DP-based path planning of a spherical mobile robot in an environment with obstacles

A direct approach to path planning of a 2-DOFs (Degrees of Freedom) spherical robot based on Bellman?s dynamic programming (DP) is introduced. The robot moves in an environment with obstacles and employs DP to find optimal trajectory by minimizing energy and preventing obstacle collision. While other path planning schemes rely on pre-planned optimal trajectories and/or feedback control techniques, in this approach there is no need to design a control system because DP yields the optimal control inputs in a closed loop (feedback) form. In other words, after completing the DP table, the optimal control inputs are known for every state in the admissible region and the robot can move toward the final position without colliding with obstacles. This enables the robot to function well in semi- or even non-observable environments. Results from several simulated experiments show that the proposed approach is capable of finding an optimal path from any given position/orientation towards a predefined target in an environment with obstacles within the admissible region. The method is very promising compared to other path planning schemes.     

Finite-time dynamic coverage for mobile sensor networks in unknown environments using neural networks

This paper addresses the finite-time dynamic coverage problem for mobile sensor networks in unknown environments. By introducing a condition where dynamic coverage of all points within the sensing range of each sensor exceeds the desired coverage level by a positive constant, a switching control strategy is developed to guarantee the achievement of desired coverage of the whole mission domain in finite time. The environment is modeled by a density function and neural networks are introduced to learn the function. Due to the approximation capability of neural networks, the proposed control scheme can learn the environment without a priori knowledge on the structure of the density function.     

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.     

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.     

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.     

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.     

动态环境中不同控制方式对创新选择的影响

快速多变的外部环境要求组织通过创新来提高动态竞争力。环境的稳定性有利于组织通过正式控制的方式促进渐进创新,而环境的可预测性有利于组织通过非正式控制的方式促进突变创新。     

Observer-Based adaptive neural inverse optimal consensus control of nonlinear multiagent systems

The objective of this article is to present an adaptive neural inverse optimal consensus tracking control for nonlinear multi-agent systems (MASs) with unmeasurable states. In the control process, firstly, to approximate the unknown state, a new observer is created which includes the outputs of other agents and their estimated information. The neural network is used to reckon the uncertain nonlinear dynamic systems. Based on a new inverse optimal method and the construction of tuning functions, an adaptive neural inverse optimal consensus tracking controller is proposed, which does not depend on the auxiliary system, thus greatly reducing the computational load. The developed scheme not only insures that all signals of the system are cooperatively semiglobally uniformly ultimately bounded (CSUUB), but also realizes optimal control of all signals. Eventually, two simulations provide the effectiveness of the proposed scheme.     

Model predictive tracking control with disturbance compensation for wheeled mobile robots in an environment with obstacles

This paper explores the trajectory tracking control problem for a wheeled mobile robot (WMR) in an environment with obstacles and unknown disturbances. A fixed-time extended state observer is presented, which is utilized to estimate unknown disturbances and improve the convergence speed of estimation errors. By introducing the obstacle avoidance cost, a model predictive controller with disturbance compensation is proposed to guarantee desired tracking performance in the presence of obstacles. The proposed method is analyzed for recursive feasibility and closed-loop system stability subject to unknown disturbances and obstacles. Finally, both simulation and experiment are conducted to express the satisfactory tracking effect of the developed approach.     

移动阅读环境下学术期刊的发展启示

【目的】揭示移动阅读环境下,微博或微信等新媒体形式给学术期刊发展带来的挑战。【方法】结合已有的调查报告,采用比较法,分析了新媒体出现后,伴随受众阅读习惯和阅读需求的改变,学术期刊在服务功能、交流功能、传播功能等方面发生的变化。【结果】移动阅读环境下,学术期刊应尝试利用新技术,尽快创建微信或微博平台,一方面是为了逐步符合新媒体的阅读特性,另一方面,借助移动阅读平台的开放特性,提升期刊服务水平,监督学术规范。同时,建议学术期刊在发展过程中,要注重平台的互动性,避免知识的过度碎片化,并注意版权的保护和管理。【结论】学术期刊的移动数字化建设应尽快深入。     

A robust fast convergence zeroing neural network and its applications to dynamic Sylvester equation solving and robot trajectory tracking

In order to find the theoretical solution of a dynamic Sylvester equation (DSE) in noisy environment, a robust fast convergence zeroing neural network (RFCZNN) is proposed in this paper. Unlike the original zeroing neural network (ZNN) model with existing activation functions (AF), by introducing a new AF, the proposed RFCZNN model guarantees fixed-time convergence to theoretical solution of DSE and robustness against noise simultaneously. The effectiveness and robustness of the proposed RFCZNN model are investigated in theory and demonstrated through simulation results. In addition, its effectiveness and robustness are further verified by a successful robotic trajectory tracking application in noisy environment.     

