Intelligent Integral Backstepping Sliding‐mode Control Using Recurrent Neural Network For Piezo‐flexural Nanopositioning Stage

In this study, an intelligent integral backstepping sliding‐mode control (IIBSMC) system using a recurrent neural network (RNN) is proposed for the three‐dimensional motion control of a piezo‐flexural nanopositioning stage (PFNS). First, the dynamic model of the PFNS is derived. Then, an integral backstepping sliding‐mode control (IBSMC) system is proposed for the tracking of the reference contours. The steady‐state response of the control system can be improved effectively due to the addition of the integrator in the IBSMC. Moreover, to relax the requirements of the bound and discard the switching function in the IBSMC, an IIBSMC system using an RNN estimator is proposed to improve the control performance and the robustness of the PFNS. The RNN estimator is proposed to estimate the lumped uncertainty, including the system parameters and external disturbance, online. Furthermore, the online tuning law for the training of the parameters of the RNN is derived using the Lyapunov stability theorem. In addition, a robust compensator is proposed to confront the minimum reconstructed error occurring in the IIBSMC system. Finally, some experimental results for the tracking of various contours are given to demonstrate the validity of the proposed IIBSMC system. From the performance measurements of the proportional‐integral control, sliding mode control, IBSMC, and IIBSMC systems, the proposed IIBSMC system has the lowest maximum, average, and standard deviation of the position tracking errors for three‐dimensional motion control of the PFNS.     

Sliding‐Mode Velocity Control of a Two‐Wheeled Self‐Balancing Vehicle

Underactuated vehicles are those in which the number of control inputs is less than the degrees of freedom to be controlled. Using actuated wheels, velocity control of the two‐wheeled self‐balancing vehicle drives the vehicle at a desired speed and balances the body of the vehicle. First, we investigate the effects of friction on the wheel and derive the hybrid model of rolling and slipping. Second, we propose a nonlinear sliding mode velocity control scheme for the pure rolling model of the two‐wheeled vehicle. We present the design of the corresponding sliding surfaces and internal dynamics of the two‐wheeled vehicle. Our stability analysis reveals that the proposed sliding mode method can guarantee the asymptotic stability of the error dynamics for velocity control of the underactuated vehicle. Compared to linear optimal control, our numerical simulations demonstrate that the proposed sliding mode schemes can effectively control the velocity under the circumstances of parametric variations, emergency braking, and rapid acceleration in slippery road conditions. The proposed velocity control and the simulation improve our understanding on designing velocity control of the two‐wheeled self‐balancing vehicle.     

Dynamic Modeling and Tracking Control of a Nonholonomic Wheeled Mobile Manipulator with Dual Arms

This paper presents methodologies for dynamic modeling and trajectory tracking of a nonholonomic wheeled mobile manipulator (WMM) with dual arms. The complete dynamic model of such a manipulator is easily established using the Lagrange’s equation and MATHEMATICA. The structural properties of the overall system along with its subsystems are also well investigated and then exploited in further controller synthesis. The derived model is shown valid by reducing it to agree well with the mobile platform model. In order to solve the path tracking control problem of the wheeled mobile manipulator, a novel kinematic control scheme is proposed to deal with the nonholonomic constraints. With the backstepping technique and the filtered-error method, the nonlinear tracking control laws for the mobile manipulator system are constructed based on the Lyapunov stability theory. The proposed control scheme not only achieves simultaneous trajectory and velocity tracking, but also compensates for the dynamic interactions caused by the motions of the mobile platform and the two onboard manipulators. Simulation results are performed to illustrate the efficacy of the proposed control strategy.     

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs) is subject to nonholonomic constraints, system uncertainties, and external disturbances. This paper proposes a barrier function-based adaptive sliding mode control(BFASMC) method to provide high-precision, fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a pre...     

Sliding Mode Adaptive Neural-Network Control for Nonholonomic Mobile Modular Manipulators

A general mobile modular manipulator can be defined as a m-wheeled holonomic/nonholonomic mobile platform combining with a n-degree of freedom modular manipulator. This paper presents a sliding mode adaptive neural-network controller for trajectory following of nonholonomic mobile modular manipulators in task space. Dynamic model for the entire mobile modular manipulator is established in consideration of nonholonomic constraints and the interactive motions between the mobile platform and the onboard modular manipulator. Multilayered perceptrons (MLP) are used as estimators to approximate the dynamic model of the mobile modular manipulator. Sliding mode control and direct adaptive technique are combined together to suppress bounded disturbances and modeling errors caused by parameter uncertainties. Simulations are performed to demonstrate that the dynamic modeling method is valid and the controller design algorithm is effective.     

