Supervisory hybrid control of piezoelectric actuators utilized in tracking piecewise continuous trajectories

This article presents a supervisory hybrid control design for piezoelectric actuators utilized in tracking trajectories with intermittent jump discontinuities. We use a previously developed robust adaptive controller and a standard PID controller to construct this hybrid control strategy. We show that when the sub-controllers are used for step tracking, while primarily tuned for continuous trajectory tracking, large undesirable oscillations occur. Conversely, when the controllers are retuned for step tracking, their performance degrades in tracking high-frequency continuous trajectories. Thus, a supervisory hybrid controller is developed to track desired trajectories with occasional discontinuities, using both the robust adaptive and the PID controllers. The robust adaptive controller performs as the primary controller for tracking the continuous segments of the desired trajectory, while the PID controller is activated when the steps occur. Results indicate that the proposed supervisory hybrid controller outperforms both sub-controllers in tracking high-frequency trajectories with intermittent discontinuities.     

基于环境刚度模糊自适应估计的机器人力控制

在未知环境中为实现精确的接触力控制,需要力控制器能够适应环境的变化。该文将多模型模糊控制器引入到机器人力控制中来适应未知环境的变化,针对几种典型的接触环境刚度设计相应的模糊控制器。由于在环境变化时,很难得到精确的环境刚度值,该文对环境刚度进行模糊自适应估计,进而确定各个模糊力控制器的加权系数,模糊力控制器生成机器人位置控制系统的输入指令。仿真研究表明所设计的控制器是可行和有效的。     

Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety

This paper studies an adaptive control strategy for a class of 5 DOF upper-limb exoskeleton robot with a special safety consideration. The safety requirement plays a critical role in the clinical treatment when assisting patients with shoulder, elbow and wrist joint movements. With the objective of assuring the tracking performance of the pre-specified operations, the proposed adaptive controller is firstly designed to be robust to the model uncertainties. To further improve the safety and fault-tolerance in the presence of unknown large parameter variances or even actuator faults, the adaptive controller is on-line updated according to the information provided by an adaptive observer without additional sensors. An output tracking performance is well achieved with a tunable error bound. The experimental example also verifies the effectiveness of the proposed control scheme.     

Adaptive neural network based position tracking control for Dual-master/Single-slave teleoperation system under communication constant time delays

The novel trajectory tracking control strategies for trilateral teleoperation systems with Dual-master/Single-slave robot manipulators under communication constant time delays are proposed in this article. By incorporating this design technique into the neural network (NN) based adaptive control framework, two controllers are designed for the trilateral teleoperation systems in free motion. First, with acceleration measurements, an adaptive controller under the synchronization variables containing the position and velocity error is constructed to guarantee the position and velocity tracking errors between the trilateral teleoperation systems asymptotically converge to zero. Second, without acceleration measurements, an adaptive controller under the new synchronization variables is presented such that the trilateral teleoperation systems can obtain the same trajectory tracking performance as the first controller. Third, in term of establishing suitable Lyapunov–Krasovskii functionals, the asymptotic tracking performances of the trilateral teleoperation systems can be derived independent of the communication constant time delays. Moreover, these two controllers are obtained without the knowledge of upper bounds of the NN approximation errors, respectively. Finally, simulation results are presented to demonstrate the validity of the proposed methods.     

Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy

A direct adaptive robust tracking control is proposed for trajectory tracking of 6 DOF industrial robot in the presence of parametric uncertainties, external disturbances and uncertain nonlinearities. The controller is designed based on the dynamic characteristics in the working space of the end-effector of the 6 DOF robot. The controller includes robust control term and model compensation term that is developed directly based on the input reference or desired motion trajectory. A projection-type parametric adaptation law is also designed to compensate for parametric estimation errors for the adaptive robust control. The feasibility and effectiveness of the proposed direct adaptive robust control law and the associated projection-type parametric adaptation law have been comparatively evaluated based on two 6 DOF industrial robots. The test results demonstrate that the proposed control can be employed to better maintain the desired trajectory tracking even in the presence of large parametric uncertainties and external disturbances as compared with PD controller and nonlinear controller. The parametric estimates also eventually converge to the real values along with the convergence of tracking errors, which further validate the effectiveness of the proposed parametric adaption law.     

