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.     

时滞的非完整动力学系统滑模抗干扰跟踪控制

针对具有外部扰动和时滞的非完整轮式移动机器人系统,本文阐述了一种基于非线性扰动观测器的时滞滑模控制方法.首先,利用扰动观测器估计系统的外部扰动;然后,用极坐标转化移动机器人的姿态,并用计算转矩法对机器人的动力学方程进行反馈线性化.设计带时滞控制的滑模,目的是使移动机器人渐近稳定在期望轨迹上,并有效地减小控制增益的过高估计.最后,利用李雅普诺夫函数建立闭环系统的稳定性.仿真结果表明,该方案具有良好的跟踪精度和鲁棒性.     

基于Sigmoid函数的机器人鲁棒滑模跟踪控制系统设计

为了保证机器人能够在保持稳定的情况下,按照规划轨迹执行工作任务,从硬件和软件两个方面,设计了基于Sigmoid函数的机器人鲁棒滑模跟踪控制系统。装设机器人传感器与状态观测器,改装机器人鲁棒滑模跟踪控制器,完成系统硬件设计;综合机器人结构、运动机理和动力机制3个方面,构建机器人数学模型;根据状态数据采集结果与规划轨迹之间的偏差,计算机器人跟踪控制量;依据滑模运动与切换方程,利用Sigmoid函数生成机器人鲁棒滑模控制律,将生成控制指令作用在机器人执行元件上,实现系统的鲁棒滑模跟踪控制功能;在系统测试与分析中,所设计控制系统的平均位置跟踪控制误差为0.93 mm,与设定轨迹目标基本重合,机器人姿态角跟踪控制误差为0.06 mm,具有较好的鲁棒滑模跟踪控制效果,能够有效提高机器人鲁棒滑模跟踪控制精度。     

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.     

A Neuro-Sliding Mode Control Scheme for Constrained Robots with Uncertain Jacobian

The joint robot control requires to map desired cartesian tasks into desired joint trajectories, by using the ill-posed inverse kinematics mapping. In order to avoid inverse kinematics, the control problem is formulated directly in task space to gives rise to cartesian robot control. In addition, when the robot is constrained due to its kinematic mappings yields a stiff system and an additional complexity arises to implement cartesian control for constrained robots. In this paper, an alternative approach is proposed to guarantee global convergence of force and position cartesian tracking errors under the assumption that the jacobian is not exactly known. A neuro-sliding mode controller is presented, where a small size adaptive neural network compensates approximately for the inverse dynamics and an inner control loop induces second order sliding modes to guarantee tracking. The sliding mode variable tunes the online adaptation of the weights. A passivity analysis yields the energy Lyapunov function to prove boundedness of all closed-loop signals and variable structure control theory is used to finally conclude convergence of position and force tracking errors. Experimental results are provided to visualize the expected performance.     

Multiple-arm space free-flying robots for manipulating objects with force tracking restrictions

Multiple Impedance Control (MIC) is an algorithm that enforces designated impedance at various levels, i.e. on the manipulated object, all cooperating manipulators, and the moving platform of a robotic system. In this paper, a force tracking strategy inspired by a human control system is added to the MIC algorithm and the general formulation is revised to fulfill a desired force tracking strategy for object manipulation tasks. The stability analysis of the MIC algorithm based on the Liapunov Direct Method, besides error analysis, shows that a good tracking of cooperative manipulators and the manipulated object is guaranteed. Next, using MAPLE and MATLAB tools, a system of three manipulators mounted on a space free-flying robot is simulated. The task is moving an object based on given trajectories which come across an obstacle, to examine the performance of the developed control law. The results show that, even in the presence of both external disturbances and an impact due to collision with the obstacle, the response of the MIC algorithm is smooth. Moreover, based on the embedded force tracking strategy, the contact force is confined to follow a desired trajectory. Also, it is shown that decreasing the values of the controller mass matrix elements results in reducing both the object position and force tracking error.     

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.      

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     

Sliding mode variable structure control for visual servoing system

A visual servoing tracking controller is proposed based on the sliding mode control theory in order to achieve strong robustness against parameter variations and external disturbances. A sliding plane with time delay compensation is presented by the pre-estimate of states. To reduce the chattering of the sliding mode controller, a modified exponential reaching law and hyperbolic tangent function are applied to the design of visual controller and robot joint controller. Simulation results show that the visual servoing control scheme is robust and has good tracking performance.     

