Dynamics and adaptive control of a dual-arm space robot with closed-loop constraints and uncertain inertial parameters

A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the po- sition and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane＇s equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters be- ing estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov＇s method, and simulation results are also presented to demonstrate the effectiveness of the proposed approach.

Vision-based Stabilization of Nonholonomic Mobile Robots by Integrating Sliding-mode Control and Adaptive Approach

Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.

Adaptive Fuzzy Dynamic Surface Control of Flexible-Joint Robot Systems With Input Saturation

In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.

Stewart平台神经网络非奇异终端滑模控制

针对Stewart平台的六自由度(six degrees of freedom,6-DOF)轨迹跟踪问题,提出一种基于神经网络的非奇异终端滑模控制方法并应用于Stewart平台的位置姿态控制中。通过分析Stewart平台的位置反解和速度反解,建立运动学方程,利用牛顿-欧拉方程建立动力学方程,并结合加速度反解得到了平台的状态空间表达式;基于非奇异滑模面函数,设计非奇异终端滑模控制律。考虑到径向基函数(radial Basis function,RBF)神经网络的逼近特性,采用RBF神经网络对模型未知部分进行自适应逼近,并利用Lyapunov第二法设计了自适应律;通过仿真证明控制器设计的有效性。仿真结果表明,相比于比例积分微分(proportional integral derivative,PID)控制器,提出的RBF神经网络非奇异终端滑模控制器具有更好的轨迹跟踪精度和动态特性。

Unified Modeling Approach of Kinematics, Dynamics and Control of a Free-Flying Space Robot Interacting with a Target Satellite

In this paper a unified control-oriented modeling approach is proposed to deal with the kinematics, linear and angular momentum, contact constraints and dynamics of a free-flying space robot interacting with a target satellite. This developed approach combines the dynamics of both systems in one structure along with holonomic and nonholonomic constraints in a single framework. Furthermore, this modeling allows consid-ering the generalized contact forces between the space robot end-effecter and the target satellite as internal forces rather than external forces. As a result of this approach, linear and angular momentum will form holonomic and nonholonomic constraints, respectively. Meanwhile, restricting the motion of the space robot end-effector on the surface of the target satellite will impose geometric constraints. The proposed momentum of the combined system under consideration is a generalization of the momentum model of a free-flying space robot. Based on this unified model, three reduced models are developed. The first reduced dynamics can be considered as a generalization of a free-flying robot without contact with a target satellite. In this re-duced model it is found that the Jacobian and inertia matrices can be considered as an extension of those of a free-flying space robot. Since control of the base attitude rather than its translation is preferred in certain cases, a second reduced model is obtained by eliminating the base linear motion dynamics. For the purpose of the controller development, a third reduced-order dynamical model is then obtained by finding a common solution of all constraints using the concept of orthogonal projection matrices. The objective of this approach is to design a controller to track motion trajectory while regulating the force interaction between the space robot and the target satellite. Many space missions can benefit from such a modeling system, for example, autonomous docking of satellites, rescuing satellites, and satellite servicing, where it is vital to limit the con-tact force during the robotic operation. Moreover, Inverse dynamics and adaptive inverse dynamics control-lers are designed to achieve the control objectives. Both controllers are found to be effective to meet the specifications and to overcome the un-actuation of the target satellite. Finally, simulation is demonstrated by to verify the analytical results.

Adaptive learning tracking control of robotic manipulators with uncertainties

An adaptive learning tracking control scheme is developed for robotic manipulators by a synthesis of adaptive control and learning control approaches. The proposed controller possesses both adaptive and learning properties and thereby is able to handle robotic systems with both time-varying periodic uncertainties and time invariant parameters. Theoretical proofs are established to show that proposed controllers ensure asymptotical tracking performance. The effectiveness of the proposed approaches is validated through extensive numerical simulation results.

Analysis and Design of Control Dynamics of Manipulator Robot’s Joint Drive

A system of automatic control(SAC)of the electric drive of rotary link of manipulator robot(MR)is considered.The variables of mechanical load of drive,depending of changes of a spatial configuration of MR in the course of motion,mass and dimensions of the moved payloads,etc.,are received from the motion equation of rotary link of MR.Changes of mechanical load can be unknown and cause the essential deterioration in dynamical properties(speed,damping,etc.)of the SAC of drive.With the aim to stabilize the desired dynamical properties of SAC the algorithm of drive control is proposed which is adaptive to changes of mechanical load.The unknown parameters of drive load,necessary to form the adaptive control algorithm,are identified via observing device(OD).The work algorithm of OD to identify the unknown parameters of drive load is proposed.For the proposed control and work of OD algorithms the block diagram of drive control is construction.The simulation results on computer proved that in considered SAC of drive the stabilization of desired dynamical properties ensured.

