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.     

A global adaptive learning control for robotic manipulators

This paper addresses the problem of designing a global adaptive learning control for robotic manipulators with revolute joints and uncertain dynamics. The reference signals to be tracked are assumed to be smooth and periodic with known period. By developing in Fourier series expansion the input reference signals of every joint, an adaptive learning PD control is designed which ‘learns’ the input reference signals by identifying their Fourier coefficients: global asymptotic and local exponential stability of the tracking error dynamics are obtained when the Fourier series expansion of each input reference signal is finite, while arbitrary small tracking errors are achieved otherwise. The resulting control is not model based and depends only on the period of the reference signals and on some constant bounds on the robot dynamics.     

基于力/力矩信息的面向位控机器人的阻抗控制

为了克服末端接触点距离力传感器中心较远时,力传感器测量实际接触力的局限性,分析实际作用力与测量力/力矩值之间的关系,利用力传感器信息或力矩信息得到位置控制方向和力控制方向.根据位控与力控方向对机器人末端进行参考轨迹规划,在阻抗控制律中应用参考比例因子调节参考轨迹.基于力误差信息通过模糊推理调节参考比例因子的大小,使生成的参考轨迹适应未知表面的变化.实验结果表明,所提出的控制方法能实现未知工件表面的恒力跟踪.     

具有时滞的柔性关节多机械臂协同自适应位置/力控制

由于关节机械臂长期运行后,齿轮间隙扩大产生的时间滞后将使得系统跟踪性能降低.针对此问题,本文提出了一种自适应位置/力控制策略来保证闭环系统稳定性以及位置/力跟踪性能.首先,对多机械臂和物体系统进行任务空间动力学建模.随后,利用Pade理论将时间滞后近似为二阶有理分式.同时,利用神经网络自适应算法克服模型建模误差对系统稳定性的影响,利用同时包含位置误差和力误差的线性滑模项,设计位置/力控制器.通过李雅普诺夫稳定性理论,证明控制策略能实现位置误差和内力误差的渐近收敛.最后,仿真验证证明所设计控制策略的有效性.     

Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics

A novel neural network-based robust finite-time control strategy is proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties, in which the actuator dynamics is fully considered. The controller, which possesses finite-time convergence and strong robustness, consists of two parts, namely a neural network for approximating the nonlinear uncertainty function and a modified variable structure term for eliminating the approximate error and guaranteeing the finite-time convergence. According to the analysis based on the Lyapunov theory and the relative finite-time stability theory, the neural network is asymptotically convergent and the controlled robotic system is finite time stable. The proposed controller is then verified on a two-link robotic manipulator by simulations and experiments, with satisfactory control performance being obtained even in the presence of various uncertainties and external disturbances.     

Neural-network-based two-loop control of robotic manipulators including actuator dynamics in task space

A neural-network-based motion controller in task space is presented in this paper. The proposed controller is addressed as a two-loop cascade control scheme. The outer loop is given by kinematic control in the task space. It provides a joint velocity reference signal to the inner one. The inner loop implements a velocity servo loop at the robot joint level. A radial basis function network （RBFN） is integrated with proportional-integral （PI） control to construct a velocity tracking control scheme for the inner loop. Finally, a prototype technology based control system is designed for a robotic manipulator. The proposed control scheme is applied to the robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.     

Motion planning for robotic manipulators using robust constrained control

Since their first appearance in the 1970's, industrial robotic manipulators have considerably extended their application fields, allowing end-users to adopt this technology in previously unexplored scenarios. Correspondingly, the way robot motion can be specified has become more and more complex, requiring new capabilities to the robot, such as reactivity and adaptability. For an even enhanced and widespread use of industrial manipulators, including the newly introduced collaborative robots, it is necessary to simplify robot programming, thus allowing this activity to be handled by non-expert users. Next generation robot controllers should intelligently and autonomously interpret production constraints, specified by an application expert, and transform them into motion commands only at a lower and real-time level, where updated sensor information or other kind of events can be handled consistently with the higher level specifications. The availability of several execution strategies could be then effectively exploited in order to further enhance the flexibility of the resulting robot motion, especially during collaboration with humans.This paper presents a novel methodology for motion specification and robust reactive execution. Traditional trajectory generation techniques and optimisation-based control strategies are merged into a unified framework for simultaneous motion planning and control. An experimental case study demonstrates the effectiveness and the robustness of this approach, as applied to an image-guided grasping task.     

