet (PM) brushless motor controller integrated circuit (IC) employing an automatic lead‐angle compensator. The proposed IC is composed of not only a sensorless sine‐wave motor controller but also an isolated gate‐driver and current self‐sensing circuit. The fabricated IC operates in sensorless mode using a position estimator based on a sliding mode observer and an open‐loop start‐up. For high efficiency PM brushless motor driving, an automatic lead‐angle control algorithm is employed, which improves the efficiency of a PM brushless motor system by tracking the minimum copper loss under various load and speed conditions. The fabricated IC is evaluated experimentally using a commercial 200 W PM brushless motor and power switches. The proposed IC is successfully operated without any additional sensors, and the proposed algorithm maintains the minimum current and maximum system efficiency under 0 N·m to 0.8 N·m load conditions. The proposed IC is a feasible sensorless speed controller for various applications with a wide range of load and speed conditions.     

具有噪声扰动的统一混沌系统的同步控制

研究了一类具有噪声扰动的统一混沌系统的同步控制问题。基于滑模变结构控制理论,提出了利用不确定性观测器估计扰动噪声,用噪声的观测值来设计控制器的新方法,而且适当选取观测器反馈增益可以使观测误差充分的小,从而可得到几乎没有受到扰动时的控制效果。和常规滑模变结构控制方法相比,所设计的控制器既能有效地抑制抖振,又能降低控制器参数设计的保守性。最后,基于Matlab 6.5软件对Lorenz混沌系统进行分析和数值模拟,验证了该滑模控制方法在混沌同步中的优良性能。     

航空发动机模糊滑模变结构控制研究

针对航空发动机是一个具有时变不确定性的非线性系统,结合模糊控制和滑模变结构控制的特点,提出了一种基于模糊滑模变结构控制的航空发动机控制方法.采用线性滑模面和指数趋近律设计变结构控制器,应用模糊逻辑系统自适应调节切换增益,避免了传统滑模变结构控制系统在到达阶段对不确定性的敏感性,消除了滑模控制中的抖振.通过数字仿真,结果表明所设计的模糊滑模变结构控制器对系统的参数摄动和外部干扰具有不变性,使被控系统在整个控制阶段都具有较强的鲁棒性.     

An adaptive hierarchical control approach of vehicle handling stability improvement based on Steer-by-Wire Systems

This paper proposes a novel adaptive hierarchical control approach for Steer-by-Wire (SbW) vehicles to improve the handling stability. The high-level stability control scheme contains a variable steering ratio (VSR) strategy based on the adaptive-network-based fuzzy inference system (ANFIS) and an active front steering (AFS) controller designed with the integral sliding mode method by tracking the expected yaw rate, in which the desired front wheel angle is generated to enhance the cornering stability performance. Besides, an adaptive tracking controller (ATC) for the SbW system is designed by using the adaptive sliding mode control method to achieve desired steering performance in the lower level. The proposed adaptive control strategy is validated with different driving circles from ISO standards in simulation tests and hardware-in-the-loop (HiL) experiments. The results demonstrate that the designed control approach improve the vehicle handling stability significantly, even in some extreme driving conditions.     

Sliding mode control combined with extended state observer for an ankle exoskeleton driven by electrical motor

A novel sliding mode control combined with extended state observer (ESO) is proposed for an ankle exoskeleton driven by electrical motor. During the process of assisting, it is necessary to design an effective controller for assisting torque of ankle exoskeleton. However, the parameter uncertainty of complex dynamics model and the irregular motion of human ankle may affect the torque control accuracy. For a high control precision of assisting torque when facing the modeling uncertainty, the sliding mode control is employed, but a large switching gain is usually needed in order to suppress the disturbance, which cause the control signal vibrate greatly. ESO can observe and suppress the disturbance and modeling uncertainty, but its tracking performance needs to be improved. Therefore, the proposed complex controller takes the advantages of sliding mode control and extended state observer, which can not only improve torque tracking performance but also overcome the disturbance force caused by the change of human joint angle without increasing chattering of control signal. Experimental studies are carried out to validate the effectiveness of the proposed control. The results show the presented controller have better torque tracking performance and robustness stability, and the proposed controller can reduce the chattering compared with the tradition sliding mode control.     

Fractional delay compensated discrete-time SMC for networked control system

Time-varying network induced delay in the communication channel severely affects the performance of closed loop network control systems. In this paper, a novel idea of compensating the fractional time varying communication delay in the sliding surface is presented. The fractional time delay in the sensor to controller and controller to actuator channel is approximated using the Thiran approximation technique to design the sliding surface. A discrete-time sliding mode control law is derived using the proposed surface that compensates fractional time delay in sensor to controller and controller to actuator channels for uncertain network control systems. The sufficient condition for closed loop stability of the system is derived using the Lyapunov function. The efficacy of the proposed strategy is supported by the simulation results.     

电流模式模糊控制Boost变换器的研究与建模

基于Boost变换器,介绍了一种新的控制方法一电流模式模糊控制。这种新的控制方法属于双环控制,外环由模糊控制器构成,内环是电流环。该控制方法不同于传统的以模糊控制器作控制环路的单环控制。这种新的控制方法结合了传统的模糊控制和电流模式控制的优点,能改善变换器系统的性能。本文建立了电流模式模糊控制的Boost变换器的小信号模型,推导了传递函数。在Matlab／Simulink环境下,做了基于传递函数的仿真和基于电路模块的仿真。仿真结果显示基于传递函数的仿真和基于电路模块的仿真结果一致,证实了本文所建立模型的正确性。     

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.     

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     

制受限滑模控制工频全桥逆变器分析

该文针对滑模控制单相全桥工频逆变器滑模系数选取困难的问题,基于滑模控制理论建立了单相全桥逆变系统的相变量模型,根据控制受限思想和滑动模态存在条件推导了滑模域与负载电阻和滑模系数之间的数学关系,深入分析了滑模域边界与滑模系数、负载电阻和负载跃变幅度的关系,提出了选取滑模系数的基本原则.基于逆变器加载系统轨线,结合滑模域右边界条件和逆变器期望动态设计指标,给出了一个计算滑模系数的公式,随后给出了切换系数的选取方法,并总结了基于滞环调制的滑模控制器的设计方法.仿真试验采用该方法设计了滑模控制器,结果验证了该设计方法的正确性和有效性.此法操作简便且易于掌握,具有较好理论参考价值和工程推广价值.