平面八连杆和九连杆机械臂关节角无偏差运动规划验证

基于平面三连杆,PUMA560,PA10等机械臂的计算机仿真结果证实了二次型性能指标方案用于冗余机械臂关节角无偏差运动规划的有效性。为了进一步验证该二次型性能指标优化方案,本文以高度冗余的平面八连杆和九连杆机械臂为例进行计算机仿真验证,仿真结果证实了该方案对解决高度冗余平面机械臂的关节角偏差问题是可行且有效的。     

Framing processes in public commentary on US federal tobacco control regulation

Governmental health policy officials increasingly seek to promote public participation in expert and technical decisions regarding health regulation. The issues of what r?le the public plays in regulatory processes, and how health policy officials orient to public opinion, matter especially in the US context, where public commentary is a requisite component of federal rule-making. This paper examines written commentary as one specific, institutionalized form of public participation. To understand the relationship between scientific evidence and public commentary in public health regulation, we examine a US Food and Drug Administration (FDA) regulation, proposed in 1995, to restrict the promotion and sale of tobacco products to minors. We use recent work on collective action frames to analyse how the FDA framed the proposed regulation as a rational, science-based policy; the discursive practices employed in the public commentary either to embrace or to contest the FDA's framing; and how the agency presented the final version of its rule as responsive to that public commentary. Our content analyses reveal a significant disparity between the FDA's emphasis on scientific evidence and the commentators' emphases on political and ideological frames, which we analyse in terms of counter-framing. An orientation to the dynamics of framing and counter-framing contributes to an understanding of the relationship between scientific evidence and public commentary in the formulation of controversial health policy regulations.     

Adaptive backstepping trajectory tracking control of robot manipulator

An adaptive backstepping control scheme is proposed for task-space trajectory tracking of robot manipulators in the presence of uncertain parameters and external disturbances. In the case of external disturbance-free, the developed controller guarantees that the desired trajectory is globally asymptotically followed. Moreover, taking disturbances into consideration, the controller is synthesized by using adaptive technique to estimate the system uncertainties. It is shown that L₂ gain of the closed-loop system is allowed to be chosen arbitrarily small so as to achieve any level of L₂ disturbance attenuation. The associated stability proof is constructive and accomplished by the development of a Lyapunov function candidate. Numerical simulation results are included to verify the control performance of the control approach derived.     

Task-space control for industrial robot manipulators with unknown inner loop control architecture

The operational space control of a robot manipulator using external sensors requires stabilizing the compound system {external sensors - outer controller - inner controller - robot manipulator}. The user must access the inner controller to reshape it to achieve this stabilization. Due to intellectual property protection purposes, most industrial robots have an unknown or inaccessible inner controller. Therefore, it is tricky to design a stable control scheme. To solve this problem, an adaptive radial basis function neural network (RBF NN) outer controller is proposed, which approximates the inner controller’s dynamics to eliminate its effect in the closed-loop. An inherent property for RBF NN is used to reduce the number of adaptive parameters. Since this technique introduces approximation errors, it is included in the control scheme, a term that constrains the system to converge rapidly to the performances prescribed by the user. It is proved that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) through Lyapunov theory. The effectiveness of the proposed approach is verified through simulation comparisons and experimental studies.     

基于arm的家用清洁机器人的控制系统设计

文章提出了一种基于ARM处理器的家用清洁机器人控制系统,包括硬件设计和软件设计。硬件部分包括主控器模块、电源及欠压检测模、传感器模块、人机交互模块、驱动模块以及清洁模块。软件部分设计了清洁机器人的主要控制流程。该控制系统以通用性和功能完备为目标,使机器人具备智能清洁能力。     

Continuous output feedback sliding mode control for underactuated flexible-joint robot