A design approach to adaptive model-following control of robotic manipulators

In this paper, the robot dynamics are represented by a nonlinear state-space model containing a disturbance term due to gravitational loading. Using a suitable linear time-invariant reference model, an adaptive model-following control problem is formulated which satisfies the matching conditions. The control input is designed to have two components: a nonadaptive linear component to do the task of model-following and a nonlinear unit-vector component based on hyperstability theory to do the adaptive task. An additional integral feedback term is further superimposed and then the overall asymptotic hyperstability is established. The simulation results on the first three joints of PUMA 560 robot manipulator indicate the potential of our design approach.Based on research supported by Kuwait University Research Administration under Grant No. EE 049.     

MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators

A new design approach of a multiple-input-multiple-output (MIMO) adaptive fuzzy terminal sliding-mode controller (AFTSMC) for robotic manipulators is described in this article. A terminal sliding-mode controller (TSMC) can drive system tracking error to converge to zero in finite time. The AFTSMC, incorporating the fuzzy logic controller (FLC), the TSMC, and an adaptive scheme, is designed to retain the advantages of the TSMC while reducing the chattering. The adaptive law is designed on the basis of the Lyapunov stability criterion. The self-tuning parameters are adapted online to improve the performance of the fuzzy terminal sliding-mode controller (FTSMC). Thus, it does not require detailed system parameters for the presented AFTSMC. The simulation results demonstrate that the MIMO AFTSMC can provide a reasonable tracking performance.     

Robust neural control for robotic manipulators

A robust neural control scheme for mechanical manipulators is presented. The design basically consists of an adaptive neural controller which implements a feedback linearization control law for a generic manipulator with unknown parameters, and a sliding-mode control which robustifies the design and compensates for the neural approximation errors. It is proved that the resulting closed-loop system is stable and that the trajectory-tracking control objective is achieved. Some simulation results are also provided to evaluate the design.     

Robust adaptive tracking control of robotic systems with uncertainties

To deal with the uncertainty factors of robotic systems, a robust adaptive tracking controller is proposed. The knowledge of the uncertainty factors is assumed to be unidentified; the proposed controller can guarantee robustness to parametric and dynamics uncertainties and can also reject any bounded, immeasurable disturbances entering the system. The stability of the proposed controller is proven by the Lyapunov method. The proposed controller can easily be implemented and the stability of the closed system can be ensured; the tracking error and adaptation parameter error are uniformly ultimately bounded （UUB）. Finally, some simulation examples are utilized to illustrate the control performance.     

基于模糊变结构的机械臂控制

现有的机械臂模糊变结构控制方法大都计算复杂或需要检测滑模面的微分信号.本文将机械臂模型分为确定部分和不确定部分进行研究,对确定部分采用一般反馈控制,对不确定部分采用变结构集中补偿控制,为了消除变结构控制器的抖震引入模糊控制方法,将滑模面作为模糊控制器的输入,补偿控制器权值作为输出.本方案不仅不需要检测滑模面微分信号,而且计算简单,易于实现.仿真结果表明,在存在模型误差和外部扰动的情况下,该方案既能达到快速跟踪,又能很好的消除控制器的抖震.     

基于降阶位置/力模型的机器人神经网络控制

双足机器人的双脚支撑期是实现其步行运动的重要过程,然而耦合的位置/力控制难以保证其稳定平滑运动.本文提出了一种基于降阶位置/力模型的机器人控制策略,整合了位置控制子空间模型和力控制子空间模型,通过模型降阶减小了控制器设计的复杂度,并采用神经网络自适应控制方法综合多控制目标,实现了双足机器人的平滑稳定控制并有效地抑制了系统外扰和参数不确定性的影响.最后,仿真算法验证了该控制方法和模型的有效性.     

Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty

This study is devoted to sensorless adaptive force/position control of robot manipulators using a position-based adaptive force estimator (AFE) and a force-based adaptive environment compliance estimator. Unlike the other sensorless method in force control that uses disturbance observer and needs an accurate model of the manipulator, in this method, the unknown parameters of the robot can be estimated along with the force control. Even more, the environment compliance can be estimated simultaneously to achieve tracking force control. In fact, this study deals with three challenging problems: No force sensor is used, environment stiffness is unknown, and some parametric uncertainties exist in the robot model. A theorem offers control laws and updating laws for two control loops. In the inner loop, AFE estimates the exerted force, and then, the force control law in the outer loop modifies the desired trajectory of the manipulator for the adaptive tracking loop. Besides, an updating law updates the estimated compliance to provide an accurate tracking force control. Some experimental results of a PHANToM Premium robot are provided to validate the proposed scheme. In addition, some simulations are presented that verify the performance of the controller for different situations in interaction.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

