Prosthetic Hand Finger Control Using Fuzzy Sliding Modes

In order to improve the life quality of amputees, providing approximate manipulation ability of a human hand to that of a prosthetic hand is considered by many researchers. In this study, a biomechanical model of the index finger of the human hand is developed based on the human anatomy. Since the activation of finger bones are carried out by tendons, a tendon configuration of the index finger is introduced and used in the model to imitate the human hand characteristics and functionality. Then, fuzzy sliding mode control where the slope of the sliding surface is tuned by a fuzzy logic unit is proposed and applied to have the finger model to follow a certain trajectory. The trajectory of the finger model, which mimics the motion characteristics of the human hand, is pre-determined from the camera images of a real hand during closing and opening motion. Also, in order to check the robust behaviour of the controller, an unexpected joint friction is induced on the prosthetic finger on its way. Finally, the resultant prosthetic finger motion and the tendon forces produced are given and results are discussed.     

Design of a sliding mode controller for an automatic guided vehicle and its implementation

In this paper, a control scheme that combines a kinematic controller and a sliding mode dynamic controller with external disturbances is proposed for an automatic guided vehicle to track a desired trajectory with a specified constant velocity. It provides a method of taking into account specific mobile robot dynamics to convert desired velocity control inputs into torques for the actual mobile robot. First, velocity control inputs are designed for the kinematic controller to make the tracking error vector asymptotically stable. Then, a sliding mode dynamic controller is designed such that the mobile robot’s velocities converge to the velocity control inputs. The control law is obtained based on the backstepping technique. System stability is proved using the Lyapunov stability theory. In addition, a scheme for measuring the errors using a USB camera is described. The simulation and experimental results are presented to illustrate the effectiveness of the proposed controller.     

Smooth trajectory tracking of three-link robot: a self-organizingCMAC approach

A neuro fuzzy system which is embedded in the conventional control theory is proposed to tackle physical learning control problems. The control scheme is composed of two elements. The first element, the fuzzy sliding mode controller (FSMC), is used to drive the state variables to a specific switching hyperplane or a desired trajectory. The second one is developed based on the concept of the self organizing fuzzy cerebellar model articulation controller (FCMAC) and adaptive heuristic critic (AHC). Both compose a forward compensator to reduce the chattering effect or cancel the influence of system uncertainties. A geometrical explanation on how the FCMAC algorithm works is provided and some refined procedures of the AHC are presented as well. Simulations on smooth motion of a three-link robot is given to illustrate the performance and applicability of the proposed control scheme.     

Design of a Sliding Mode Controller with Fuzzy Rules for a 4-DoF Service Robot

In this study, a novel control strategy that combines a fuzzy system and the sliding mode controller is proposed for improving stability and achieving high-accuracy control in service robots. Based on the kinematic and dynamic models of a 4-degrees of freedom manipulator, and the observed tracking error using a low-cost inertial sensor, the proposed fuzzy sliding mode controller (FSMC(IMU)) is designed to generate appropriate torques at robot joints. The FSMC(IMU) controller parameters are adjusted through a fuzzy rule that determines the state of the system. The error in trajectory tracking is reduced through this. The gain value K can be finely adjusted by fuzzy control by observing the degree of vibration after entering the sliding mode surface. The larger the observed vibration value, the faster the fuzzy controller follows the given input trajectory by selecting a smaller gain value K and reducing jitter due to the sliding mode control’s discontinuous switch characteristics. When the degree of error is small, it achieves faster and more accurate control performance than when the observer is not used. The stability of the FSMC(IMU) system is verified via disturbance experiments. The experimental data are compared with the conventional sliding mode controller and proportional-derivative control. The experimental results demonstrate that the proposed FSMC(IMU) controller is stable, fast, and highly accurate in controlling service robots.

     

基于全身协调的仿人机器人步行稳定控制

提出利用机器人质心（CoM）雅克比矩阵,实现全身协调补偿的算法。提出机器人的简化模型;分析基于CoM雅克比矩阵的补偿算法;采用CoM/ZMP（零点矩点）、减振和软着陆控制器实时控制双足步行,实现机器人全身协调的稳定控制;通过仿人机器人AFU09的双足步行实验证明该控制方法的有效性。     

仿人机器人复杂动作设计中人体运动数据提取及分析方法

提出了仿人机器人复杂动作设计中人体运动数据提取及分析方法. 首先, 通过运动捕捉系统获取人体运动数据, 并采用运动重定向技术, 输出人--机简化模型的数据; 然后, 对运动数据进行分析和运动学解算, 给出基于人体运动数据的仿人机器人逆运动学求解方法, 得到仿人机器人模型的关节角数据; 再经过运动学约束和稳定性调节后, 生成能够应用于仿人机器人的运动轨迹. 最终, 通过在仿人机器人BHR-2上进行刀术实验验证了该方法的有效性.     