Model-based feedforward precompensation and VS-type robust nonlinear postcompensation for uncertain robotic systems with/without knowledge of uncertainty bounds (II)

In this paper, the robust nonlinear controller for an uncertain robot system is developed and characterized with a unified method. Based on deterministic approach, the control structure consists of two parts: In the first part, the primary control law is synthesized to precompensate for the nominal system; and in the second part the adaptive version of robust controllers are utilized to postcompensate for the system uncertainties. The uncertainties assumed in this papar are bounded by higher-order polynomials in the Euclidean norms of system states without knowledge of bounding coefficients. Using the Lyapunov stability theory, we can guarantee that all possible responses of the closed-loop system are at least uniformly and ultimately bounded. The tracking properties of the control algorithms are verified through numerical simulations, and the results show that the proposed controllers are proven to be robust enough for any higher-order system uncertainty.     

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.     

积分切换面自适应滑模控制及其在并联机器人中的应用

针对交流伺服电机作为驱动装置的2-DOF并联机器人,设计出一种带有积分切换面的自适应滑模控制器。首先,带有积分运算切换面的变结构控制器使系统具有对未知参数变化和外部干扰不敏感的特性。其次,通过设计一种自适应律,实现对系统不确定量的在线辨识估计,以辨识结果实时调整控制器参数,以削弱系统抖动,提高了控制系统的实用性。仿真结果表明所设计控制器抗干扰能力强,能较好地实现2-DOF并联机器人各支路的运动控制,具有较好的稳定性和鲁棒性能。     

Navigation of autonomous mobile robot using adaptive network based fuzzy inference system

Intelligent soft computing techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are proven to be efficient and suitable when applied to a variety of engineering systems. The hallmark of this paper investigates the application of an adaptive neuro-fuzzy inference system (ANFIS) to path generation and obstacle avoidance for an autonomous mobile robot in a real world environment. ANFIS has also taken the advantages of both learning capability of artificial neural network and reasoning ability of fuzzy inference system. In this present design model different sensor based information such as front obstacle distance (FOD), right obstacle distance (ROD), left obstacle distance (LOD) and target angle (TA) are given input to the adaptive fuzzy controller and output from the controller is steering angle (SA) for mobile robot. Using ANFIS tool box, the obtained mean of squared error (MSE) for training data set in the current paper is 0.031. The real time experimental results also verified with simulation results, showing that ANFIS consistently perform better results to navigate the mobile robot safely in a terrain populated by variety obstacles.     

机器人臂的多项式PD型学习控制方法

提出一种用于机器人臂的带有重力补偿的多项式PD型(PPD)学习控制器,基于多项式神经网络给出了这种控制器的比例系数连续学习算法,由非线性机器人动力学模型与所提出的学习控制器所组成的闭环系统被证明在满足李雅普诺夫直接法和拉萨尔不变集定理时是全局渐近稳定的,除了理论结果,也提供了在两自由度机器人臂位置控制中的仿真实验比较,结果表明PPD学习控制器在系统快速响应性方面优于常规PD控制器。PPD学习控制器为机器人控制系统提供了一种新的途径。     

基于自适应反演的超细金刚线张力控制系统

针对多线切割机张力控制系统存在库仑摩擦和摆角耦合等较强非线性特征,以及收放卷轮直径变化引起的参数不确定问题,提出了一种基于自适应反演的非线性补偿控制方法。该方法结合实验辨识和自适应参数控制器设计方法分别对多线切割机的多轴电机同步运动控制系统和不确定系统参数进行简化分析和在线估计,使用李雅普诺夫稳定性理论保证了系统全局渐近稳定性以及系统状态的有界性。仿真和实验结果表明,所设计的自适应控制器可以实现多轴同步运动,并将张力摆角控制在较小的范围内,获得更高的张力控制精度。     