The tracking control based on output feedback for a category of flexible-joint robot (FJR) systems is investigated in this brief. Control performance of the systems is inevitably bearing the brunt of various unknown time-varying disturbances, which can be categorized to be matched and mismatched and generally cover internal parameter uncertainties, couplings, unmodelled dynamics, and external load or changing operating environments. To cope with these disturbances, the mismatched disturbances are first transferred to the matched ones by a flatness method, which eliminates the computational cost of estimating mismatched disturbances. Then, a generalized proportional integral observer (GPIO) is constructed to estimate the unavailable states and disturbances. By integrating the estimated disturbance and states provided by the GPIO, a novel dynamic sliding surface is constructed. Finally, a continuous sliding mode control (CSMC)-based output feedback control framework is further designed. The presented control strategy only requires link position information and is continuous, which can effectively reduce the chattering driven by the high-frequency switching item in the traditional SMC method. Asymptotic convergence of output tracking error is guaranteed by theoretical analysis under some mild conditions. Comparative tests on a two-link FJR verify the claimed control performance.     

A novel adaptive robust control approach for underactuated mobile robot

Underactuated mobile robot (UMR) is a typical nonlinear underactuated system with nonholonomic and holonomic constraints. Based on the model of UMR, we propose a novel adaptive robust control to control the UMR and compensate the uncertainties from the view of constraint-following. The uncertainties, which are (possibly fast) time-varying and bounded, include modeling error, initial condition deviation, friction force and other external disturbances. However, the bounds are unknown. To estimate the bounds of the uncertainties, we design an adaptive law which is of leakage type. The uniform boundedness and the uniform ultimate boundedness of the proposed control are verified by Lyapunov method. Furthermore, the effectiveness of the control is shown via numerical simulation of a case.     

Adaptive event-triggered control for uncalibrated visual servoing of robot manipulators

In the study, an adaptive event-triggered control strategy is proposed for image-based visual servoing of eye-to-hand robot manipulators, where the camera used does not need to be calibrated, and the dynamics behavior of manipulator is considered in design and analysis. To address the uncertainty in camera parameters and the nonlinearity in robot dynamics, and accommodate the errors between the real-time control signals and the piecewise-constant event-triggered control signals, a new and novel robust adaptive estimation approach is developed, and well fused with visual feedback control design so that the boundedness of all closed-loop signals, and the asymptotic convergence of image error towards zero are established simultaneously. Besides rigorous proof in theory, the obtained results are also confirmed by comparative simulation tests.     

Design of switching path-planning control for obstacle avoidance of mobile robot

Generally speaking, the mobile robot is capable of sensing its surrounding environment, interpreting the sensed information to obtain the knowledge of its location and the environment, and planning a real-time trajectory to reach the object. In this process, the issue of obstacle avoidance is a fundamental topic to be challenged. Thus, a switching path-planning control scheme is designed without detailed environmental information, large memory size, and heavy computation burden in this study for the obstacle avoidance of a mobile robot. In this scheme, the robot can gradually approach its object according to the motion tracking mode, obstacle avoidance mode, self-rotation mode, and robot state selection designed by learning and expert rules for enhancing the tracking speed and adapting to different environments. The effectiveness of the proposed adaptive path-planning control scheme is verified by numerical simulations and experimental results of a differential-driving mobile robot under the possible occurrence of obstacle shapes.     

Scientific coats of arms

With their mythical creatures and arcane symbolism, coats of arms seem to have little connection with modern science. Yet despite its chivalric origins, the ancient language of heraldry has long fascinated famous scientists. Although this idiosyncratic tradition was parodied by Victorian geologists, who laughingly replaced unicorns and griffins with images of dinosaurs that they had recently discovered, it has been perpetuated since by Ernest Rutherford, who liked to present himself as a new alchemist.     

Observer-Based coverage control of unicycle mobile robot network in dynamic environment

This paper studies the coverage control problem of unicycle mobile robot network with external disturbance in the dynamic environment. The environment model is described by a time-varying density function, which is not known by the robot network. An observation method is proposed to approximate the unknown density function. It is proved that the density approximated by the robot network converge to the real density and the consensus of coefficient vector is realized in the robot network. Based on the approximated density function, a robust coverage control is successfully designed to drive the unicycle robot network to the optimal configuration and the coverage of the task region is optimized. Finally, the effectiveness of observation method and robust control are shown by simulation results.     

