Neural network robust control of a 3-DOF hydraulic manipulator with asymptotic tracking

Multiaxial hydraulic manipulators are complicated systems with highly nonlinear dynamics and various modeling uncertainties, which hinders the development of high-performance controller. In this paper, a neural network feedforward with a robust integral of the sign of the error (RISE) feedback is proposed for high precise tracking control of hydraulic manipulator systems. The established nonlinear model takes three-axis dynamic coupling, hydraulic actuator dynamics, and nonlinear friction effects into consideration. A radial basis function neural network (RBFNN) is synthesized to approximate the uncertain system dynamics and external disturbance, which can greatly reduce the dependence on accurate system model. In addition, a continuous RISE feedback law is judiciously integrated to deal with the residual unknown dynamics. Since the major unknown dynamics can be estimated by the RBFNN and then compensated in the feedforward design, the high-gain feedback issue in RISE feedback control will be avoided. The proposed RISE-based neural network robust controller theoretically guarantees an excellent semi-global asymptotic stability. Comparative simulation is performed on a 3-DOF hydraulic manipulator, and the obtained results verify the effectiveness of the proposed controller.     

Applicable conditions of instantaneous source used for welding heat conduction

Abstract

This paper deals with the sufficient conditions under which instantaneous heat source can be considered as welding heat source instead of moving source. Temperature rises calculated by instantaneous source and moving one were compared. The applicable conditions of the instantaneous heat source with errors of 1% and 5% were found. This enables us to select either moving or instantaneous source with information of welding speed, thermal diffusivity and the distance from the weld centre line to the interested location. Approximate equations to determine the maximum temperature rise using a moving source were proposed and the accuracy of their calculation was proved to be ～99%.     

Robust integral of neural network and precision motion control of electrical–optical gyro-stabilized platform with unknown input dead-zones

Parametric uncertainty associated with unmodeled disturbance always exist in physical electrical–optical gyro-stabilized platform systems, and poses great challenges to the controller design. Moreover, the existence of actuator deadzone nonlinearity makes the situation more complicated. By constructing a smooth dead-zone inverse, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback is proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded (GUUB) tracking stability of servo systems recently. An asymptotic tracking performance in the presence of unknown dead-zone, parametric uncertainties and various disturbances, which is vital for high accuracy tracking, is achieved by the proposed robust adaptive backstepping controller. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy.     

Neural network-based asymptotic tracking control of unknown nonlinear systems with continuous control command

ABSTRACT

This paper proposes a robust tracking controller for a class of nonlinear second-order systems with time-varying uncertainties. The controller is mainly based on the robust integral of the sign of the error (RISE) control approach to achieve an asymptotic stability result with a continuous control command in the presence of additive uncertainties. An adaptive feedforward neural network control term is blended with a new RISE controller to improve the system's transient performance. The proposed RISE controller is a modified version of the existing saturated RISE controller such that only sign of the derivative of the output is needed. The stability of the closed-loop system is well studied, where a local asymptotic stability is proven. The controller performance is validated through simulations on a two-degree-of-freedom lower limb robotic exoskeleton.     

蛀孔寻径网络中通信机制的研究及实现

通信机制的研究及功能实现是实时交互仿真环境ＲＩＳＥ设计的关键技术。本文在分析仿真系统网络环境和蛀孔寻径原理的基础上,建立系统的通信模型,进行相应的算法设计,给出具体的函数实现,并与有关通信机制进行原理对比。分析表明：本设计具有通信开销小、通信速度快的特点。同时,也提高了用户编程透明性。     

硬实时综合业务以太网络的研究

对现有的以太网和实时以太网相关技术进行了深入研究，提出了一种基于以太网技术的具有硬实时特性的综合业务网络访问控制技术——硬实时综合业务以太网RISE。它能够实现实时和非实时数据的混合传输，为网络综合业务数据的传输提供保障。     

基于虚拟分解的液压机械手RISE渐近跟踪控制

多自由度液压机械臂是一个多输入多输出、强非线性、强耦合的机电液复杂非线性系统，对液压机械臂精确控制是极富挑战的任务。提出一种基于虚拟分解的误差符号积分鲁棒控制方法用于液压机械手系统的高精度跟踪控制。考虑三关节之间的动力学耦合、液压作动器动态和摩擦效应，首先基于虚拟分解建立液压机械手的运动学和动力学数学模型，基于虚拟功率流保证子系统和整个系统的L₂和L_∞稳定性，设计虚拟控制方法。然后进一步将关节之间的耦合作用融入到鲁棒控制器设计之中，发展出基于虚拟分解的误差符号积分鲁棒控制方法，进一步增强了关节位置跟踪能力。基于Lyapunov理论证明该控制方法可实现位置跟踪误差的渐近收敛。对比仿真结果表明，提出的基于虚拟分解的鲁棒积分控制器具有优异的渐近跟踪性能。     

Laser structuring for back junction silicon solar cells

We demonstrate mask‐free fabrication of a 22·0%‐efficient crystalline Si solar cell by applying laser ablation of Si and by laser ablation of protective coatings. The bulk absorber material is a p ‐type float zone silicon wafer and the designated cell area is 4 cm². While the processing time of our laboratory‐type of laser system is far too slow for industrial processing, we estimate on the basis of our experiments that laser processing of 12·5 × 12·5 cm²‐sized solar cells in just a few seconds is feasible with commercially available equipment. Copyright © 2006 John Wiley & Sons, Ltd.     

基于电力线载波和GPRS的智能变电站抄表系统的研究

电网智能化是世界各国大力研究的一个方向。本文基于电力线载波和G PR S无线抄表技术对智能变电站抄表系统进行了拓展。分析了硬件设计的关键问题并阐述了软件的基本架构。该系统具有良好的应用价值。