Toward The Implementation Of A Seek‐Controller For An Optical Pick‐Up Head Using A Discrete Sliding‐Mode Control Scheme

Chattering is inevitable in most sliding‐mode controllers due to finite and imperfect switching. Most of the discrete‐time sliding‐mode schemes reported to date fail to alleviate chattering effectively; even under perturbation‐free conditions, chattering is still inevitable. This present work proposes a scheme that can guarantee both one‐sided behavior and chattering‐free performance with uncertainties of the known bounds of the plant. This scheme is applied to the design of a seek‐controller for an optical pick‐up head to illustrate its feasibility. Both simulation and experimental studies were performed to further validate its effectiveness.     

基于神经网络的机器人二阶滑模控制

本文提出了一种基于神经网络与二阶滑模控制融合的控制策略用于非线性机器人控制,设计了一种新颖简易的二阶滑模控制方法,有效地避免了常规变结构控制的抖震问题,并采用神经网络辨识未知的机器人的非线性模型,通过Lyapunov直接法设计网络的权值更新率,确保了系统闭环全局渐近稳定性。最后,通过仿真验证了算法的有效性。     

Robust tracking control of a unicycle-type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network

This article presents a robust tracking controller for an uncertain mobile manipulator system. A rigid robotic arm is mounted on a wheeled mobile platform whose motion is subject to nonholonomic constraints. The sliding mode control (SMC) method is associated with the fuzzy neural network (FNN) to constitute a robust control scheme to cope with three types of system uncertainties; namely, external disturbances, modelling errors, and strong couplings in between the mobile platform and the onboard arm subsystems. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that the tracking error dynamics and the FNN weighting updates are ensured to be stable with uniform ultimate boundedness (UUB).     

机械臂卷积神经网络滑模轨迹跟踪控制

针对工业技术的发展对于多关节机械臂的精度与快速控制高要求,提出了一种机械臂卷积神经网络滑模轨迹跟踪控制方法。分析机械臂动力学方程,提取其中的不确定部分,针对不确定部分,构建深度卷积神经网络对其进行补偿,将补偿部分引入到滑模控制律中,通过改进后的滑模控制实现对机械臂轨迹跟踪的精确控制,并通过构建Lyapunov函数论证了系统的稳定性。仿真结果显示该方法能够满足轨迹跟踪要求,且减小了抖振现象。通过与其余三种典型控制方法的对比,测试结果表明,该方法加快了轨迹跟踪误差的收敛,且跟踪精度有了明显的提高。     

Discrete Sliding‐Mode Control of Mimo Linear Systems

This paper introduces a discrete sliding‐mode control for linear time‐invariant systems in the presence of matched uncertainties. The switching surface is constructed by applying the pole‐assignment method applying to the overall system, not the system in the sliding mode. Importantly, the control algorithm is derived to handle the rate of convergence to the sliding mode and to prevent the control law from encountering any unreasonably large variations. As for the system stability, it can be found that the system is stabilized and finally restricted to a known region. A numerical example is also included to demonstrate all the features of the developed discrete sliding‐mode control.     

Robust Tracking Control for Self-balancing Mobile Robots Using Disturbance Observer

In this paper, a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances. A desired velocity control law is firstly designed using the Lyapunov analysis method and the arctan function. To improve the tracking control performance, a nonlinear disturbance observer is developed to estimate the unknown disturbance of the self-balancing mobile robot. Using the output of the designed disturbance observer, the robust tracking control scheme is presented employing the sliding mode method for the selfbalancing mobile robot. Numerical simulation results further demonstrate the effectiveness of the proposed robust tracking control scheme for the self-balancing mobile robot subject to external unknown disturbances.      

神经网络滑模变结构控制研究综述

综述了近年来将人工神经网络控制和滑模变结构控制相结合的研究工作．从神经网络在滑模变结构控制中不同的应用方式出发，论述了神经网络在提高和改善滑模变结构系统性能方面的理论和方法，分析了它们各自的特点与相互之间的联系．最后展望了该领域未来的研究方向．     

Intelligent tracking control of a dual‐arm wheeled mobile manipulator with dynamic uncertainties

This paper presents an intelligent control approach that incorporates sliding mode control (SMC) and fuzzy neural network (FNN) into the implementation of back‐stepping control for a path tracking problem of a dual‐arm wheeled mobile manipulator subject to dynamic uncertainties and nonholonomic constraints. By using the back‐stepping technique, the system equations are reformulated into two levels: the kinematic level and the dynamic level. A sliding manifold is constructed by considering the disturbance free kinematic level equations only. With all the system uncertainties concentrated in the dynamic level, an FNN controller associated with a switching type of control law is employed to enforce sliding mode on the prescribed manifold. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that uniform ultimate boundedness for both the tracking error and the FNN weighting updates is ensured. A simulation study, which compares different control design approaches, is included to illustrate the promise of the proposed SMC–FNN method. Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive Chattering Free Neural Network Based Sliding Mode Control for Trajectory Tracking of Redundant Parallel Manipulators

In this paper, an adaptive chattering free neural network‐based sliding mode control (ACFN‐SMC) method is proposed for tracking trajectories of redundant parallel manipulators. ACFN‐SMC combines adaptive chattering free radial basis function neural networks (RBFN), sliding mode control with online updating the robust term parameters, and a nonlinear compensation item for reducing tracking errors. The stability of the closed‐loop system with modeling uncertainties, frictional uncertainties, and external disturbances is ensured by using the Lyapunov method. The proposed controller has a simple structure and little computation time while securing dynamic performance with expected quality in tracking trajectories of redundant parallel manipulators. In addition, the ACFN‐SMC strategy does not need to know the upper bound of any uncertainties. From the simulation results, it is evident that the proposed control strategy not only has significantly higher robustness capability for uncertainties but also can achieve better chattering elimination when compared with those using existing intelligent control schemes.     

