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.     

Robust Fuzzy Neural Network Sliding-Mode Control for Two-Axis Motion Control System

A robust fuzzy neural network (RFNN) sliding-mode control based on computed torque control design for a two-axis motion control system is proposed in this paper. The two-axis motion control system is an$x-y$table composed of two permanent-magnet linear synchronous motors. First, a single-axis motion dynamics with the introduction of a lumped uncertainty including cross-coupled interference between the two-axis mechanism is derived. Then, to improve the control performance in reference contours tracking, the RFNN sliding-mode control system is proposed to effectively approximate the equivalent control of the sliding-mode control method. Moreover, the motions at$x$-axis and$y$-axis are controlled separately. Using the proposed control, the motion tracking performance is significantly improved, and robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained as well. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer. From the simulated and experimental results due to circle and four leaves reference contours, the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.     

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     

参数不确定移动机器人全局轨迹跟踪的自适应滑模控制

通过对一类质心和几何中心不重合情况下移动机器人轨迹跟踪问题的研究,得到了两独立驱动轮角速度为控制输入的机器人运动学模型.对于车轮半径和两驱动轮之间距离参数已知的情况,基于反演控制的思想设计了变结构控制的切换函数,构造了具有全局渐近稳定的滑模轨迹跟踪控制律,并针对这两个参数未知时,通过自适应方法对其进行参数估计,给出了自适应滑模轨迹跟踪控制律的设计方法.该方法设计过程简单且具有直观的稳定性分析,适用于移动机器人的全局轨迹跟踪控制.仿真算例验证了所提控制律的有效性和正确性.     

Experimental Comparison of Position Tracking Control Algorithms for Pneumatic Cylinder Actuators

Many researchers have investigated pneumatic servo positioning systems due to their numerous advantages: inexpensive, clean, safe, and high ratio of power to weight. However, the compressibility of the working medium, air, and the inherent nonlinearity of the system continue to make achieving accurate position control a challenging problem. In this paper, two control algorithms are designed for the position tracking problem and their experimental performance is compared for a pneumatic cylinder actuator. The first algorithm is sliding-mode control based on a linearized plant model (SMCL) and the second is sliding-mode control based on a nonlinear plant model (SMCN). Extensive experiments using different payloads (1.9, 5.8, and 10.8 kg), vertical and horizontal movements, and move sizes from 3 to 250 mm were conducted. Averaged over 70 experiments with various operating conditions, the tracking error for SMCN was 18% less than with SMCL. For a 5.8-kg payload and a 0.5-Hz 70-mm amplitude, sine wave reference trajectory, the root-mean-square error with SMCN was less than 0.4 mm for both vertical and horizontal motions. This tracking control performance is better than those previously reported for similar systems.     

Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggleservomechanism

The dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode controlled motor-toggle servomechanism are described. The servomechanism is a toggle mechanism actuated by a permanent magnet (PM) synchronous servo motor. First, a total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. 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, a RFNN sliding-mode control system is investigated to control the motor-toggle servomechanism. In the RFNN sliding-mode control system a 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 sinusoidal command show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties     

移动机器人全局轨迹跟踪的自适应滑模控制

讨论了两驱动后轮角速度为控制输入的移动机器人轨迹跟踪问题，针对含有未知参数的非完整移动机器人运动学模型．基于反演（backstepping）控制算法的思想设计了变结构控制的切换函数，并由此构造了具有全局渐近稳定的白适应滑模轨迹跟踪控制器。该方法设计过程简单并具有直观的稳定性分析，适用于移动机器人的全局轨迹跟踪控制。仿真结果表明了该方法的有效性和正确性。     

Reliable $$H_{\infty }$$ Stabilization of Fuzzy Systems with Random Delay Via Nonlinear Retarded Control

