Robust fuzzy tracking control for robotic manipulators

In this paper, a stable adaptive fuzzy-based tracking control is developed for robot systems with parameter uncertainties and external disturbance. First, a fuzzy logic system is introduced to approximate the unknown robotic dynamics by using adaptive algorithm. Next, the effect of system uncertainties and external disturbance is removed by employing an integral sliding mode control algorithm. Consequently, a hybrid fuzzy adaptive robust controller is developed such that the resulting closed-loop robot system is stable and the trajectory tracking performance is guaranteed. The proposed controller is appropriate for the robust tracking of robotic systems with system uncertainties. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator.     

Nonlinear robust adaptive Cartesian impedance control of UAVs equipped with a robot manipulator

In this paper, a new nonlinear robust adaptive impedance controller is addressed for Unmanned Aerial Vehicles (UAVs) equipped with a robot manipulator that physically interacts with environment. A UAV equipped with a robot manipulator is a novel system that can perform different tasks instead of human being in dangerous and/or inaccessible environments. The objective of the proposed robust adaptive controller is control of the UAV and its robotic manipulator’s end-effector impedance in Cartesian space in order to have a stable physical interaction with environment. The proposed controller is robust against parametric uncertainties in the nonlinear dynamics model of the UAV and the robot manipulator. Moreover, the controller has robustness against the bounded force sensor inaccuracies and bounded unstructured modeling (nonparametric) uncertainties and/or disturbances in the system. Tracking performance and stability of the system are proved via Lyapunov stability theorem. Using simulations on a quadrotor UAV equipped with a three-DOF robot manipulator, the effectiveness of the proposed robust adaptive impedance controller is investigated in the presence of the force sensor error, and parametric and non-parametric uncertainties.     

Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks

This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator     

Rotorcraft with a 3DOF Rigid Manipulator: Quaternion-based Modeling and Real-time Control Tolerant to Multi-body Couplings

This paper proposes a simple solution for the stabilization of a mini-quadcopter carrying a 3DoF (degrees of freedom) manipulator robot in order to enhance its achievable workspace and application profile. Since the motion of the arm induces torques which degrade the stability of the system, in the present work, we consider the stabilization of both subsystems: the quadcopter and the robotic arm. The mathematical model of the system is based on quaternions. Likewise, an attitude control law consisting of a bounded quaternion-based feedback stabilizes the quadcopter to a desired attitude while the arm is evolving. The next stage is the translational dynamics which is simplified for control (nonlinear) design purposes. The aforementioned controllers are based on saturation functions whose stability is explicitly proved in the Lyapunov sense. Finally, experimental results and a statistical study validate the proposed control strategy.     

作业型飞行机器人抓取后重心偏移的轨迹跟踪控制

作业型飞行机器人是指能够对环境施加主动影响的飞行机器人, 它通常由旋翼飞行器与机械臂组合而成. 本文针对作业型飞行机器人在动态飞行抓取后, 重心位置变化产生的系统控制难题, 设计了有效的跟踪控制策略. 首先, 在系统建模时引入重心偏移系统参数和重心偏移控制参数, 并考虑惯性张量不为常数, 提高了系统建模的精度. 然后, 在姿态解算时, 考虑重心偏移对系统性能的影响, 构建包含重心偏移系统参数的解算方法, 得到更高精度的期望翻滚角和期望俯仰角. 接着, 设计了基于滑模控制的重心偏移补偿位置控制器, 实现了有效的位置跟踪控制. 同时, 在姿态反演控制器的基础上, 加入自适应律估计重心偏移控制参数和变化的惯性张量, 再通过小脑神经网络逼近惯性张量的真实值, 提高姿态控制器的精度. 最后, 给出了所设计控制器的稳定性证明, 并在仿真环境下验证了所提出的方法的有效性和优越性.     

Adaptive tracking with external force disturbance rejection for uncertain robotic systems

This paper is concerned with the tracking control problem of robotic systems perturbed by time-varying parameters, unmodelled dynamics and external force (and moment) disturbances. The upper bound of system uncertainties and disturbances is not required for controller design. Also, no limitations are assumed on the speed of variation and the magnitude of unknown parameters and perturbations. An adaptive algorithm with simplicity and universality properties is proposed to ensure robust tracking. Presenting the closed loop stability proof analytically, the tracking controller is applied to a two-link robot manipulator and the simulation results are demonstrated to show the effectiveness of the method.     