基于径向基函数神经网络的机器人滑模控制

尽管滑模控制响应快,对系统参数和外部扰动呈不变性,但在保证系统的渐进稳定性上却存在很强的抖动缺点.因此,在一般滑模控制的基础上,引入了径向基函数神经网络(RBFNN).利用滑模控制的特点设定目标函数,将切换函数作为RBFNN的输入,滑模控制量作为其输出.利用RBF神经网络的在线学习功能,消除了控制的抖动,同时使系统具有很强的鲁棒性.对两连杆机械手进行了仿真研究,其结果表明,在存在模型误差和外部扰动的情况下,该方案既能达到高精度快速跟踪的目的,又能消除滑模控制的抖动问题.     

Sliding Mode Neuro Adaptive Control in Trajectory Tracking for Mobile Robots

In this work a neural indirect sliding mode control method for mobile robots is proposed. Due to the nonholonomic property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate the dynamics of the robot. Using an online adaptation scheme, a neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown nonlinear dynamics. The proposed design simultaneously guarantees the stability of the adaptation of the neural nets and obtains suitable equivalent control when the parameters of the robot model are unknown in advance. The robust adaptive scheme is applied to a mobile robot and shown to be able to guarantee that the output tracking error will converge to zero.     

基于遥感GIS信息融合的测绘机器人滑动模跟踪控制系统

测绘机器人是实现测绘自动化的执行设备,测绘机器人的工作空间更为复杂,给机器人的跟踪控制工作带来较大挑战。为提高测绘机器人跟踪控制效果,设计了基于遥感GIS信息融合的测绘机器人滑动模跟踪控制系统。加设遥感信息采集器和GIS信息采集器,改装遥感GIS信息处理器以及滑动模跟踪控制器,完成硬件系统的优化设计。考虑信息结构以及信息之间的逻辑关系,构建系统数据库,为遥感GIS信息提供充足的存储空间。根据测绘任务生成机器人滑动模移动轨迹,作为机器人的控制目标。采集测绘机器人实时遥感与GIS信息,利用遥感GIS信息融合技术跟踪机器人实时位姿,比对位姿跟踪结果与生成的控制目标,计算滑动模跟踪控制量,完成系统的测绘机器人滑动模跟踪控制软件功能优化。系统测试结果表明:设计系统的控制误差平均值为1.9 m,抖振幅值为0.8 dB,具有较好的控制效果。     

Robust chattering-free sliding mode control of space robot in task space

This paper studies the tracking control problem of a free-floating space robot in a task space. Considering the model uncertainties and external disturbance, a robust sliding mode controller is proposed using the Lyapunov direct method and dissipative theory. To eliminate the chattering phenomenon, an radial basis function （RBF） neural network is applied to replace the discontinuous part of the control signal. A novel on-line learning method of the weights and parameters of the RBF neural network established using Lyapunov function assures the stability of the system. It is proved that the proposed controller can guarantee that the L2 gain from disturbance to tracking error is lower than the given index y. Simulation results show that the control method is valid.     

空间机器人抓捕目标后基于任务相容性的消旋策略

针对双臂空间机器人抓捕自旋目标后的镇定操作,在考虑机器人系统输入约束的条件下,提出了一种基于任务相容性的消旋规划与控制方法。首先,给出空间机器人抓捕目标后的组合系统的动力学模型,作为规划与控制的基础。然后,根据动力学可操作度和任务相容性设计了目标的快速消旋策略,其期望加速度的方向和大小分别取作速度的反方向和机器人系统输入约束允许的最大值。最后,基于所推导的运动学和动力学模型,通过对目标和机械臂末端分别建立柔顺度等式,提出了一种跟踪期望运动轨迹同时对末端接触力进行调节的柔顺控制方法。通过双臂7自由度空间机器人消除目标自旋运动的仿真结果,验证了所提方法的有效性。     

Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators

The ever increasingly stringent performance requirements of industrial robotic applications highlight significant importance of advanced robust control designs for serial robots that are generally subject to various uncertainties and external disturbances. Therefore, this paper proposes and investigates the design and implementation of a robust adaptive fuzzy sliding mode controller in the task space for uncertain serial robotic manipulators. The sliding mode control is well known for its robustness to system parameter variations and external disturbances, and is thus a highly desirable and cost-effective approach to achieve high precision control task for serial robots. The proposed controller is designed based on a fuzzy logic approximation to accomplish trajectory tracking with high accuracy and simultaneously attenuate effects from uncertainties. In the controller, the high-frequency uncertain term is approximated by using a fuzzy logic system while the low-frequency term is adaptively updated in real time based on a parametric adaption law. The control efficacy and effectiveness of the proposed control algorithm are comparatively verified against a recently proposed conventional controller. The test results demonstrate that the proposed controller has better trajectory tracking performances and is more robust against large disturbances than the conventional controller under the same operating conditions.     

Adaptive Sliding Mode Control for Re-entry Attitude of Near Space Hypersonic Vehicle Based on Backstepping Design

Combining sliding mode control method with radial basis function neural network (RBFNN), this paper proposes a robust adaptive control scheme based on backstepping design for re-entry attitude tracking control of near space hypersonic vehicle (NSHV) in the presence of parameter variations and external disturbances. In the attitude angle loop, a robust adaptive virtual control law is designed by using the adaptive method to estimate the unknown upper bound of the compound uncertainties. In the angular velocity loop, an adaptive sliding mode control law is designed to suppress the effect of parameter variations and external disturbances. The main benefit of the sliding mode control is robustness to parameter variations and external disturbances. To further improve the control performance, RBFNNs are introduced to approximate the compound uncertainties in the attitude angle loop and angular velocity loop, respectively. Based on Lyapunov stability theory, the tracking errors are shown to be asymptotically stable. Simulation results show that the proposed control system attains a satisfied control performance and is robust against parameter variations and external disturbances.      

Robust Fuzzy Output Sliding Control without the Requirement of State Measurement

A robust fuzzy output sliding control for nonlinear robotic arms is proposed in this paper. The proposed method not only retains the advantages of the conventional sliding mode control such as robustness against parameter variations and external disturbances, but also uses measurable output signals to define the sliding surface function. A fuzzy controller is developed to modify the control law to avoid state measurement. Control system stability is proved by using the Lyapunov stability theorem. The system robustness is guaranteed. Simulations results demonstrate the validity and effectiveness of the proposed method for uncertain nonlinear robotic arms.     

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     

A Robust Adaptive Terminal Sliding Mode Control for Rigid Robotic Manipulators

In this paper, a robust adaptive terminal sliding mode controller is developed for n-link rigid robotic manipulators with uncertain dynamics. An MIMO terminal sliding mode is defined for the error dynamics of a closed loop robot control system, and an adaptive mechanism is introduced to estimate the unknown parameters of the upper bounds of system uncertainties in the Lyapunov sense. The estimates are then used as controller parameters so that the effects of uncertain dynamics can be eliminated and a finite time error convergence in the terminal sliding mode can be guaranteed. Also, a useful bounded property of the derivative of the inertial matrix is explored, the convergence rate of the terminal sliding variable vector is investigated, and an experiment using a five bar robotic manipulator is carried out in support of the proposed control scheme.     

Dynamic modelling and adaptive robust tracking control of a space robot with two-link flexible manipulators under unknown disturbances

In this paper, both the closed-form dynamics and adaptive robust tracking control of a space robot with two-link flexible manipulators under unknown disturbances are developed. The dynamic model of the system is described with assumed modes approach and Lagrangian method. The flexible manipulators are represented as Euler–Bernoulli beams. Based on singular perturbation technique, the displacements/joint angles and flexible modes are modelled as slow and fast variables, respectively. A sliding mode control is designed for trajectories tracking of the slow subsystem under unknown but bounded disturbances, and an adaptive sliding mode control is derived for slow subsystem under unknown slowly time-varying disturbances. An optimal linear quadratic regulator method is proposed for the fast subsystem to damp out the vibrations of the flexible manipulators. Theoretical analysis validates the stability of the proposed composite controller. Numerical simulation results demonstrate the performance of the closed-loop flexible space robot system.