基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法

设计了一种基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法.在机器人与人体上肢接触面安装力传感器采集人机交互力矩信息作为量化的主动运动意图,设计了一种无模型自适应滤波算法使交互力矩变得平滑而连贯;以人机交互力矩为输入,综合考虑机器人末端点与参考轨迹的相对位置和补偿力的信息,设计了人机交互阻抗控制器,用于调节各关节的给定目标速度;设计了将无模型自适应与离散滑模趋近律相结合的速度控制器完成机器人各关节对目标速度的跟踪.仿真结果表明,该控制方法可以实现外骨骼式上肢康复机器人辅助患者完成主动交互训练的功能.通过调节人机交互阻抗控制器的相应参数,机器人可以按照患者的运动意图完成不同的主动交互训练任务,并在运动出现偏差时予以矫正.控制器在设计实现过程中不要求复杂准确的动力学建模和参数识别,并有一定的抗干扰性和通用性.     

基于模糊神经网络水下机器人直接自适应控制

提出了基于广义动态模糊神经网络的水下机器人直接自适应控制方法, 该控制方法既不需要预先知道模糊神经结构, 也不需要预先的训练阶段, 完全通过在线自适应学习算法构建水下机器人的逆动力学模型. 首先, 本文提出了基于这种网络结构的水下机器人直接自适应控制器, 然后, 利用 Lyapunov 稳定理论, 证明了基于该控制器的水下机器人控制系统闭环稳定性, 最后, 采用某水下机器人模型仿真验证了该控制方法的有效性.     

自适应神经模糊推理结合PID控制的并联机器人控制方法

针对6自由度液压驱动并联机器人的精确控制问题,提出一种结合自适应神经模糊推理系统(ANFIS)和比例积分微分(PID)控制的机器人控制方法。首先,利用浮动坐标系描述法(FFRF)来模拟机器人柔性组件,并构建并联机器人的拉格朗日动力学模型。然后,根据模糊推理中的模糊规则来自适应调整PID控制器参数。最后,利用神经自适应学习算法使模糊逻辑能计算隶属度函数参数,从而使模糊推理系统能追踪给定的输入和输出数据。将该控制器与传统PID控制器、模糊PID控制器进行比较,结果表明,ANFIS自整定PID控制器大大减小了末端器位移误差,能很好的控制并联机器人末端机械手的运动。     

基于动力学与控制统一模型的蛇形机器人速度跟踪控制方法研究

对带有被动轮的蛇形机器人进行速度跟踪控制时,利用传统的动力学建模方法得到的动力学方程复杂且不利于控制器的设计. 本文基于微分几何的方法将带有被动轮的蛇形机器人动力学投影到速度分布空间中, 得到了动力学与控制统一模型, 更有利于速度跟踪控制器的设计. 考虑到蛇形机器人在进行速度跟踪时容易出现奇异位形, 提出增加头部扰动速度的方法. 基于头部扰动速度和统一模型, 提出避免奇异位形的速度跟踪控制方法, 最后通过逆向动力学得到控制力矩. 文中对速度跟踪控制进行了数值仿真和实验验证. 仿真和实验结果表明, 提出的速度跟踪控制方法能够跟踪想要方向的速度, 并且在跟踪过程中可以有效地避免奇异位形.     

软体机械臂水下自适应鲁棒视觉伺服

水下仿生软体机器人在水底环境勘测,水下生物观测等方面具有极高的应用价值.为进一步提升仿章鱼臂软体机器人在特殊水下环境中控制效果,提出一种自适应鲁棒视觉伺服控制方法,实现其在干扰无标定环境中的高精度镇定控制.基于水底动力学模型,设计保证动力学稳定的控制器;针对柔性材料离线标定过程繁琐、成本高,提出材料参数自适应估计算法;针对水下特殊工作条件,设计自适应鲁棒视觉伺服控制器,实现折射效应的在线补偿,并通过自适应未知环境干扰上界,避免先验环境信息的求解.所提算法在软体机器人样机中验证其镇定控制性能,为仿生软体机器人的实际应用提供理论基础.     

人机不确定条件下康复步行训练机器人的部分记忆迭代学习限速控制

为了提高康复步行训练机器人的跟踪精度及安全性,提出了一种带有运动速度约束和部分记忆信息的自适应迭代学习控制方法,目的是抑制人机不确定性及速度突变对系统跟踪性能的影响.在考虑人机不确定性的基础上,建立了康复步行训练机器人的动力学模型.提出了基于模型预测的速度约束方法,通过限制每个轮子的运动速度,约束了机器人的实际运动速度.进一步,利用受约束的运动速度建立了动力学跟踪误差系统,提出了具有部分记忆信息的自适应迭代学习控制器设计方法,并验证了跟踪误差系统的稳定性.仿真对比分析和实验研究结果表明,文中提出的控制方法能抑制人机不确定性并使康复者在安全速度下完成步行训练.     