In this paper, the robust reliable \(H_\infty \) control problem has been investigated for a class of nonlinear discrete-time TS fuzzy systems with random delay. In particular, the proposed fuzzy system consists of local nonlinear models with set of fuzzy rules, but the conventional TS fuzzy systems has local linear models. Our attention is focused on the design of a feedback reliable nonlinear retarded control law to ensure the robust asymptotic stability for nonlinear discrete-time TS fuzzy system with admissible uncertainties as well as actuator failure cases and random delay. In particular, by using an input delay approach, the random delay with stochastic parameters in the system matrices is introduced in the system model. Based on the Lyapunov approach, firstly, a sufficient condition for asymptotic stability is proposed for TS fuzzy systems in the presence of actuator failures. Then, a robust reliable \(H_\infty \) control is designed for the uncertain TS fuzzy system by solving a strict linear matrix inequalities using the available numerical software. Finally, a numerical example based on real-time ball and beam system is provided to validate the effectiveness of the proposed design technique.     

A Neuro-Sliding-Mode Control With Adaptive Modeling of Uncertainty for Control of Movement in Paralyzed Limbs Using Functional Electrical Stimulation

During the past several years, several strategies have been proposed for control of joint movement in paraplegic subjects using functional electrical stimulation (FES), but developing a control strategy that provides satisfactory tracking performance, to be robust against time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, muscle fatigue, and external disturbances, and to be easy to apply without any re-identification of plant dynamics during different experiment sessions is still an open problem. In this paper, we propose a novel control methodology that is based on synergistic combination of neural networks with sliding-mode control (SMC) for controlling FES. The main advantage of SMC derives from the property of robustness to system uncertainties and external disturbances. However, the main drawback of the standard sliding modes is mostly related to the so-called chattering caused by the high-frequency control switching. To eliminate the chattering, we couple two neural networks with online learning without any offline training into the SMC. A recurrent neural network is used to model the uncertainties and provide an auxiliary equivalent control to keep the uncertainties to low values, and consequently, to use an SMC with lower switching gain. The second neural network consists of a single neuron and is used as an auxiliary controller. The control law will be switched from the SMC to neural control, when the state trajectory of system enters in some boundary layer around the sliding surface. Extensive simulations and experiments on healthy and paraplegic subjects are provided to demonstrate the robustness, stability, and tracking accuracy of the proposed neuroadaptive SMC. The results show that the neuro-SMC provides accurate tracking control with fast convergence for different reference trajectories and could generate control signals to compensate the muscle fatigue and reject the external disturbance.     

Neural-Network-Based Terminal Sliding-Mode Control of Robotic Manipulators Including Actuator Dynamics

A neural-network-based terminal sliding-mode control (SMC) scheme is proposed for robotic manipulators including actuator dynamics. The proposed terminal SMC (TSMC) alleviates some main drawbacks (such as contradiction between control efforts in the transient and tracking errors in the steady state) in the linear SMC while maintains its robustness to the uncertainties. Moreover, an indirect method is developed to avoid the singularity problem in the initial TSMC. In the proposed control scheme, a radial basis function neural network (NN) is adopted to approximate the nonlinear dynamics of the robotic manipulator. Meanwhile, a robust control term is added to suppress the modeling error and estimate the error of the NN. Finite time convergence and stability of the closed loop system can be guaranteed by Lyapunov theory. Finally, the proposed control scheme is applied to a robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.      

Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.     

FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive

A field-programmable gate array (FPGA)-based adaptive backstepping sliding-mode controller is proposed to control the mover position of a linear induction motor (LIM) drive to compensate for the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping sliding-mode approach is designed to compensate the uncertainties occurring in the motion control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance of practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping sliding-mode control law is the result. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping sliding-mode controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

Adaptive integral robust control of hydraulic systems with asymptotic tracking

In this paper, an adaptive integral robust controller is developed for high accuracy motion tracking control of a double-rod hydraulic actuator. We take unknown constant parameters including the load and hydraulic parameters, and lumped unmodeled disturbances in inertia load dynamics and pressure dynamics into consideration. A discontinuous projection-based adaptive control law is constructed to handle parametric uncertainties, and an integral of the sign of the extended error based robust feedback term to attenuate unmodeled disturbances. Moreover, the present controller does not require a priori knowledge on the bounds of the lumped disturbances and the gain of the designed robust control law can be tuned itself. The major feature of the proposed full state controller is that it can theoretically guarantee global asymptotic tracking performance with a continuous control input, in the presence of various parametric uncertainties and unmodeled disturbances such as unmodeled dynamics as well as external disturbances via Lyapunov analysis. Comparative experimental results are obtained for motion control of a double-rod hydraulic actuator and verify the high-performance nature of the proposed control strategy.     

