Disturbance observer based hierarchical control of coaxial-rotor UAV

This paper propose an hierarchical controller based on a new disturbance observer with finite time convergence (FTDO) to solve the path tracking of a small coaxial-rotor-typs Unmanned Aerial Vehicles (UAVs) despite of unknown aerodynamic efforts. The hierarchical control technique is used to separate the flight control problem into an inner loop that controls attitude and an outer loop that controls the thrust force acting on the vehicle. The new disturbance observer with finite time convergence is intergated to online estimate the unknown uncertainties and disturbances and to actively compensate them in finite time.The analysis further extends to the design of a control law that takes the disturbance estimation procedure into account. Numerical simulations are carried out to demonstrate the efficiency of the proposed control strategy.     

Numerical treatment of a quasilinear initial value problem with boundary layer

The paper deals with the singularly perturbed quasilinear initial value problem exhibiting initial layer. First the nature of solution of differential problem before presenting method for its numerical solution is discussed. The numerical solution of the problem is performed with the use of a finite-fitted difference scheme on an appropriate piecewise uniform mesh (Shishkin-type mesh). An error analysis shows that the method is first-order convergent except for a logarithmic factor, in the discrete maximum norm, independently of the perturbation parameter. Finally, numerical results supporting the theory are presented.     

Li-function activated ZNN with finite-time convergence applied to redundant-manipulator kinematic control via time-varying Jacobian matrix pseudoinversion

This paper presents and investigates the application of Zhang neural network (ZNN) activated by Li function to kinematic control of redundant robot manipulators via time-varying Jacobian matrix pseudoinversion. That is, by using Li activation function and by computing the time-varying pseudoinverse of the Jacobian matrix (of the robot manipulator), the resultant ZNN model is applied to redundant-manipulator kinematic control. Note that there are nine novelties and differences of ZNN from the conventional gradient neural network in the research methodology. More importantly, such a Li-function activated ZNN (LFAZNN) model has the property of finite-time convergence (showing its feasibility to redundant-manipulator kinematic control). Simulation results based on a four-link planar robot manipulator and a PA10 robot manipulator further demonstrate the effectiveness of the presented LFAZNN model, as well as show the LFAZNN application prospect.     

New solutions to the 2D adaptive visual servoing problem with relaxed excitation requirements

In this paper, we present three new globally convergent vision‐based position controllers for a planar manipulator in a fixed‐camera configuration, where the camera orientation and scale factor are considered unknown. This is a basic adaptive visual servoing problem whose solution was hampered by the nonlinear dependence of the system dynamics on the unknown parameters. Proposing a suitable reparameterization of the systems mathematical model, and exploiting some structural properties of it, we propose three different solutions to the problem. The first one is the certainty equivalent version of the known parameter controller and requires some excitation conditions to ensure global asymptotic convergence. A second version of the controller, which is now slightly more complicated and, possibly, needs to inject some high gain but requires significantly weaker excitation conditions, is given. Finally, we propose a slight modification to the second scheme to achieve the trajectory tracking in finite time. The efficacy of the three adaptive controllers is shown through realistic simulations.     

一种解决非光滑伪凸优化问题的新型神经网络

针对带有不等式约束条件的非光滑伪凸优化问题，提出了一种基于微分包含理论的新型递归神经网络模型，根据目标函数与约束条件设计出随着状态向量变化而变化的罚函数，使得神经网络的状态向量始终朝着可行域方向运动，确保神经网络状态向量可在有限时间内进入可行域，最终收敛到原始优化问题的最优解。最后，用两个仿真实验用来验证神经网络的有效性与准确性。与现有神经网络相比，它是一种新型的神经网络模型，模型结构简单，无需计算精确的罚因子，最重要的是无需可行域有界。     

An improved particle swarm optimizer based on tabu detecting and local learning strategy in a shrunk search space

To improve the performance of the standard particle swarm optimization (PSO) which suffers from premature convergence and slow convergence speed, many PSO variants introduce lots of stochastic or aimless strategies to overcome the convergence problem. However, the mutual learning between elites particles is omitted, although which might be benefit to the convergence speed and, prevent the premature convergence. In this paper, we introduce DSLPSO, which integrates three novel strategies, specifically, tabu detecting, shrinking and local learning strategies, into PSO to overcome the aforementioned shortcomings. In DSLPSO, search space of each dimension is divided into many equal subregions. Then the tabu detecting strategy, which has good ergodicity for search space, helps the global historical best particle to detect a more suitable subregion, and thus help it jump out of a local optimum. The shrinking strategy enables DSLPSO to take optimization in a smaller search space and obtain a higher convergence speed. In the local learning strategy, a differential between two elites particles is used to increase solution accuracy. The experimental results show that DSLPSO has a superior performance in comparison with several other participant PSOs on most of the tested functions, as well as offering faster convergence speed, higher solution accuracy and stronger reliability.     

A simple solution method for the blade element momentum equations with guaranteed convergence

The blade element momentum (BEM) equations, though conceptually simple, can be challenging to solve reliably and efficiently with high precision. These requirements are particularly important for efficient rotor blade optimization that utilizes gradient‐based algorithms. Many solution approaches exist for numerically converging the axial and tangential induction factors. These methods all generally suffer from a lack of robustness in some regions of the rotor blade design space, or require significantly increased complexity to promote convergence. The approach described here allows for the BEM equations to be parameterized by one variable—the local inflow angle. This reduction is mathematically equivalent, but greatly simplifies the solution approach. Namely, it allows for the use of one‐dimensional root‐finding algorithms for which very robust and efficient algorithms exist. This paper also discusses an appropriate arrangement of the equation and corresponding bounds for the one‐dimensional search—intervals that bracket the solution and over which the function is well behaved. The result is a methodology for solving the BEM equations with guaranteed convergence and at a superlinear rate.Copyright © 2013 John Wiley & Sons, Ltd.     

产业融合视野下的战略性新兴产业培育策略探析——基于新一代网络信息技术发展的视角

随着网络信息技术的日益发展，信息通信技术产业的融合之势愈加明显。产业间的融合渗透促进了新兴业务的活跃爆发，为产业发展带来了一股全新的力量。我国应当以此为契机，培育新一代产业增长极。首先需要认清产业发展规律，科学地指导产业发展；其次，有必要打破体制壁垒，创造良好的政策支持环境；再者，营造开放发展环境，鼓励企业创新，从而以产业融合有力地推动战略性新兴产业发展。     

朱光潜青年教育思想探析

美学大师朱光潜也是一位对青年教育倾注热忱的教育学家。"尽人之性,培养‘全人’""德育为首,学会做人""怡情养性,美化人生""引路启发,催生潜能"是朱先生青年教育思想的四个主要方面,这些教育思想迄今仍具有重要的借鉴意义。     

A homotopy analysis method for the nonlinear partial differential equations arising in engineering

In this article, we have established the homotopy analysis method (HAM) for solving a few partial differential equations arising in engineering. This technique provides the solutions in rapid convergence series with computable terms for the problems with high degree of nonlinear terms appearing in the governing differential equations. The convergence analysis of the proposed method is also discussed. Finally, we have given some illustrative examples to demonstrate the validity and applicability of the proposed method.