移动机器人编队的递归模糊神经网络滑模控制

研究非完整移动机器人编队控制优化问题,由于动态模型存在诸多不稳定性,针对领航者-跟随者l-ψ控制结构,提出了一种Back stepping运动学控制器与自适应神经滑模控制器相结合的新型控制策略.采用动态递归模糊神经网络(dynam-ic recurrent fuzzy neural network,DRFNN)对跟随者及领航者动力学非线性不确定部分进行在线估计,并通过自适应鲁棒控制器对神经网络建模误差进行补偿.所提方法不但解决了移动机器人编队控制的参数与非参数不确定性问题,同时也确保了机器人编队在期望队形下对指定轨迹的跟踪;根据Lyapunov方法的设计过程,保证了控制系统的稳定;仿真结果表明了改进方法对机器人编队优化控制的有效性.     

基于自适应反步法的轮式移动机器人跟踪控制

轮式移动机器人是一种典型的非完整约束系统.基于反步法提出一种自适应扩展控制器,对含有未知参数的非完整轮式移动机器人动力学系统进行轨迹跟踪控制并且Lyapunov稳定性理论保证跟踪误差渐近收敛到零.为了克服速度跳变产生滑动,加入了神经动力学模型对控制器进行改进.以两驱动轮移动机器人为例,利用运动学自适应控制器设计出转矩控制器,有效解决了不确定非完整轮式移动机器人动力学系统的轨迹跟踪问题.仿真结果证明该方法的正确性和有效性.     

基于分层控制策略的六轮滑移机器人横向稳定性控制

六轮野外机器人通常体积庞大,难以建立其动力学模型.采用传统的速度控制方法很难保证机器人的横向稳定性.为解决这一问题,开展基于分层控制策略的六轮滑移机器人横向稳定性控制研究.首先分析整车受力情况,建立六轮滑移机器人的动力学模型.其次,设计基于分层控制策略的动力学控制器,其中上层为基于改进趋近律的滑模控制器,实现对期望横摆角速度的跟踪;下层为基于附着率最优的转矩分配控制器,该控制器可以保证机器人行驶的横向稳定性.最后,在不同工况下进行仿真实验,并搭建实验平台进行实物测试.结果表明设计的控制器可以有效提高机器人的横向稳定性.     

未校正摄像机的非完整机器人的自适应镇定

针对基于未校准顶置摄像机的非完整移动机器人视觉伺服镇定问题,首先从标准机器人运动学模型以及视觉空间与工作空间的转换得到视觉平面上的机器人运动学模型,而后根据视觉空间的运动学的速度误差以及视觉空间的机器人的动力学模型设计了一个自适应控制器,而且控制器具有鲁棒性,控制器中的鲁棒项函数用以抑制动力学的扰动,摄像机估计值用以估计未知的摄像机参数,动力学的惯性参数估计值用以消除动力学参数的不确定性.控制系统的稳定性以及参数估计值的有界性由李雅普诺夫定理证明.仿真结果用于说明控制律的有效性.     

Sliding Mode Neuro Adaptive Control in Trajectory Tracking for Mobile Robots

In this work a neural indirect sliding mode control method for mobile robots is proposed. Due to the nonholonomic property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate the dynamics of the robot. Using an online adaptation scheme, a neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown nonlinear dynamics. The proposed design simultaneously guarantees the stability of the adaptation of the neural nets and obtains suitable equivalent control when the parameters of the robot model are unknown in advance. The robust adaptive scheme is applied to a mobile robot and shown to be able to guarantee that the output tracking error will converge to zero.     

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.

     

Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm

This paper proposes a TSK-type recurrent neuro fuzzy system (TRNFS) and hybrid algorithm- GA_BPPSO to develop a direct adaptive control scheme for stable path tracking of mobile robots. The TRNFS is a modified model of the recurrent fuzzy neural network (RFNN) to obtain generalization and fast convergence. The TRNFS is designed using hybridization of genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO), called GA_BPPSO. For the tracking control of mobile robot, two TRNFSs are designed to generate the control inputs by direct adaptive control scheme and hybrid algorithm GA_BPPSO. Through simulation results, we demonstrate the effectiveness of our proposed controller.     

Velocity control of wheeled mobile robots using computed torque control and its performance for a differentially driven robot

This article presents the development of a Computed Torque Control (CTC) scheme for Cartesian velocity control of Wheeled Mobile Robots (WMRs). The literature presents extensive study on the need and suitability of the CTC scheme for robot arms. Many researchers have identified the benefits of using a CTC scheme for mobile robots. There is however very little information on CTC schemes for controlling mobile robots. The need for the CTC scheme stems from the fact that mobile robots (industrial AGVs) employing conventional velocity control schemes experience side slip due to suspended loads while negotiating curves, and the controller gains and accelerations need to be modified for changes in the dynamics. The structure of the proposed control scheme can be employed to control any mobile robot for which an inverse dynamic model exists, as a CTC scheme requires an inverse dynamic model to compute unique values for the motor current for a given trajectory. It is demonstrated that the existence of the inverse dynamic model is guaranteed for all differentially driven WMRs for all operating conditions, and is not affected by the number of castor wheels in the WMR. In the proposed CTC scheme, the linear and angular velocities of the WMR are controlled by adjusting the WMR accelerations, which are computed based on the motor torques required to follow a given trajectory. The motor torque is pre-computed based on a dynamic model of the mobile robotic system. The simulation and experimental results presented for a differentially driven WMR demonstrate that a computed-torque control scheme provides adaptive cruising and steering control for nominally tuned controller gains compared to a conventional velocity controller to achieve proper road following in the presence of changes in the dynamics. © 1997 John Wiley & Sons, Inc.     

