Adaptive Jacobian tracking control of rigid-link electrically driven robots based on visual task-space information

This paper studies stable adaptive tracking control of rigid-link electrically driven robot manipulators in the presence of uncertainties in kinematics, manipulator dynamics, and actuator dynamics. A new task-space control method using visual task-space information is proposed to overcome the uncertainties adaptively. Accelerations measurements are avoided in the control voltage inputs by constructing observers to specify desired armature currents. Simulation results illustrate the performance of the proposed control method.     

Repetitive control of electrically driven robot manipulators

This article presents a novel robust discrete repetitive control of electrically driven robot manipulators for tracking of a periodic trajectory. We propose a novel model, which presents the highly non-linear dynamics of robot manipulator in the form of linear discrete-time time-varying system. Based on the proposed model, we develop a two-term control law. The first term is an ordinary time-optimal and minimum-norm (TOMN) control by employing parametric controllers to guarantee stability. The second term is a novel robust control to improve the control performance in the face of uncertainties. The robust control estimates and compensates uncertainties including the parametric uncertainty, unmodelled dynamics and external disturbances. Performance of the proposed method is compared with two discrete methods, namely the TOMN control and an adaptive iterative learning (AIL) control. Simulation results confirm superiority of the proposed method in terms of the convergence speed and precision.     

Adaptive Output Feedback Control of Flexible-Joint Robots Using Neural Networks: Dynamic Surface Design Approach

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.     

Robust adaptive neural tracking control for a class of electrically driven robots with time delays

This article addresses the problem of designing the robust tracking control for a class of uncertain electrically driven robots with time delays. The unknown time-delay uncertainty is assumed to be bounded by a function of all the state variables. By suitably choosing the Lyapunov–Krasovskii functionals, a novel adaptive/robust neural tracking control scheme is developed for the first time such that all the states and signals of the closed-loop time-delay robot system are bounded and the tracking error is shown to be uniformly ultimately bounded. By suitably designing the embedded current signal, the effect of time-delay uncertainty in the mechanical dynamics does not require to be incorporated into the current tracking error dynamics, and so the Lyapunov–Krasovskii functionals can be easily constructed in the stability analysis. Compared with the previous investigations of controlling robots the control scheme developed here can be extended to handle a broader class of electrically driven robots perturbed simultaneously by plant uncertainties, time-varying perturbations, and time-delay uncertainties. Finally, simulation examples are made to demonstrate the effectiveness of the proposed control algorithm.     

An Intelligent Robust Tracking Control for a Class of Electrically Driven Mobile Robots

This paper addresses the problem of designing robust tracking control for a class of uncertain wheeled mobile robots actuated by brushed direct current motors. This class of electrically‐driven mechanical systems consists of the robot kinematics, the robot dynamics, and the wheel actuator dynamics. Via the backstepping technique, an intelligent robust tracking control scheme that integrates a kinematic controller and an adaptive neural network‐based (or fuzzy‐based) controller is developed such that all of the states and signals of the closed‐loop system are bounded and the tracking error can be made as small as possible. Two adaptive approximation systems are constructed to learn the behaviors of unknown mechanical and electrical dynamics. The effects of both the approximation errors and the unmodeled time‐varying perturbations in the input and virtual‐input weighting matrices are counteracted by suitably tuning the control gains. Consequently, the robust control scheme developed here can be employed to handle a broader class of electrically‐driven wheeled mobile robots in the presence of high‐degree time‐varying uncertainties. Finally, a simulation example is given to demonstrate the effectiveness of the developed control scheme.     

A robust anti-windup control design for electrically driven robots — Theory and experiment

A Robust Anti-Windup Control (RAWC) method is proposed for n-Degree-of-Freedom (DOF) electrically driven robots considering the actuator voltage saturation. The actuator’s saturation is fairly modeled by a smooth nonlinear function and the control design task is developed to avoid windup besides being robust against both model uncertainties and external disturbances. As a major point, the paper also takes into consideration the fact that windup phenomenon can be caused by some strong disturbances. As a result, being robust to external disturbances promises safer situation against windup. The proposed controller needs no saturation output feedback and torque’s measurement for control implementation. The analytical studies as well as the experimental results produced using MATLAB/SIMULINK External Mode Control on a 2-DOF robot manipulator driven by geared Permanent magnet DC motors prove the superiority of the proposed approach.     