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     

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。     

超机动飞行快回路的自适应模糊滑模控制

针对超机动飞行快回路的不确定非线性模型,提出了一种利用自适应模糊滑模控制器算法。在所得的最终控制信号中,采用模糊逻辑系统来逼近未知系统函数和开关项;所设计的鲁棒自适应律用来减小逼近误差,从而有效降低抖振。仿真结果表明,所设计的控制律能在过失速机动条件下控制飞机跟踪指令飞行,确保系统具有良好的动态和稳态性能,而且控制器具有很强的鲁棒性。     

高超声速飞行器递阶滑模控制研究

针对高超声速飞行器轨迹高度和速度跟踪控制问题,基于纵向动力学的输入/输出线性化模型,设计了递阶滑模控制器和非线性扰动观测器,用于解决系统存在不确定性问题和执行机构带有死区非线性问题,对于所设计的控制器和观测器进行了稳定性分析,并且通过仿真验证了本文提出的方法能够提高系统的收敛速度和收敛精度并能克服执行机构死区的影响。     

Fuzzy Sliding-Mode Control of Active Suspensions

In this paper, a robust fuzzy sliding-mode controller for active suspensions of a nonlinear half-car model is introduced. First, a nonchattering sliding-mode control is presented. Then, this control method is combined with a single-input–single-output fuzzy logic controller to improve its performance. The negative value of the ratio between the derivative of error and error is the input and the slope constant of the sliding surface of the nonchattering sliding-mode controller is the output of the fuzzy logic controller. Afterwards, a four-degree-of-freedom nonlinear half-car model, which allows wheel hops and includes a suspension system with nonlinear spring and piecewise linear damper with dry friction, is presented. The designed controllers are applied to this model in order to evaluate their performances. It has been shown that the designed controller does not cause any problem in suspension working limits. The robustness of the proposed controller is also investigated for different vehicle parameters. The results indicate the success of the proposed fuzzy sliding-mode controller.      

Control of articulated robot arm with sensory feedback: laser beamtracking system

A method for the trajectory tracking control of an articulated robot arm using sensory feedback is presented. First, a general control algorithm for such a problem is presented. To implement sensory feedback effectively, the dynamics of a robot arm is described in the task coordinate system. Then the dynamics of the robot arm in the task coordinate system are linearized using nonlinear feedback. Because the linearization cannot be done completely because of variations and identification errors of the physical parameters of a robot arm, a robust controller is designed so that the effect of parameter variations and errors can be lessened. The control law is shown to be simplified by the use of high-gain feedback. The simplification can make the implementation of the control law very easy. The proposed algorithm is applied to the trajectory-tracking control of an articulated robot arm using a laser beam. The experiments show that the proposed algorithm works well for such a sensory feedback system     

Hybrid Terminal Sliding-Mode Observer Design Method for a Permanent-Magnet Synchronous Motor Control System

This paper proposes a hybrid terminal sliding-mode observer based on the nonsingular terminal sliding-mode (NTSM) and the high-order sliding-mode (HOSM) for the rotor position and speed estimation in the permanent-magnet synchronous motor control system. An NTSM manifold is utilized to realize both fast convergence and better tracking precision. In addition, a derivative estimator is used to obtain the derivative of the sliding-mode function. Meanwhile, an HOSM control law is designed to guarantee the stability of the observer and eliminate the chattering, so that smooth back-electromotive-force (EMF) signals can be obtained without a low-pass filter. According to the back-EMF equations, the rotor position and speed of the motor can be calculated. Simulation and experimental results are presented to validate the proposed method.