Tracking control of a nonholonomic mobile robot using compound cosine function neural networks

The purpose of this paper is to propose a compound cosine function neural network with continuous learning algorithm for the velocity and orientation angle tracking control of a nonholonomic mobile robot with nonlinear disturbances. Herein, two neural network (NN) controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the adaptive control of the mobile robot. The neuron function of the hidden layer in the three-layer feed-forward network structure is on the basis of combining a cosine function with a unipolar sigmoid function. The developed neural network controllers have simple algorithm and fast learning convergence because the weight values are only adjusted between the nodes in hidden layer and the output nodes, while the weight values between the input layer and the hidden layer are one, i.e. constant, without the weight adjustment. Therefore, the main advantages of this control system are the real-time control capability and the robustness by use of the proposed neural network controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances which are considered as dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of nonholonomic mobile robots has real-time control capability, better robustness and higher control precision. The compound cosine function neural network provides us with a new way to solve tracking control problems for mobile robots.     

A system decomposition method for region tracking control of a non-holonomic mobile robot with dynamic parameter uncertainties

The tracking control problem of non-holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed-point tracking. This technical note focuses on the region tracking control for a non-holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non-holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non-holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed-point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed-point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy.     

Adaptive critic motion control design of autonomous wheeled mobile robot by dual heuristic programming

Autonomous wheeled mobile robot (WMR) needs implementing velocity and path tracking control subject to complex dynamical constraints. Conventionally, this control design is obtained by analysis and synthesis or by domain expert to build control rules. This paper presents an adaptive critic motion control design, which enables WMR to autonomously generate the control ability by learning through trials. The design consists of an adaptive critic velocity control loop and a self-learning posture control loop. The neural networks in the velocity neuro-controller (VNC) are corrected with the dual heuristic programming (DHP) adaptive critic method. Designer simply expresses the control objective by specifying the primary utility function then VNC will attempt to fulfill it through incremental optimization. The posture neuro-controller (PNC) learns by approximating the specialized inverse velocity model of WMR so as to map planned positions to suitable velocity commands. Supervised drive supplies variant velocity commands for PNC and VNC to set up their neural weights. During autonomous drive, while PNC halts learning VNC keeps on correcting its neural weights to optimize the control performance. The proposed design is evaluated on an experimental WMR. The results show that the DHP adaptive critic design is a useful base of autonomous control.     

Adaptive unified motion control of mobile manipulators

This paper presents a unified motion controller for mobile manipulators which not only solves the problems of point stabilization and trajectory tracking but also the path following problem. The control problem is solved based on the kinematic model of the robot. Then, a dynamic compensation is considered based on a dynamic model with inputs being the reference velocities to the mobile platform and the manipulator joints. An adaptive controller for on-line updating the robot dynamics is also proposed. Stability and robustness of the complete control system are proved through the Lyapunov method. The performance of the proposed controller is shown through real experiments.     

Robust adaptive neural network control for environmental boundary tracking by mobile robots

The paper addresses the problem of environmental boundary tracking for the nonholonomic mobile robot with uncertain dynamics and external disturbances. To do environmental boundary tracking, a reference velocity is designed for the nonholonomic mobile robot. In this paper, a radial basis function neural network (NN) is used to approximate a nonlinear function containing the uncertain model terms and the elements of the Hessian matrix of the environmental concentration function. Then, the NN approximator is combined with a robust control to construct a robust adaptive NN control for the mobile robot to track the desired environment boundary. It is proved that the tracking error can be guaranteed to converge to zero in the ultimate. Simulation results are presented to illustrate the stability of the robust adaptive control. Copyright © 2011 John Wiley & Sons, Ltd.     

Vision-based path following by using a neural network Guidance System

This article describes a neural network controller for guidance of a robot arm, used to model some aspects of autonomous vehicle technology. The controller uses video images with adaptive view-angles for the sensory input, and the system was configured to simulate an autonomous vehicle guidance system on a flat terrain using a high-contrast guiding path. To demonstrate the feasibility of using neural networks in this type of application, an Intelledex 405 robot fitted with a video camera and associated vision system was used. Phase I of the project consisted of a single-speed implementation and limited network training. Phase II featured a multi-speed implementation using adaptively varied view-angles based on robot arm velocity. It was shown that the neural network controller was able to control the robot arm along a path composed of path segments unlike those with which it was trained. In addition it was shown that a multi-speed implementation with adaptive view angles improved system performance. © 1994 John Wiley & Sons, Inc.