动态环境下的组织柔性化战略

随着知识经济的到来,企业竞争条件从静态向动态转变。在动态环境下,企业的竞争也应是动态竞争。在分析环境与组织变革的关系基础上,提出组织柔性化趋势的必然性;企业应通过柔性战略来增强组织的柔性,使企业保持竞争优势。     

Dynamic path planning of mobile robot based on improved simulated annealing algorithm

Dynamic path planning for mobile robots is an urgent issue that needs to be solved because of the growing use of mobile robots in daily life and industrial operations. This work focuses on avoiding moving obstacles in dynamic situations. The computational effort required by some current algorithms makes them difficult to utilize for path planning in dynamic situations whilst the computational effort required by other methods makes them simple yet prone to local minima. In this paper, an improved simulated annealing (SA) algorithm for dynamic path planning is proposed. To reduce its computational effort, the initial path selection method and deletion operation are introduced. Simulation results show the improved SA algorithm outperforms other algorithms and provides optimal solutions in static and dynamic environments.     

合作创新全过程知识产权风险控制研究——应用软系统方法建立概念模型

为了应对复杂多变的市场环境,越来越多的企业采取合作创新的模式进行技术创新,但合作创新固有的知识产权风险可能导致企业资源、能力甚至核心能力的流失与扩散。本文在分析了合作创新基本特征及创新过程的基础上,应用软系统方法建立了基于合作创新全过程的知识产权风险控制系统的概念模型。     

论网络信息环境下的顾客忠诚管理

随着网络信息环境的形成和发展，顾客忠诚受到企业的倍加重视，企业只有充分识别顾客忠诚的决定因素并进行有效的管理，才能获得持续竞争优势。首先阐述了网络信息环境下顾客忠诚作用的变化以及对顾客忠诚的决定因素的分析，最后提出了有关顾客忠诚管理的建议。     

Adaptive neural network dynamic surface control of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault

In this paper, the tracking control problem of a class of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault is studied. By using the neural network control approach and dynamic surface control technique, an adaptive neural network dynamic surface control law is designed. Based on the neural network approximator, the uncertain nonlinear dynamics are approximated. Using the dynamic surface control technique, the complexity explosion problems in the design of virtual control laws and adaptive updating laws can be overcome. Moreover, to solve the unknown control direction and unknown actuator fault problems, a type of Nussbaum gain function is incorporated into the recursive design of dynamic surface control. Based on the designed adaptive control law, it can be confirmed that all of the signals in the closed-loop system are semi-global bounded, and the convergence of the tracking error to the specified small neighborhood of the origin could be ensured by adjusting the designing parameters. Finally, two examples are provided to demonstrate the effectiveness of the proposed adaptive control law.     

移动通信市场营销环境研究

针对我国移动通信企业市场营销现状,旨在通过对移动通信企业市场营销活动所处的宏观环境、微观环境的分析和研究,确立在我国目前情况下,各项环境因素的特征和对企业市场营销活动的影响,为移动通信企业在开展市场营销活动时,主动利用环境因素的有利方面、合理规避不利方面,提供理论依据。     

LTE高铁网络覆盖的研究与应用

相比城区网络覆盖,高铁网络覆盖则更具复杂性,成为线覆盖的重点和难点。文章通过对高铁覆盖的穿透损耗、多普勒效应及其影响因素分析,进而从站址选择、设备选型及天馈部署等方面进行覆盖策略研究,确立了公网与专网建设的原则,最后以南广高铁LTE网络部署为例进行覆盖分析及工程测试验证,为后期高铁网络部署提供了有益的参考。     

动态环境下战略管理研究的新趋势

基于环境动态性和我国经济转型的背景,提出了从战略到战略变化的概念演变过程,并对战略变化研究的内容学派、过程学派的主要观点和理论模型进行了总结,同时对相关研究存在的问题进行了说明,最后提出了未来的主要研究方向。     

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.