一种球形机器人的非线性滑模运动控制

基于非线性滑模控制方法,对一种欠驱动的球形机器人的运动控制问题进行了研究．球形机器人的输入由两个相互正交的力矩组成．在非完整约束的条件下,分别建立球形机器人的运动学和动力学模型,并通过输入变换将动力学模型变换为一个两输入的二阶系统．基于非线性滑模控制方法分别设计了横向姿态控制器和纵向速度控制器,可以保证被控的运动状态收敛到期望的邻域内．仿真和实验结果验证了所建立的动力学模型和控制方法的有效性．     

基于Lyapunov方法和快速终端滑模的轨迹跟踪控制

针对移动机器人的运动学模型,提出一种具有全局渐近稳定性的跟踪控制器。该跟踪控制器的设计分为两部分：第一部分是采用全局快速终端滑动模态的思想设计了角速度的控制律,用来渐近镇定移动机器人跟踪的前向角误差;第二部分是采用Lyapunov方法设计了线速度的控制律,用来渐近镇定移动机器人跟踪的平面坐标误差。采用Lyapunov稳定性定理,证明了移动机器人在满足这些控制律条件下,实现了对参考轨迹的全局渐近跟踪。实验结果表明移动机器人能够有效地跟踪期望轨迹,有利于在实际应用中推广。     

空间三关节机器人模糊积分滑模控制

研究提高关节机器人轨迹跟踪控制的性能,由于关节机器人运动中产生振动,影响系统的稳定性能。为解决上述问题,提出了一种反馈线性化的自适应模糊积分滑模控制方法。在上述方法的基础上,对机器人非线性动力学模型反馈线性化。为了进一步提高滑模控制的精度,设计了一种积分滑模面的滑模控制器,可以减弱积分滑模控制的抖振。通过设计一个模糊控制器,根据积分滑模面的大小自适应地调节积分滑模控制的切换部分,达到削弱抖振的目的。利用李亚普诺夫定理证明了控制系统的稳定性。仿真结果表明,改进方法有效地提高了关节机器人跟踪控制性能。     

基于视觉图像的全向移动机器人轨迹跟踪控制研究

机器人轨迹节点跟踪比较难,导致机器人实际轨迹偏离期望轨迹,所以设计基于视觉图像的全向移动机器人轨迹跟踪控制方法;构建全向移动机器人的运动学数学模型,以此确定机器人移动轨迹数学模型;以移动轨迹数学模型为基础,按照视觉图像划分标准对全向移动机器人运动图像的分割,通过分离目标节点的方式提取运动学特征参量,完成机器人轨迹节点跟踪处理;结合节点跟踪处理结果,将运动学不等式与误差向量作为机器人轨迹跟踪控制的约束条件,利用滑模变结构搭建轨迹跟踪控制模型,实现全向移动机器人轨迹跟踪控制;对比实验结果表明,所设计的方法应用后,全向移动机器人角速度曲线、线速度曲线与期望运动轨迹曲线之间的贴合程度均超过90%,满足全向移动机器人轨迹跟踪控制要求。     

An adaptive technique for trajectory tracking control of a wheeled mobile robots without velocity measurements

This paper addresses an adaptive method for designing a sensorless trajectory tracking control scheme for a wheeled mobile robot. In order to reduce the cost of the robot, a new Nonlinear Observer (NOB) is used to leave out velocity sensors in the robot. Also, an adaptive model reference technique is used for designing the dynamic controller. In order to ensure the implementability of proposed controller, dynamic controller and nonlinear observer are designed in the presence of uncertainties. In addition, the Observer-based Kinematic Controller (OKC) is designed in the presence of sliding velocity. In order to improve the performance of the kinematic controller, sliding velocity is estimated and used for modification of kinematic controller. Finally, the effectiveness of the proposed method is demonstrated by simulations.     

Coordination and Control of Multi-fingered Robot Hands with Rolling and Sliding Contacts

In this paper, the problem of controlling multi-fingered robot hands with rolling and sliding contacts is addressed. Several issues are explored. These issues involve the kinematic analysis and modeling, the dynamic analysis and control, and the coordination of a multi-fingered robot hand system. Based on a hand-object system in which the contacts are allowed to both roll and slide, a kinematic model is derived and analyzed. Also, the dynamic model of the hand-object system with relative motion contacts is studied. A control law is proposed to guarantee the asymptotic tracking of the object trajectory together with the desired rolling and/or sliding motions along the surface of the object. A planning approach is then introduced to minimize the contact forces so that the desired motion of the object and the relative motions between the fingers and the object can be achieved. Simulation results which support the theoretical development are presented.     

Posture/Walking Control for Humanoid Robot Based on Kinematic Resolution of CoM Jacobian With Embedded Motion

This paper proposes the walking pattern generation method, the kinematic resolution method of center of mass (CoM) Jacobian with embedded motions, and the design method of posture/walking controller for humanoid robots. First, the walking pattern is generated using the simplified model for bipedal robot. Second, the kinematic resolution of CoM Jacobian with embedded motions makes a humanoid robot balanced automatically during movement of all other limbs. Actually, it offers an ability of whole body coordination to humanoid robot. Third, the posture/walking controller is completed by adding the CoM controller minus the zero moment point controller to the suggested kinematic resolution method. We prove that the proposed posture/walking controller brings the disturbance input-to-state stability for the simplified bipedal walking robot model. Finally, the effectiveness of the suggested posture/walking control method is shown through experiments with regard to the arm dancing and walking of humanoid robot.     