混合输入机构的模糊自适应变结构控制

针对混合输入机构中常速电机可不可控的特点,提出了基于常速电机位置跟踪的控制策略来对伺服电机进行控制,对常速电机的速度波动进行补偿,并给出了控制框图。因为系统的精确动力学模型难以获得,故考虑系统参数的不确定、外部扰动和非线性摩擦,设计了模糊自适应滑模变结构控制器以实现混合输入机构的轨迹跟踪。应用模糊自适应推理逼近系统的不确定之和,从而得到连续的控制增益,消除了变结构控制的抖振。     

Global finite-time adaptive control for uncalibrated robot manipulator based on visual servoing

The paper addresses the finite-time convergence problem of a uncalibrated camera-robot system with uncertainties. These uncertainties include camera extrinsic and intrinsic parameters, robot dynamics and feature depth parameters, which are all considered as time-varying uncertainties. In order to achieve a better dynamic stability performance of the camera-robot system, a novel FTS adaptive controller is presented to cope with rapid convergence problem. Meanwhile, FTS adaptive laws are proposed to handle these uncertainties which exist both in robot and in camera model. The finite-time stability analysis is discussed in accordance with homogeneous theory and Lyapunov function formalism. The control method we proposed extends the asymptotic stability results of visual servoing control to a finite-time stability. Simulation has been conducted to demonstrate the performance of the trajectory tracking errors convergence under control of the proposed method.     

基于径向基神经网络的直线超声电机位置控制

针对直线超声电机的精密位置控制,提出了一种基于径向基神经网络的自适应控制机制。鉴于直线超声电机工作原理,其运行状态必然受到摩擦、强非线性和时变等不确定性因素的干扰,为了对这些不确定性因素进行有效的逼近,采用了径向基神经网络。为了提高控制机制的自适应能力,首先利用来自试验数据的训练样本按正交最小二乘算法确定径向基神经网络的隐层单元的个数和相关参数,再按递推最小二乘法在线调整隐层与输出层之间的权重。试验结果表明,基于径向基神经网络的自适应控制器的性能不仅优于传统的PID控制和误差反向传播神经网络控制,而且具有很好的抗干扰能力。     

Active disturbance rejection based trajectory linearization control for hypersonic reentry vehicle with bounded uncertainties

This paper investigates a novel compound control scheme combined with the advantages of trajectory linearization control (TLC) and alternative active disturbance rejection control (ADRC) for hypersonic reentry vehicle (HRV) attitude tracking system with bounded uncertainties. Firstly, in order to overcome actuator saturation problem, nonlinear tracking differentiator (TD) is applied in the attitude loop to achieve fewer control consumption. Then, linear extended state observers (LESO) are constructed to estimate the uncertainties acting on the LTV system in the attitude and angular rate loop. In addition, feedback linearization (FL) based controllers are designed using estimates of uncertainties generated by LESO in each loop, which enable the tracking error for closed-loop system in the presence of large uncertainties to converge to the residual set of the origin asymptotically. Finally, the compound controllers are derived by integrating with the nominal controller for open-loop nonlinear system and FL based controller. Also, comparisons and simulation results are presented to illustrate the effectiveness of the control strategy.     