Robust formation control of electrically driven nonholonomic mobile robots via sliding mode technique

This paper proposes a sliding mode formation control method for electrically driven nonholonomic mobile robots in the presence of model uncertainties and disturbances. We use the kinematic model based on the leader-following approach for the formation control of multiple robots. Unlike many researches considering only the kinematic model, we also consider the dynamic model including actuator dynamics to obtain the voltage input because it is more realistic to use the voltage as input than the velocity. Then, the sliding mode control method is used to deal with model uncertainties and disturbances acting on the mobile robots. The stability of the proposed control system is proven using Lyapunov stability theory. Finally, we perform computer simulations to demonstrate the performance of the proposed control system.     

不确定机器人的自适应神经网络控制与学习

针对具有未知动态的电驱动机器人,研究其自适应神经网络控制与学习问题.首先,设计了稳定的自适应神经网络控制器,径向基函数(RBF)神经网络被用来逼近电驱动机器人的未知闭环系统动态,并根据李雅普诺夫稳定性理论推导了神经网络权值更新律.在对回归轨迹实现跟踪控制的过程中,闭环系统内部信号的部分持续激励(PE)条件得到满足.随着PE条件的满足,设计的自适应神经网络控制器被证明在稳定的跟踪控制过程中实现了电驱动机器人未知闭环系统动态的准确逼近.接着,使用学过的知识设计了新颖的学习控制器,实现了闭环系统稳定、改进了控制性能.最后,通过数字仿真验证了所提控制方法的正确性和有效性.     

Trajectory tracking control of mobile robots without using longitudinal velocity measurements

We propose a new robust trajectory tracking control scheme for wheeled mobile robots without longitudinal velocity measurements. In the proposed controller, a velocity observer is used to estimate the longitudinal velocity of a wheeled mobile robot. A wheeled mobile robot model, including motor dynamics, is used to develop the controller. The developed controller has the following useful properties. (1) The developed controller does not require any accurate knowledge of the robot parameters or the motor parameters. Even if there are uncertainties in the robot dynamics, including the motor properties, it is certain that tracking errors ultimately become uniformly bounded in a closed-loop system using the developed controller. (2) It is shown theoretically that the ultimate norms of tracking errors can easily be reduced by setting only one design parameter.     

Robust visual servo control of robot manipulators with uncertain dynamics and camera parameters

In this paper, an image-based robust controller is designed to compensate uncertainties with image Jacobian and robot dynamics due to uncertain depth measurement and load variations. The proposed controller with eye-in-hand structure has separate terms to compensate each of uncertainties. The ultimately uniform boundedness of the closed-loop system is proved by the Lyapunov method. The performance of the proposed control system is demonstrated by experimental results of a 5-link robot manipulator with two degree of freedom.     

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.     

Robust tracking enhancement of robot systems including motordynamics: a fuzzy-based dynamic game approach

A robust tracking control design of robot systems including motor dynamics with parameter perturbation and external disturbance is proposed in this study via adaptive fuzzy cancellation technique. A minimax controller equipped with a fuzzy-based scheme is used to enhance the tracking performance in spite of system uncertainties and external disturbance. The design procedure is divided into three steps. At first, a linear nominal robotic control design is obtained via model reference tracking with desired eigenvalue assignment. Next, a fuzzy logic system is constructed and then tuned to eliminate the nonlinear uncertainties as possibly as it can to enhance the tracking robustness. Finally, a minimax control scheme is specified to optimally attenuate the worst-case effect of both the residue due to fuzzy cancellation and external disturbance to achieve a minimax tracking performance. In addition, an adaptive fuzzy-based dynamic game theory is introduced to solve the minimax tracking problem. The proposed method is appropriate for the robust tracking design of robotic systems with large parameter perturbation and external disturbance. A simulation example of a two-link robotic manipulator driven by DC motors is also given to demonstrate the effectiveness of proposed design method's tracking performance     

电驱动刚性机器人的鲁棒神经网络复合控制

采用逐步逆向的设计思想，提出一种新的电驱动刚性机器人轨迹跟踪的鲁棒神经网络复合控制策略，该策略不仅能有效地消除模型不确定性的影响，而且可避免复杂的求导运算以及对关节角加速度可测的要求。给出了控制器的具体组成和神经网络连接权的在线学习算法，理论表明该算法能保证跟踪误差及神经网络连接权估计最终一致有界，仿真结果也验证了算法的有效性。     