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.     

Learning to Walk by Imitation in Low-Dimensional Subspaces

In this paper, we provide the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data without any prior information of its dynamics model. Programming a humanoid robot to perform an action (such as walking) that takes into account the robot's complex dynamics is a challenging problem. Traditional approaches typically require highly accurate prior knowledge of the robot's dynamics and environment in order to devise complex (and often brittle) control algorithms for generating a stable dynamic motion. Training using human mocap is an intuitive and flexible approach to programming a robot, but direct usage of mocap data usually results in dynamically unstable motion. Furthermore, optimization using high-dimensional mocap data in the humanoid full-body joint space is typically intractable. We propose a new approach to tractable imitation-based learning in humanoids without a robot's dynamic model. We represent kinematic information from human mocap in a low-dimensional subspace and map motor commands in this low-dimensional space to sensory feedback to learn a predictive dynamic model. This model is used within an optimization framework to estimate optimal motor commands that satisfy the initial kinematic constraints as best as possible while generating dynamically stable motion. We demonstrate the viability of our approach by providing examples of dynamically stable walking learned from mocap data using both a simulator and a real humanoid robot.     

五指形仿人机械手的数学模型研究

以人手的解剖学研究成果为基础，对具有五个手指和手掌的仿人机械手（以下简称仿人机械手）的数学模型进行研究。首先，以现有的工业机器人研究成果为基础，提出了仿人机械手的坐标系建立方法。然后，从人手的解剖学特点出发，采用D—H变换矩阵建立了仿人机械手运动学模型。     

Humanoids skill learning based on real-time human motion imitation using Kinect

In this paper, a novel framework which enables humanoid robots to learn new skills from demonstration is proposed. The proposed framework makes use of real-time human motion imitation module as a demonstration interface for providing the desired motion to the learning module in an efficient and user-friendly way. This interface overcomes many problems of the currently used interfaces like direct motion recording, kinesthetic teaching, and immersive teleoperation. This method gives the human demonstrator the ability to control almost all body parts of the humanoid robot in real time (including hand shape and orientation which are essential to perform object grasping). The humanoid robot is controlled remotely and without using any sophisticated haptic devices, where it depends only on an inexpensive Kinect sensor and two additional force sensors. To the best of our knowledge, this is the first time for Kinect sensor to be used in estimating hand shape and orientation for object grasping within the field of real-time human motion imitation. Then, the observed motions are projected onto a latent space using Gaussian process latent variable model to extract the relevant features. These relevant features are then used to train regression models through the variational heteroscedastic Gaussian process regression algorithm which is proved to be a very accurate and very fast regression algorithm. Our proposed framework is validated using different activities concerned with both human upper and lower body parts and object grasping also.     

Formation control of multiple mecanum-wheeled mobile robots with physical constraints and uncertainties

Aiming at the formation control of multiple Mecanum-wheeled mobile robots (MWMRs) with physical constraints and model uncertainties, a novel robust control scheme that combines model predictive control (MPC) and extended state observer-based adaptive sliding mode control (ESO-ASMC) is proposed in this paper. First, a linear MPC strategy is proposed to address the motion constraints of MWMRs, which can transform the robot formation model based on leader-follower into a constrained quadratic programming (QP) problem. The QP problem can be solved iteratively online by a delay neural network (DNN) to obtain the optimal control velocity of the follower robot. Then, to address the input saturation constraints, model uncertainties and unknown disturbances in the dynamic model, an improved ESO-ASMC is proposed and compared with the robust adaptive terminal sliding mode control (RATSMC) and the conventional sliding mode control (SMC) to prove the effectiveness. The proposed scheme, considering the optimal control velocity obtained by the kinematics controller as the given desired velocity of the dynamics controller, can implement precise formation control, while solving various physical constraints of the robot, and eliminating the effects of model uncertainties and disturbances. Finally, through a comparative simulation case, the effectiveness and robustness of the proposed method are verified.

     

SCARA机器人内模控制方法

为了使SCARA机器人在外界干扰和模型不精确的情况下具有优良的轨迹跟踪性能,提出一种基于内模控制原理设计SCARA机器人控制器的方法。采用拉格朗日方法获得SCARA机器人动力学模型,将其作为内模控制的估计模型;选择内模滤波器[f(S)]设计内模控制器[Q(S),]使其满足稳态误差为零的条件,通过推导得出不同输入信号下的SCARA机器人控制律。通过仿真,将其与自适应模糊滑模控制方法进行对比分析,结果表明所提出的方法轨迹跟踪精度高,抗干扰能力强,控制器参数调节简单。