Brief communication: Uniform ultimate boundedness of a fuzzy logic controlled industrial robot

This article addresses the proof of uniform ultimate boundedness of a fuzzy logic controller plus a computed torque control scheme applied to trajectory tracking control of robotic manipulators. Further improvement of the performance of this fuzzy logic control scheme is achieved through automatic tuning of a weight parameter α leading to a self‐tuning fuzzy logic compensator. Experimental results demonstrate the effectiveness of the computed torque and fuzzy compensation scheme, as well as the self‐tuning fuzzy logic controller, applied to an industrial CRS Robotics Corporation A460 robot during a trajectory tracking task. © 2001 John Wiley & Sons, Inc.     

Recursive Direct Learning of Control Efforts for Trajectories with Different Magnitude Scales

Direct learning control (DLC) schemes have been developed recently to address non‐repeatable trajectory tracking problems. Unlike conventional iterative learning schemes, DLC schemes learn a set of unknown basis function vectors which can be used to generate the desired control profile of a new trajectory. DLC schemes use all available trajectory tracking information to obtain the unknown basis function vectors in a Least Squares and pointwise manner. A drawback of DLC is that the inverse matrix calculation is inevitable, which is time consuming and may result in singularities due to the batch processing nature. A Recursive Direct Learning Control method is proposed which learns the basis function vectors meanwhile overcomes the implementation difficulties in DLC schemes. The focus of this paper is on learning the control profile of trajectories with same operation period but different magnitude scales. The recursive learning method makes use of one trajectory information at a time, thus avoids the batch processing. The scheme is first developed for a class of nonlinear time varying systems and then extended to cover more general classes of nonlinear systems including robotic manipulator dynamics. Extensive simulation results on a two‐link robotic model are provided to confirm the features of the proposed algorithm.     

Control of a robotic orthosis for gait rehabilitation

Robot assisted gait training may help in producing rapid improvements in functional gait parameters. This paper presents new experimental results with an intrinsically compliant robotic gait training orthosis. The newly developed robotic orthosis has 6 degrees of freedom (DOFs). A trajectory tracking controller based on the boundary layer augmented sliding control (BASMC) law was implemented to guide the subject’s limbs on physiological gait trajectories. The compliance of the robotic orthosis sagittal plane hip and knee joints was also controlled, independently of the trajectory tracking control. The robotic orthosis and the control scheme were evaluated on three neurologically intact subjects walking on a treadmill. A maximum trajectory tracking error of 10° was recorded at the hip and knee sagittal plane joints. The results showed that subjects can walk in the robotic orthosis with comfort and the BASMC law was able to guide the subject’s limbs on reference physiological trajectories.     

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.     

基于干扰观测器的机械臂广义模型预测轨迹跟踪控制

为实现对多自由度机械臂关节运动精确轨迹跟踪,提出一种基于非线性干扰观测器的广义模型预测轨迹跟踪控制方法。针对机械臂轨迹跟踪运动学子系统,采用广义预测控制（Generalized Predictive Control,GPC）方法设计期望的虚拟关节角速度。对于机械臂轨迹跟踪动力学子系统,考虑机械臂的参数不确定性和未知外界扰动,利用GPC方法设计关节力矩控制输入,基于非线性干扰观测器方法实时估计和补偿系统模型中的不确定性。在李雅普诺夫稳定性理论框架下证明了机械臂关节角位置和角速度的跟踪误差最终收敛于零的小邻域。数值仿真验证了所提出控制方法的有效性和优越性。     

机械手输出反馈控制的新方法

本文将滑动模设计思想转变为较弱的滑动域吸引性设计,并以此提出了一种机械手速度观测方案,证明了观测误差的一致有界性,进而将这一观测方法应用于控制器设计中,并给出了控制误差上限,这一误差限与观测噪声限有关,而与实际运动轨迹无关,所以该方法具有较高的实用性和鲁棒性,实验结果验证了它的有效性.     