Robust Control of a Two-Link Flexible Manipulator with Quasi-Static Deflection Compensation Using Neural Networks

A robust control method of a two-link flexible manipulator with neural networks based quasi-static distortion compensation is proposed and experimentally investigated. The dynamics equation of the flexible manipulator is divided into a slow subsystem and a fast subsystem based on the assumed mode method and singular perturbation theory. A decomposition based robust controller is proposed with respect to the slow subsystem, and H ^∞ control is applied to the fast subsystem. The overall closed-loop control is determined by the composite algorithm that combines the two control laws. Furthermore, a neural network compensation scheme is also integrated into the control system to compensate for quasi-static deflection. The proposed control method has been implemented on a two-link flexible manipulator for precise end-tip tracking control. Experimental results are presented in this paper along with concluding remarks.     

Full‐Order Sliding‐Mode Control of Rigid Robotic Manipulators

This paper proposes a full‐order sliding‐mode control for rigid robotic manipulators. The output signals of the proposed controller are continuous. Therefore, the controller can be directly applied in practice. A time‐varying gain is constructed to regulate the gain of the signum function in the sliding‐mode control so as to avoid the overestimation of the upper‐bounds of the uncertainties in the systems and reduce the waste of the control power. The chattering is attenuated by using a novel full‐order sliding manifold and establishing a novel ideal sliding motion. The proposed method is robust to the load disturbance and unmodeled parameters, especially to the unknown portion in the control matrix. Simulation results validate the proposed methods.     

动态滑模控制及其在移动机器人输出跟踪中的应用

针对轮式移动机器人的输出跟踪问题,提出一种动态滑模控制方法,首先给出机器人的动力学简化模型,然后将其分解成两个低阶子系统,并给出其输出跟踪的动态滑模控制器设计方法,仿真试验表明该方法能明显地削弱滑模控制系统的抖振。     

Robust Actuator‐Fault‐Tolerant Control System Based on Sliding‐Mode Observer for Thrust‐Vectoring Aircrafts

In this paper, a robust actuator‐fault‐tolerant control (FTC) system is proposed for thrust‐vectoring aircraft (TVA) control. To this end, a TVA model with actuator fault dynamics, disturbances, and uncertain aerodynamic parameters is described, and a local fault detection and identification (FDI) mechanism is proposed to locate and identify faults, which utilizes an adaptive sliding‐mode observer (SMO) to detect actuator faults and two SMOs to identify and estimate their parameters. Finally, a fault‐tolerant controller is designed to compensate for these actuator faults, disturbances, and uncertain aerodynamic parameters; the approach combines back‐stepping control with fault parameters and a high‐order SMO. Furthermore, the stability of the entire control system is validated, and simulation results are given to demonstrate the effectiveness and potential for this robust FTC system.     

Neural Network Based Terminal Sliding Mode Control for WMRs Affected by an Augmented Ground Friction With Slippage Effect

Wheeled mobile robots (WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly. To overcome this drawback, this article presents a neural network (NN) based terminal sliding mode control (TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance. In contrast to the existing friction models, the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously. Besides, the presented control approach can combine the merits of both TSMC and radial basis function (RBF) neural networks techniques, thereby providing numerous excellent performances for the closed-loop system, such as finite time convergence and faster friction estimation property. Simulation results validate the proposed friction model and robustness of controller; these research results will improve the autonomy and intelligence of WMRs, particularly when the mobile platform suffers from the sophisticated unstructured environment.      

Using the Sliding‐Mode PWM Method in an Anti‐Lock Braking System

This study attempted to integrate the Pulse Width Modulation (PWM) method and sliding mode control theory to develop quasi‐continuous control for an automobile anti‐lock braking system. Two controllers are designed in this study. One applies directly by applying quasi‐continuous control to achieve ABS slip control. In addition, the quasi‐continuous control method was applied to develop pressure tracking control, and then this pressure tracking controller and the acceleration signal of the tire were implemented together to construct an anti‐lock braking controller. Both controllers were investigated on a dynamic test stand. Wet road braking was simulated by spraying water on the contact surface between the tire and the flywheel. Excellent braking results not only verify the performance of the sliding PWM method but also provide an alternative to an ABS controller without slip feedback.