Stability of hybrid position and force control for robotic manipulator with kinematics and dynamics uncertainties

Most research so far on hybrid position and force control laws of robotic manipulator has assumed that knowledge of kinematics is known exactly. In the presence of modeling errors, it is unknown whether the stability of the constrained robot system can still be ensured. In this paper, stability and setpoint control problems of constrained robot with kinematics and dynamics uncertainties are formulated and solved. We shall show that the manipulator end-effector's motion is asymptotically stable even in the presence of such uncertainties.     

Adaptive synchronised tracking control for multiple robotic manipulators with uncertain kinematics and dynamics

In this study, a new adaptive synchronised tracking control approach is developed for the operation of multiple robotic manipulators in the presence of uncertain kinematics and dynamics. In terms of the system synchronisation and adaptive control, the proposed approach can stabilise position tracking of each robotic manipulator while coordinating its motion with the other robotic manipulators. On the other hand, the developed approach can cope with kinematic and dynamic uncertainties. The corresponding stability analysis is presented to lay a foundation for theoretical understanding of the underlying issues as well as an assurance for safely operating real systems. Illustrative examples are bench tested to validate the effectiveness of the proposed approach. In addition, to face the challenging issues, this study provides an exemplary showcase with effectively to integrate several cross boundary theoretical results to formulate an interdisciplinary solution.     

Mimo and siso self-tuning hybrid position/force control of robotic manipulators

This article presents a new approach for the hybrid position/force control of a manipulator by using self-tuning regulators (STR). For this purpose, the discrete-time stochastic multi-input multi-output (MIMO) and single-input single-output (SISO) models are introduced. The MIMO model's output vector has the positions and velocities of the gripper expressed in the world (xyz) coordinate system as the components. The SISO model outputs are the hybrid errors consisting of the derivatives of the position and force errors at the joints. The inputs of both models are the joint torques. The unknown parameters of those models can be calculated recursively on-line by the square-root estimation algorithm (SQR). An adaptive MIMO and SISO self-tuning type controllers are then designed by minimizing the expected value of a quadratic criterion. This performance index penalizes the deviations of the actual position and force path of the gripper from the desired values expressed in the Cartesian coordinate system. An integrating effect is also included in the performance index to remove the steady-state errors. Digital simulation results using the parameter estimation and the control algorithms are presented and the performances of those two controllers are discussed. © 1996 John Wiley & Sons, Inc.     

Stabilizing position/force control of robots interacting with environment by learning connectionist structures

This paper is mostly concerned with the application of connectionist architectures for fast on-line learning of robot dynamic uncertainties used at the executive hierarchical control level in robot contact tasks. The connectionist structures are integrated in non-learning control laws for contact tasks which enable stabilization and good tracking performance of position and force. It has been shown that the problem of tracking a specified reference trajectory and specified force profile with a present quality of their transient response can be efficiently solved by means of the application of a four-layer perceptron. A four-layer perceptron is part of a hybrid learning control algorithm through the process of synchronous training which uses fast learning rules and available sensor information in order to improve robotic performance progressively in the minimum possible number of learning epochs. Some simulation results of the deburring process with robot MANUTEC r3 are shown to verify effectiveness of the proposed control learning algorithms.     

performance control of robot manipulators with kinematics,dynamics and actuator uncertainties

This paper deals with the task‐space trajectory tracking control problem of robot manipulators. An improved adaptive backstepping controller is proposed to deal with the uncertainties in kinematics, dynamics, and actuator modeling. To avoid the explosion of computation in conventional backstepping techniques, a modified dynamic surface control algorithm is proposed, which guarantees the asymptotic convergence rather than the uniformly ultimately boundedness of tracking errors in conventional dynamic surface control methods. Furthermore, the expression of the norm of tracking errors is explicitly derived in relation to the controller parameters, which provides instructions on tuning controller parameters to adjust the system performance. Moreover, the passivity structure of the designed adaptation law is thoroughly analyzed. Simulation of a free‐floating space robot is used to verify the effectiveness of the proposed control strategy in comparison with the conventional tracking control schemes. Copyright © 2016 John Wiley & Sons, Ltd.