μ理论在柔顺力控制中的应用

研究μ理论在柔顺力控制系统中的应用。为模拟空间对接强制校正阶段的推出和拉近过程,提出基于6自由度并联机器人位置内环的柔顺力控制策略。描述基于位置内环的柔顺力控制系统串级控制结构,阐述用经典控制策略实现柔顺力控制的方法。综合考虑参数变化、模型变动和外来干扰等不确定性,利用μ综合控制理论设计鲁棒力控制器。给出鲁棒力控制系统回路中加权函数的详细选取方法和鲁棒力控制器的设计过程。通过μ分析比较鲁棒力控制器和经典力控制器的鲁棒稳定性和鲁棒性能。通过鲁棒力控制器和经典力控制器进行柔顺力控制试验,结果表明了所设计鲁棒力控制器的有效性和优越性。     

Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems

In this paper, robust and adaptive nonsingular fast terminal sliding-mode (NFTSM) control schemes for the trajectory tracking problem are proposed with known or unknown upper bound of the system uncertainty and external disturbances. The developed controllers take the advantage of the NFTSM theory to ensure fast convergence rate, singularity avoidance, and robustness against uncertainties and external disturbances. First, a robust NFTSM controller is proposed which guarantees that sliding surface and equilibrium point can be reached in a short finite-time from any initial state. Then, in order to cope with the unknown upper bound of the system uncertainty which may be occurring in practical applications, a new adaptive NFTSM algorithm is developed. One feature of the proposed control law is their adaptation techniques where the prior knowledge of parameters uncertainty and disturbances is not needed. However, the adaptive tuning law can estimate the upper bound of these uncertainties using only position and velocity measurements. Moreover, the proposed controller eliminates the chattering effect without losing the robustness property and the precision. Stability analysis is performed using the Lyapunov stability theory, and simulation studies are conducted to verify the effectiveness of the developed control schemes.     

可重构机器人体系结构及模块化控制系统的实现

本文提出了一种适用于新型可重构星球机器人的模块化控制系统,根据机构和运动特性,基于CAN总线和分布式控制器技术,将系统结构和功能分解成不同模块由各自的控制器独立执行,建立具有任务层和运动层的分层次控制结构,实现了组合式规划、分布式控制的混合式控制方法.本文设计了两种不同的控制器,并采用PPG脉冲宽度调节方法实现了对在机器人上使用的R/C电机的标定和控制.通过在子机器人原理样机上进行实验,验证了这套控制系统和控制体系结构的可行性.     

Wall-following control of a two-wheeled mobile robot

Wall-following control problem for a mobile robot is to move it along a wall at a constant speed and keep a specified distance to the wall.This paper proposes wall-following controllers based on Lyapunov function candidate for a two-wheeled mobile robot (MR) to follow an unknown wall. The mobile robot is considered in terms of kinematic model in Cartesian coordinate system. Two wall-following feedback controllers are designed: full state feedback controller and observer-based controller. To design the former controller, the errors of distance and orientation of the mobile robot to the wall are defined, and the feedback controller based on Lyapunov function candidate is designed to guarantee that the errors converge to zero asymptotically. The latter controller is designed based on Busawon’s observer as only the distance error is measured. Additionally, the simulation and experimental results are included to illustrate the effectiveness of the proposed controllers.     

蛇形机器人跟踪误差预测的自适应轨迹跟踪控制器

为了满足蛇形机器人轨迹跟踪运动的精度需要,消除外界干扰对机器人跟踪误差的影响,提出了一种蛇形机器人跟踪 误差预测的自适应轨迹跟踪控制器。 所提出的控制器实现了机器人干扰变量、摩擦系数和控制参数的预测,并用预测值和虚拟 控制函数来补偿系统的控制输入,抵消了蛇形机器人在轨迹跟踪过程中的侧滑角,避免了干扰变量对机器人带来的负面影响, 提高了轨迹跟踪的误差稳定性与控制精度。 在建立蛇形机器人模型后,利用积分形式的侧滑角补偿项改进了视线法,并设计了 蛇形机器人的自适应轨迹跟踪控制器。 使机器人的位置误差在 10 s 内实现收敛,角度误差小于 0. 03 rad,预测值误差在 5 s 内 收敛。 通过仿真实验,验证了所提出的控制器的有效性和优越性。