智能同步广播激励器及其标频

使用智能同步广播激励器,并采用CCTV传输的1MHz标频信号,就有可能实现自适应相位跟踪同步广播。这将大大节省中波段的频率资源。     

Robust adaptive control of robotic manipulators without the regressor matrix

A new adaptive controller is presented here for rigid-body robotic manipulators. It is stable and robust with respect to a class of external disturbances. The robustness of the adaptive controller is established without the ‘slow-varying’ assumption and the computationally demanding regressor matrix. The control law consists of a non-adaptive PD control part and an adaptive control part. It uses two adaptive matrices to compensate two uniformly bounded coefficient matrices derived from the original dynamics. A α σ|q?|-modified adaptive law is designed to adjust the adaptive matrices. A Lyapunov-type stability analysis indicates that the closed-loop system is uniformly ultimately bounded. The tracking error and compensation error will eventually converge into a closed region, which can be made arbitrarily small by adjusting the controller parameters. Simulation results are included to demonstrate the performance of the proposed controller.     

Mesh Simplification Based on Super-Face and Genetic Algorithm in Reverse Engineering

Mesh simplification is one of the most important links in the context of reverse engineering. It is also an important consideration in computer graphics. This paper presents a novel and original method of triangular mesh reduction. A new concept of super-face is defined. In order to construct a super-face, a way to calculate the scattered point curvature is discussed. The growth of a seed point and the combination of different regions are also necessary for the construction of a super-face. When the hole left by the deletion of super-face is retriangulated a genetic algorithm is used that has been modified for our specific problem. Then the different weights to balance the optimisation of the new triangular mesh can be adjusted.     

强噪声背景下确定性谐波信号的模糊滤波

文章提出了一种在强背景噪声环境下对被淹没的谐波信号进行模糊滤波，从而恢复微弱目标信号的方法。在MATLAB环境下，先利用相干平均法，除去被测信号中的随机白噪声，对于有色噪声运用自适应神经模糊推理系统（ANFIS）的方法，对被测信号进行模糊滤波，在较低信噪比下，能较好的恢复出原始微弱信号。     

Robot control and parameter estimation with only joint position measurements

Most adaptive control schemes for rigid robots assume velocities measurements to be available. Although it is possible to measure velocities by using tachometers, this increases costs and the signals delivered may be contaminated with noise. Since the use of encoders allows to read joint position pretty accurately, it is desirable to estimate joint velocities through an observer. This paper presents an adaptive scheme designed in conjunction with a linear observer. Boundedness of the estimated parameters and uniform ultimate boundedness for the tracking and observation errors are guaranteed. Experimental results are included to support the developed theory.     

Steady-state weights solution to affine projection algorithm

A new expression of the weights update equation for the affine projection algorithm (APA) is proposed that improves the convergence rate of an adaptive filter,particularly for highly colored input sign...     

Adaptive control of permanent magnet synchronous machines with disturbance estimation

In this paper,an adaptive control scheme is introduced for permanent magnet synchronous machines (PMSMs)as an alternative to classical control techniques.The adaptive control strategy capitalizes on the machine’s inverse dynamics to achieve accurate tracking by using an observer to approximate disturbance in the form of friction and load torque.The controller’s output is then fed to a space vector pulse width modulation(SVPWM)algorithm to produce duty cycles for the inverter.The control scheme is validated through a set of simulations on an experimentally validated PMSM model.Results for different situations highlight its high speed tracking accuracy and high performance in compensating for friction and load disturbances of various magnitudes.     

Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking

With the introduction of correlation filtering (CF), the performance of visual object tracking is significantly improved. Circular shifts collecting samples is a key component of the CF tracker, and it also causes negative boundary effects. Most trackers add spatial regularization to alleviate boundary effects well. However, these trackers ignore the effect of environmental changes on tracking performance, and the filter discriminates poorly in the background interference. Here, to break these limitations, we propose a new correlation filter model, namely Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking. Specifically, we use the Average Peak to Correlation Energy (APCE) and the response value error between the two frames together to perceive environmental changes, which adjusts the learning rate to make the template more adaptable to environmental changes. To enhance the discriminatory capability of the filter, we use real background information as negative samples to train the filter model. In addition, the introduction of the regular term destroys the closed solution of CF, and this problem can be effectively solved by the use of the alternating direction method of multipliers (ADMM). Extensive experimental evaluations on three large tracking benchmarks are performed, which demonstrate the good performance of the proposed method over some of the state-of-the-art trackers.     

Safe and intuitive manual guidance of a robot manipulator using adaptive admittance control towards robot agility

Key areas of robot agility include methods that increase capability and flexibility of industrial robots and facilitate robot re-tasking. Manual guidance can achieve robot agility effectively, provided that a safe and smooth interaction is guaranteed when the user exerts an external force on the end effector. We approach this by designing an adaptive admittance law that can adjust its parameters to modify the robot compliance in critical areas of the workspace, such as near and on configuration singularities, joint limits, and workspace limits, for a smooth and safe operation. Experimental validation was done with two tests: a constraint activation test and a 3D shape tracing task. In the first one, we validate the proper response to constraints and in the second one, we compare the proposed approach with different admittance parameter tuning strategies using a drawing task where the user is asked to guide the robot to trace a 3D profile with an accuracy or speed directive and evaluate performance considering path length error and execution time as metrics, and a questionnaire for user perception. Results show that appropriate response to individual and simultaneous activation of the aforementioned constraints for a safe and intuitive manual guidance interaction is achieved and that the proposed parameter tuning strategy has better performance in terms of accuracy, execution time, and subjective evaluation of users.     

遗传算法模糊PID测控系统在环控试验台中的应用

飞机环控试验台须模拟流量0～14000kg/h、压力0～2.5MPa和常温～500℃的空气环境;项目要求测控范围广、精度±1%且不超调;空气状态具有非线性、时变等特点,且控制参数之间存在复杂耦合;针对以上难点,设计了分布式测控系统,提出了改进的智能PID控制方案;通过遗传算法分段整定PID参数,离线建立PID数据库,使系统能够根据控制目标值选择最优PID初值;在此基础上,结合模糊推理在线调整PID参数,使系统具有了自适应性,能在具体工况和干扰下达到很好的控制效果;实际应用中完全满足了指标要求,解决了传统PID的控制难点,对类似的复杂系统有一定借鉴意义。