Robust fault tolerant control of robot manipulators with global fixed-time convergence

In this paper, a robust fault tolerant control, which provides a global fixed-time stability, is proposed for robot manipulators. This approach is constructed based on an integration between a fixed-time second-order sliding mode observer (FxTSOSMO) and a fixed-time sliding mode control (FxTSMC) design strategy. First, the FxTSOSMO is developed to estimate the lumped disturbance with a fixed-time convergence. Then, based on the obtained disturbance estimation, the FxTSMC is developed based on a fixed-time sliding surface and a fixed-time reaching strategy to form a global fixed-time convergence of the system. The proposed approach is then applied for fault tolerant control of a PUMA560 robot and compared with other state-of-the-art controllers. The simulation results verify the outstanding fault estimation and fault accommodation capability of the proposed fault diagnosis observer and fault tolerant strategy, respectively.     

Neural network adaptive control design for robot manipulators under velocity constraints

This paper studies the neural adaptive control design for robotic systems with uncertain dynamics under the existence of velocity constraints and input saturation. The control objective is achieved by choosing a control Lyapunov function using joint error variables that are restricted to linear growth and furthermore by introducing a secant type barrier Lyapunov function for constraining the joint rate variables. The former is exploited to bind the forward propagation of the position errors, and the latter is utilized to impose hard bounds on the velocity. Effective input saturation is expressed, and neural networks are employed to tackle the uncertainty problem in the system dynamics. Feasibility conditions are formulated, and the optimal design parameters are obtained by solving the constrained optimization problem. We prove that under the proposed method, semi-global uniform ultimate boundedness of the closed-loop system can be guaranteed. Tracking errors meanwhile converge to small neighborhoods of the origin, and violations of predefined velocity constraints are avoided. Finally, numerical simulations are performed to verify the effectiveness of the theoretical developments.     

Velocity field control of robot manipulators by using only position measurements

In the velocity field control approach the robot motions are specified through a vectorial function that assigns the desired velocity to each point of the configuration space. In other words, a velocity field defines the robot desired velocity in the operational space as a function of its current position. In this paper is introduced a new algorithm to solve the velocity field control formulation in the robot operational space. The proposed approach assumes only joint position measurements and is based on a hierarchical structure that results of using the kinematic control concept and a joint velocity controller. To estimate the joint velocity, nonlinear filtering of the joint position is used.     

漂浮基空间机器人的基于模糊神经网络的自适应补偿控制

针对自由漂浮状态的空间机器人模型不确定性及其动力传动机构的摩擦死区非线性,将一种自适应模糊小脑模型关联控制( FCMAC)补偿策略用于轨迹跟踪及补偿问题.利用模糊神经网络并引入GL矩阵及其乘法算子“.”分别对执行机构中的摩擦死区及系统模型不确定部分进行自适应补偿,其补偿误差及外界扰动通过滑模控制器来消除.基于Lyapunov理论证明了闭环系统跟踪误差的有界性.仿真表明控制器可以达到较高精度,且能满足实时性要求.     

Udwadia-Kalaba approach based distributed consensus control for multi-mobile robot systems with communication delays

In this paper, a distributed consensus algorithm for multi-mobile robot systems (MMRSs) with communication delays is proposed based on the Udwadia-Kalaba (UK) approach. The key feature of the proposed algorithm is that the consensus requirement is configured as a second-order constraint, and then a concise and explicit equation of motion for the constrained mechanical systems is formulated. Furthermore, the necessary and sufficient conditions for achieving the consensus of MMRSs with or without communication delays are developed under the network topology possessing a directed spanning tree. Finally, some numerical simulations are performed to verify the validity of the proposed consensus algorithm.     

Decentralized neural identification and control for uncertain nonlinear systems: Application to planar robot

This paper presents a discrete-time decentralized neural identification and control for large-scale uncertain nonlinear systems, which is developed using recurrent high order neural networks (RHONN); the neural network learning algorithm uses an extended Kalman filter (EKF). The discrete-time control law proposed is based on block control and sliding mode techniques. The control algorithm is first simulated, and then implemented in real time for a two degree of freedom (DOF) planar robot.