Modeling and Adaptive Control of an Omni-Mecanum-Wheeled Robot

The complete dynamics model of a four-Mecanum-wheeled robot considering mass eccentricity and friction uncertainty is derived using the Lagrange’s equation. Then based on the dynamics model, a nonlinear stable adaptive control law is derived using the backstepping method via Lyapunov stability theory. In order to compensate for the model uncertainty, a nonlinear damping term is included in the control law, and the parameter update law with σ-modification is considered for the uncertainty estimation. Computer simulations are conducted to illustrate the suggested control approach.

An Adaptive Fuzzy Controller for Trajectory Tracking of Robot Manipulator

In this paper, an adaptive fuzzy control algorithm is proposed for trajectory tracking of an n-DOF robot manipulator subjected to parametric uncertainty and it is advantageous compared to the conventional nonlinear saturation controller. The asymptotic stability of the proposed controller has been derived based on Lyapunaov energy function. The design procedure is straightforward due to its simple fuzzy rules and control strategies. The simulation results show that the present control strategy effectively reduces the control effort with negligible chattering in control torque signals in comparison to the existing nonlinear saturation controller.

助行康复机器人自适应步态控制方法研究

为了提高助行康复机器人在不同坡度上的稳定性,合理的步态控制方法显得尤为重要。这里以助行康复机器人为研究对象,提出了一种将长短期记忆网络和动态运动基元结合的步行康复机器人自适应步态控制方法。将传感器采集信息与动态运动基元中步态控制项相结合,对参考步态进行调整。同时通过更改动态运动基元的时间项和空间项对步态进行二次调整以适应不同的坡度。通过试验对该控制方法的可用性进行验证。结果表明,该方法可以在(0~20)°斜坡进行自适应步态变换,具有一定的实用价值。

基于多人零和博弈的模块化机器人系统近似最优控制

提出一种基于多人零和博弈的模块化机器人(Modular Robot Manipulators, MRMs)系统近似最优控制方法。建立了具有交联耦合(Interconnected Dynamic Couplings, IDC)的模块化机器人系统动力学模型。将机器人系统的控制律和IDC效应作为零和博弈的参与者,MRM系统的最优跟踪控制问题转化为多人零和博弈问题。根据自适应动态规划(Adaptive Dynamic Programming, ADP)算法,通过建立评判神经网络求解哈密顿-雅克比-埃塞克斯(Hamilton-Jacobi-Issacs, HJI)方程,推导出最优控制律。基于李雅普诺夫定理,证明了闭环机器人系统是渐近稳定的,最后通过实验验证了所提控制方法的有效性。

食品协作机器人动态目标抓取控制方法研究

目的:解决目前协作机器人在食品动态目标抓取中存在的准确性较低问题。方法:基于协作机器人体系结构,提出将模糊自整定PID控制和鲁棒自适应补偿器相结合用于协作机器人食品动态目标抓取。PID结合模糊控制完成参数自整定,鲁棒算法与自适应算法相结合用于系统不确定性补偿。通过试验分析了所提方法的性能,验证了该方法的可行性。结果:所提方法在协作机器人动态目标抓取中具有较好的效果,提高了协作机器人动态抓取的准确性,在传送带速度100 mm/s时,动态抓取成功率达到99.50%,对食品动态目标抓取具有一定的应用价值。结论:通过优化现有目标抓取控制方法,可有效提高协作机器人的抓持精度。

面向未知空间的模块化机器人最优位置-力控制研究

针对模块化机器人系统与外界未知环境交互的控制任务,提出一种基于自适应动态规划的位置-力控制。首先,考虑模块化机器人系统受未知环境约束,对未知环境约束空间进行分析,并设计自适应估计算法近似未知约束参数向量,从而得到未知外部约束力转化为模块化机器人所受的近似约束力矩,设计融合位置和末端接触力跟踪误差的多指标性能指标函数,结合自适应动态规划思想和策略迭代算法,求解哈密尔顿方程,从而实现面向未知空间的模块化机器人最优位置-力控制。

高精度激光测距下的机器人自主避障控制

在机器人自主避障过程中,由于传感器数据的误差会降低机器人感知和决策的准确性,从而影响机器人自主避障能力;为此,提出高精度激光测距下的机器人自主避障控制方法;通过设计机器人体系结构,建立机器人运动学模型,为机器人避障控制提供依据;采用高精度激光测距技术,构建机器人移动场地地形;通过自适应阈值方法,完成机器人的自主避障控制;实验结果表明,所提方法的机器人自主避障控制效果好,且障碍物位置测试值与实际位置值的误差保持在0.4 m以内,具有较高的避障控制精确度。