基于扰动观测器的机器人自适应神经网络跟踪控制研究

为解决机器人动力学模型未知问题并提升系统鲁棒性,本文基于扰动观测器,考虑动力学模型未知的情况,设计了一种自适应神经网络（Neural network,NN）跟踪控制器.首先分析了机器人运动学和动力学模型,针对模型已知的情况,提出了刚体机械臂通用模型跟踪控制策略;在考虑动力学模型未知的情况下,利用径向基函数（Radial basis function,RBF）神经网络设计基于全状态反馈的自适应神经网络跟踪控制器,并通过设计扰动观测器补偿系统中的未知扰动.利用李雅普诺夫理论证明所提出的控制策略可以使闭环系统误差信号半全局一致有界（Semi-globally uniformly bounded,SGUB）,并通过选择合适的增益参数可以将跟踪误差收敛到零域.仿真结果证明所提出算法的有效性并且所提出的控制器在Baxter机器人平台上得到了实验验证.     

Generalized pole-placement self-tuning controller Part 2, Application to robot manipulator control

A practical application of self-tuning generalized pole placement (GPP) is discussed. The application, which involves the control of a five-axis, electrically actuated robot manipulator, is presented for two reasons. First, it illustrates the performance of a novel neo-classical multi-step predictive self-tuner in an important area of applied research—namely, robot control. Second, since the manipulator in question is electrically driven through a harmonic gearbox, the investigation has a general relevance to the area of self-tuning electromechanical servomechanisms. Two forms of GPP algorithms are compared, one based upon a controlled autoregressive integrated moving average model and the other upon a controlled autoregressive moving average model. The relative merits are discussed in the context of (i) single-input single-output and multiloop robot joint control, (ii) programmed setpoint control, and (iii) the use of the performance tuning aids with which the GPP algorithm is equipped.     

电驱移动机器人多变量固定时间连续编队控制

针对扰动下电驱动非完整移动机器人固定时间编队控制问题,通过引入包含驱动器动力学的领航者-跟随者状态空间动力学模型,分两步对编队控制器进行了设计。对领航者跟随者编队运动学模型进行了多变量固定时间控制设计。在动力学层面,为实现扰动下的速度跟踪,通过辅助输入设计了一种跟随者机器人多变量超螺旋固定时间连续电压控制器。所提算法使机器人编队克服了跟随者机器人所受干扰,确保了跟随者机器人与领航者在固定时间达到期望队形,跟随者在固定时间内跟随期望速度,设计的连续控制消除了开关控制的抖振现象。通过参数设计提前给定系统收敛的固定时间,与系统初始状态无关。基于Lyapunov方法进行了系统稳定性分析。通过仿真对算法进行了验证。     

基于AVR单片机的智能避障小车设计

本文介绍了以ATmega128A单片机作为主控制器,以超声波测距传感器HC-SR04为主要测距元器件,以两驱动的步进电机小车作为主体,设计出智能避障小车系统.步进电机驱动器采用L297和L298构成的混合式步进电机控制器.软件部分采用模块化程序,将距离的测量、避障算法、步进电机控制分为几个子程序,综合后实现智能车的避障功能,获得了较好的效果.     

具有参数不确定性的轮式移动机器人自适应backstepping控制

针对参数不确定的轮式移动机器人的轨迹跟踪问题,设计自适应跟踪控制器.基于移动机器人的动力学模型,采用backstepping积分方法,通过逐步递推选择适当的Lyapunov函数,设计基于状态反馈的自适应控制器,并进行了相应的稳定性分析.与传统PID控制进行仿真对比,结果表明提出的自适应控制策略能较好地补偿系统参数摄动的影响,提高了移动机器人的轨迹跟踪性能和鲁棒性.     

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.

     

A Robust Tracking Controller for Electrically Driven Robot Manipulators: Stability Analysis and Experiment

 In this paper, a robust controller for electrically driven robotic systems is developed. The controller is designed in a backstepping manner. The main features of the controller are: 1) Control strategy is developed at the voltage level and can deal with both mechanical and electrical uncertainties. 2) The proposed control law removes the restriction of previous robust methods on the upper bound of system uncertainties. 3) It also benefits from global asymptotic stability in the Lyapunov sense. It is worth to mention that the proposed controller can be utilized for constrained and nonconstrained robotic systems. The effectiveness of the proposed controller is verified by simulations for a two link robot manipulator and a four-bar linkage. In addition to simulation results, experimental results on a two link serial manipulator are included to demonstrate the performance of the proposed controller in tracking a given trajectory.