Controller design for natural and robotic systems with transmission delays

Robust stability and two‐dimensional trajectory following problems are considered for n‐link robotic systems with transmission delays. Such problems appear in telerobotics, where the controller is physically far from the robot, and in neural control of musculo‐skeletal (biological) systems, where muscle actuation and neural sensing are subject to time delays. A typical second‐order nonlinear dynamical model is taken with input and output time delays. In a prior work by the authors, a control strategy was developed for stable movement of the planar linkage system, using the standard Q‐parameterization and solving an H^∞ control problem to determine the free parameter. In this article, a new control scheme is proposed to eliminate the steady‐state errors seen in the tracking performance of the controller derived in the earlier work. Simulation examples are shown to demonstrate the effectiveness of the proposed control methodology. © 2002 Wiley Periodicals, Inc.     

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.     

Continuous output‐feedback finite‐time control for a class of second‐order nonlinear systems with disturbances

In this paper, a solution to the continuous output‐feedback finite‐time control problem is proposed for a class of second‐order MIMO nonlinear systems with disturbances. First, a continuous finite‐time controller is designed to stabilize system states at equilibrium points in finite time, which is proven correct by a constructive Lyapunov function. Next, because only the measured output is available for feedback, a continuous nonlinear observer is presented to reconstruct the total states in finite time and estimate the unknown disturbances. Then, a continuous output‐feedback finite‐time controller is proposed to track the desired trajectory accurately or alternatively converge to an arbitrarily small region in finite time. Finally, proposed methods are applied to robotic manipulators, and simulations are given to illustrate the applicability of the proposed control approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Improved nonlinear trajectory tracking using RBFNN for a robotic helicopter

This paper presents a backstepping control method using radial‐basis‐function neural network (RBFNN) for improving trajectory tracking performance of a robotic helicopter. Many well‐known nonlinear controllers for robotic helicopters have been constructed based on the approximate dynamic model in which the coupling effect is neglected; their qualitative behavior must be further analyzed to ensure that the unmodeled dynamics do not destroy the stability of the closed‐loop system. In order to improve the controller design process, the proposed controller is developed based on the complete dynamic model of robotic helicopters by using an RBFNN function approximation to the neglected dynamic uncertainties, and then proving that all the trajectory tracking error variables are globally ultimately bounded and converge to a neighborhood of the origin. The merits of the proposal controller are exemplified by four numerical simulations, showing that the proposed controller outperforms a well‐known controller in (J. Robust Nonlinear Control 2004; 14 (12):1035–1059). Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

State Space Constrained Iterative Learning Control for Robotic Manipulators

Real‐life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well‐known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.     

A closed-loop jacobian transpose scheme for solving the inverse kinematics of nonredundant and redundant wrists

The solution of the inverse kinematic problem is of the utmost importance in robotic manipulator control. This article proposes a closed-loop scheme for solving the inverse kinematic problem for nonredundant and redundant wrists based on the computation of the Jacobian transpose. The manipulability measure is suitably introduced as a constraint for redundant wrists, by taking advantage of the null space of the Jacobian matrix. The resulting algorithm provides a computational tool to solve a specified orientation trajectory into a joint trajectory. Numerical results with two spherical wrists show the excellent performance of the scheme.     

Adaptive fault tolerant control for a class of input and state constrained MIMO nonlinear systems

In this work, we present a novel adaptive fault tolerant control (FTC) scheme for a class of control input and system state constrained multi‐input multi‐output (MIMO) nonlinear systems with both multiplicative and additive actuator faults. The input constraints can be asymmetric, and the state constraints can be time‐varying. A novel tan‐type time‐varying Barrier Lyapunov Function (BLF) is proposed to deal with the state constraints, and an auxiliary system is designed to analyze the effect of the input constraints. We show that under the proposed adaptive FTC scheme, exponential convergence of the output tracking error into a small neighbourhood of zero is guaranteed, while the constraints on the system state will not be violated during operation. Estimation errors for actuator faults are bounded in the closed loop. An illustrative example on a two degree‐of‐freedom robotic manipulator is presented to demonstrate the effectiveness of the proposed FTC scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

一种模糊学习控制方法及其在多指手爪位置伺服系统中的应用

本文针对重复抓取与操作的多指手爪，提出了一种模糊学习控制方法，该方法利用先前的控制误差，逐步地修正不完善的控制规则，使系统的实际输出收敛于给定的轨迹，本文还给出了这种方法的收敛性条件，并应用于多指手爪的位置伺服系统中，改善控制性能，达到了预期的控制效果。     