A precise robust fuzzy control of robots using voltage control strategy

Fuzzy control of robot manipulators with a decentralized structure is facing a serious challenge. The state-space model of a robotic system including the robot manipulator and motors is in non-companion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. Considering the problem, causes and solutions, we use voltage control strategy and convergence analysis to design a novel precise robust fuzzy control (PRFC) approach for electrically driven robot manipulators. The proposed fuzzy controller is Mamdani type and has a decentralized structure with guaranteed stability. In order to obtain a precise response, we regulate a fuzzy rule which governs the origin of the tracking space. The proposed design is verified by stability analysis. Simulations illustrate the superiority of the PRFC over a proprotional derivative like (PD-like) fuzzy controller applied on a selective compliant assembly robot arm (SCARA) driven by permanent magnet DC motors.     

基于Lagrangian支持向量机的机械手鲁棒自适应控制

提出了一种基于Lagrangian支持向量机的不确定机械手鲁棒自适应控制方法。Lagrangian支持向量机采用梯度投影法学习机械手系统的未知部分,来对机械手系统进行非线性补偿。根据Lyapunov稳定性理论设计自适应律进一步在线调整支持向量机的参数,并叠加一个滑模控制项,以保证控制系统的稳定性和鲁棒性。对两关节机械手的仿真结果证明了以上控制方法的有效性。     

基于不确定逼近的机械手自适应鲁棒预测控制

针对具有参数不确定性和未知外部干扰的机械手轨迹跟踪问题提出了一种多输入多输出自适应鲁棒预测控制方法. 首先根据机械手模型设计非线性鲁棒预测控制律, 并在控制律中引入监督控制项; 然后利用函数逼近的方法逼近控制律中因模型不确定性以及外部干扰引起的未知项. 理论证明了所设计的控制律能够使机械手无静差跟踪期望的关节角轨迹. 仿真验证了本文设计方法的有效性.     

A Robust Tracking Controller for Electrically Driven Robot Manipulators: Stability Analysis and Experiment

 In this paper, a robust controller for electrically driven robotic systems is developed. The controller is designed in a backstepping manner. The main features of the controller are: 1) Control strategy is developed at the voltage level and can deal with both mechanical and electrical uncertainties. 2) The proposed control law removes the restriction of previous robust methods on the upper bound of system uncertainties. 3) It also benefits from global asymptotic stability in the Lyapunov sense. It is worth to mention that the proposed controller can be utilized for constrained and nonconstrained robotic systems. The effectiveness of the proposed controller is verified by simulations for a two link robot manipulator and a four-bar linkage. In addition to simulation results, experimental results on a two link serial manipulator are included to demonstrate the performance of the proposed controller in tracking a given trajectory.     

Trajectory tracking control for robot manipulators using only position measurements

In the paper, the trajectory tracking control problem is investigated for robotic manipulators which are not equipped with the tachometers. Our contribution consists in establishing uniform asymptotic stability in closed-loop system by using the dynamic position-feedback controller with feedforward. Using Lyapunov vector function and comparison principle, we construct the non-linear controller with variable gain matrices and first-order linear dynamic compensator such that the origin of the closed-loop system is uniformly asymptotically stable. The controller is shown to be robust with respect to parameters incertainties. We illustrate the utility of our result by simulation tests with reference to a two-link planar elbow robot manipulator.     

Robust tracking enhancement of robot systems including motordynamics: a fuzzy-based dynamic game approach

A robust tracking control design of robot systems including motor dynamics with parameter perturbation and external disturbance is proposed in this study via adaptive fuzzy cancellation technique. A minimax controller equipped with a fuzzy-based scheme is used to enhance the tracking performance in spite of system uncertainties and external disturbance. The design procedure is divided into three steps. At first, a linear nominal robotic control design is obtained via model reference tracking with desired eigenvalue assignment. Next, a fuzzy logic system is constructed and then tuned to eliminate the nonlinear uncertainties as possibly as it can to enhance the tracking robustness. Finally, a minimax control scheme is specified to optimally attenuate the worst-case effect of both the residue due to fuzzy cancellation and external disturbance to achieve a minimax tracking performance. In addition, an adaptive fuzzy-based dynamic game theory is introduced to solve the minimax tracking problem. The proposed method is appropriate for the robust tracking design of robotic systems with large parameter perturbation and external disturbance. A simulation example of a two-link robotic manipulator driven by DC motors is also given to demonstrate the effectiveness of proposed design method's tracking performance     