6-(P-2P-S)并联宏动平台的轨迹跟踪控制

提出了一种新型6-(P-2P-S)并联宏动平台,应用凯恩方法建立了该机构的刚体动力学模型。采用分散控制策略对各分支独立控制,建立了单通道驱动关节空间的CARIMA模型,并针对广义预测控制(GPC)计算量大的缺陷,提出了一种模糊自适应直接广义预测控制(FDGPC)方法,该方法利用模糊逻辑系统直接设计预测控制器,并基于广义误差估计值对控制器参数和广义误差估计值中的未知向量进行自适应调整,轨迹跟踪控制仿真结果验证了该方法具有很强的鲁棒性,可实现并联宏动平台的高速高精度定位。

Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

To achieve the collision-free trajectory tracking of the four-wheeled mobile robot(FMR),existing methods resolve the tracking control and obstacle avoidance separately.Guaranteeing the synergistic robustness and smooth navigation of mobile robots subjected to motion uncertainties in a dynamic environment using this non-cooperative processing method is difficult.To address this challenge,this paper proposes an obstacle-circumventing adaptive control(OCAC)framework.Specifically,a novel anti-disturbance terminal slide mode control with adaptive gains is formulated,incorporating specified control laws for different stages.This formulation guarantees rapid convergence and simultaneous chattering elimination.By introducing sub-target points,a new sub-target dynamic tracking regression obstacle avoidance strategy is presented to transfer the obstacle avoidance problem into a dynamic tracking one,thereby reducing the burden of local path searching while ensuring system stability during obstacle circumvention.Comparative experiments demonstrate that the proposed OCAC method can strengthen the convergence and obstacle avoidance efficiency of the concerned FMR system.

4-R(2-SS)并联机器人机构积分指数自适应滑模控制研究

为实现4-R(2-SS)并联机器人机构的运动学高性能控制,提出了克服支路间耦合作用和系统不确定性作用的积分指数自适应滑模控制方法。通过建立关节空间动力学模型,求得各支路驱动关节包含耦合惯量的等效惯量,以补偿各支路间耦合作用;通过设计积分指数自适应律,提出并联机器人机构的积分指数自适应滑模控制方法。使用MatLab对所设计的控制算法进行仿真,并将其应用于4-R(2-SS)并联机器人样机,验证了所提出积分指数自适应滑模控制方法的有效性。

Dynamic Movement Primitives Based Robot Skills Learning

In this article,a robot skills learning framework is developed,which considers both motion modeling and execution.In order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primitives(DMPs)is introduced to model motion.A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint manipulators.The DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion sequences.In addition,motions are categorized into different goals and durations.It is worth mentioning that an adaptive neural networks(NNs)control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution,which is beneficial to the improvement of reliability of the skills learning system.The experiment test on the Baxter robot verifies the effectiveness of the proposed method.

基于自适应动态规划的移动机器人视觉伺服跟踪控制

针对移动机器人视觉伺服跟踪控制问题,提出一种基于自适应动态规划(Adaptive dynamic programming,ADP)的控制方法.通过移动机器人上的相机拍摄共面特征点的当前图像、期望图像以及参考图像,利用单应性技术得到移动机器人当前的位姿信息与期望的位姿信息(即平移量与旋转角度),从而通过当前与期望的平移旋转之间差值得到系统的开环误差模型.进而,针对此系统设计最优控制器,同时做合适的控制输入变换.在此基础上设计一个基于ADP的视觉伺服控制方法以保证移动机器人完成轨迹跟踪任务.为求出最优控制输入,采用一个评价神经网络近似值函数,通过不断学习逼近哈密顿-雅可比-贝尔曼(Hamilton-Jacobi-Bellman,HJB)方程的解.与以往不同的是,由于系统存在时变项,导致HJB方程也含有时变项,因此需要设计具有时变权值结构的神经网络近似值函数.最终证明在所设计的控制方法作用下,闭环系统是一致最终有界的.

一种自适应的双臂协作码垛机器人的研发

针对烟草物流行业中纸箱包装品种多、物流量大以及物流空间封闭和狭窄等特点,结合机器视觉、机器人等先进技术,采用自动轨迹规划以及自适应的多阶S型曲线速度控制方法,研发了一种自适应的六轴双臂协作码垛机器人。测试结果表明:该码垛机器人的适应性强,码垛平稳快速,大大提高了烟草物流行业的码垛效率。