Experimental Control of a Two-Dof Flexible Robot Manipulator by Optimal and Sliding Methods

This paper presents an experimental study of a robust control scheme for flexible-link robotic manipulators. The design is based on a simple strategy for trajectory tracking which exploits the two-time scale nature of the flexible part and the rigid part of the dynamic equations of this kind of robotic arms: A slow subsystem associated with the rigid motion dynamics and a fast subsystem associated with the flexible link dynamics. Two experimental approaches are considered. In a first test an LQR optimal design strategy is used, while a second design is based on a sliding-mode scheme. Experimental results on a laboratory two-dof flexible manipulator show that this composite approach achieves good closed-loop tracking properties for both design philosophies, which compare favorably with conventional rigid robot control schemes.     

Nonlinear adaptive control for dynamic and dead-zone uncertainties in robotic systems

Accurate position tracking performance of robotic systems with both dynamic and dead-zone uncertainties is difficult to achieve by traditional adaptive control. In this paper, for nonlinear robotic systems in the presence of uncertain dynamics and dead-zone, an adaptive control scheme is employed to guarantee accurate and stable position tracking. The dynamics of the robot and dead-zone parameters are concurrently estimated in the adaptation laws and the estimated values are subsequently applied in the control law. In order to demonstrate the practicability of this paper, an experimental setup composed of Phantom Omni robots are built. The experimental results show that even when the dynamic and dead-zone uncertainties simultaneously appear, the robot can well track any desired position trajectory.

     

Adaptive Fault‐Tolerant Control of Uncertain MIMO Systems with Reduced Requirement on Desired Trajectory Under Unknown Control Direction

This paper aims at investigating the tracking control problem for a class of multi‐input multi‐output (MIMO) nonlinear systems with non‐square control gain matrix subject to unknown control direction and uncertain desired trajectory. By using the artificial neural network (NN) reconstructs the target trajectory with actual disguised trajectory, we are able to design a practical and stable tracking control scheme without the need for the unavailable desired trajectory. Nussbaum‐type function is incorporated in the control law to handle the unknown control direction. The remarkable feature of the proposed scheme is that it is robust against modeling uncertainties and tolerant to actuation faults, yet guarantees that the closed‐loop system is stable in the sense of ultimately uniformly bounded (UUB). The effectiveness of the proposed control schemes are illustrated through simulation results.     

Visual servoing tracking control of uncalibrated manipulators with a moving feature point

The paper is concerned with the problem of uncalibrated visual servoing robots tracking a dynamic feature point along with the desired trajectory. A nonlinear observer and a nonlinear controller are proposed, which allow the considered uncalibrated visual servoing robotic system to fulfil the desired tracking task. Based on this novel control method, a dynamic feature point with unknown motion parameters can be tracked effectively along with the desired trajectory, even with multiple uncertainties existing in the camera, the kinematics and the manipulator dynamics. By the Lyapunov theory, asymptotic convergence of the image errors to zero with the proposed control scheme is rigorously proven. Simulations have been conducted to verify the performance of the proposed control scheme. The results demonstrated good convergence of the image errors.     

Stabilization of Nonholonomic Robotic Systems Using Adaptation and Homogeneous Feedback

This paper considers the problem of stabilizing nonholonomic robotic systems in the presence of uncertainty regarding the system dynamic model. It is proposed that a simple and effective solution to this problem can be obtained by combining ideas from homogeneous system theory and adaptive control theory. Thus each of the proposed control systems consists of two subsystems: a (homogeneous) kinematic stabilization strategy which generates a desired velocity trajectory for the nonholonomic system, and an adaptive control scheme which ensures that this velocity trajectory is accurately tracked. This approach is shown to provide arbitrarily accurate stabilization to any desired configuration and can be implemented without knowledge of the system dynamic model. Moreover, it is demonstrated that exponential rates of convergence can be achieved with this methodology. The efficacy of the proposed stabilization strategies is illustrated through extensive computer simulations with nonholonomic robotic systems arising from explicit constraints on the system kinematics and from symmetries of the system dynamics.