地面装调的空间机械臂在空间应用时的自适应鲁棒控制

针对空间机械臂在轨操控过程中,重力加速度不同于地面装调阶段的重力加速度,会随着空间位置的改变而变化的问题.本文提出了一种自适应鲁棒控制策略,用于空间机械臂的末端控制,从而使在地面重力条件下装调好的空间机械臂能够在空间微重力条件下实现在轨操控任务.通过分析重力项对空间机械臂轨迹跟踪控制的影响,设计自适应律在线估计重力加速度,从而得到重力项的估计,系统的不确定性通过鲁棒控制器来补偿.基于李雅普诺夫理论证明了闭环系统的稳定性.仿真结果表明,在地面装调阶段的重力环境下和空间应用阶段的微重力环境下,该控制器对空间机械臂的末端控制均能达到较高的轨迹跟踪精度,具有重要的工程应用价值.     

Observer-Based Adaptive Controller Design of Flexible Manipulators Using Time-Delay Neuro-Fuzzy Networks

In this paper, a dynamical time-delay neuro-fuzzy controller is proposed for the adaptive control of a flexible manipulator. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity. For a perfect tracking control of the robot, the output redefinition approach is used in the adaptive controller design using time-delay neuro-fuzzy networks. The time-delay neuro-fuzzy networks with the rule representation of the TSK type fuzzy system have better learning ability for complex dynamics as compared with existing neural networks. The novel control structure and learning algorithm are given, and a simulation for the trajectory tracking of a flexible manipulator illustrates the control performance of the proposed control approach.     

T-S 模糊模型变结构的机器臂轨迹跟踪控制

针对不确定性的机械臂轨迹跟踪问题,结合滑模变结构和T-S模糊模型的优点,给出一种基于T-S模糊模型的变结构轨迹跟踪的方法。首先采用T-S模型建模,得到机械臂的模糊模型;然后设计出保证机械臂全局渐近稳定的滑模控制器。仿真结果表明,所设计的模糊变结构控制器与普通变结构控制器相比,可使机械臂无论在计算时间、误差上都具有更大的优势和更强的鲁棒性。     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.     

作业型飞行机器人动态滑翔抓取的鲁棒自适应控制

作业型飞行机器人是指将多自由度机械臂固连在飞行机器人上的一类新型机器人系统,它能够对周围环境施加主动影响,同时也存在较为复杂的动力学性能.本文针对作业型飞行机器人滑翔抓取物体时所受到的摩擦力和接触力问题以及在飞行过程中产生的转动惯量变化问题,设计了一种整体式鲁棒自适应控制策略.首先在作业型飞行机器人系统动力学建模中引入...     

基于细菌觅食算法的嵌入式机械臂角度控制器设计

进行机械臂角度控制器设计过程中,为提高机器人机械臂灵活性,降低关节角度控制误差,设计一种细菌觅食算法的嵌入式机械臂角度控制器。首先,构建机械臂动力学模型以获取机械臂的柔性特征及其关节位置,根据获取的信息确定角度控制器的硬件逻辑结构和算法。然后,使用ARM微处理器嵌入式操作系统,设计包含移动控制终端和机械臂控制端的控制器硬件结构。最后,采用细菌觅食算法优化控制器参数,并实现代码完成机器人机械臂角度的精准跟踪控制。仿真分析结果表明:所提方法具有较高的位姿跟踪精度、角度控制误差小、稳定性强,能够保证机械臂关节角度无超调,具有极高的机器工程应用价值。     

Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive.A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems.     

Robust adaptive control for mobile manipulators

This paper addresses the trajectory tracking control of a nonholonomic wheeled mobile manipulator with parameter uncertainties and disturbances. The proposed algorithm adopts a robust adaptive control strategy where parametric uncertainties are compensated by adaptive update techniques and the disturbances are suppressed. A kinematic controller is first designed to make the robot follow a desired end-effector and platform trajectories in task space coordinates simultaneously. Then, an adaptive control scheme is proposed, which ensures that the trajectories are accurately tracked even in the presence of external disturbances and uncertainties. The system stability and the convergence of tracking errors to zero are rigorously proven using Lyapunov theory. Simulations results are given to illustrate the effectiveness of the proposed robust adaptive control law in comparison with a sliding mode controller.