利用加速度反馈改善转台低速性能

惯性技术的发展对惯性测试转台速率性能,特别是低速性能的要求越来越高,但摩擦力矩以及其它各种干扰力矩对转台低速性能的进一步提高产生了严重影响。为了有效克服这些干扰力矩对转台低速性能的影响,在传统的速率、位置双闭环控制结构的基础上,提出了利用加速度反馈来抑制干扰力矩的影响以提高转台低速运动性能的方法。通过利用获取的角加速度信息构成加速度闭环,对干扰力矩对低速运动的影响进行抑制,减少了转台低速爬行现象,提高了低速运动平稳性。分析和实验均表明,加速度反馈能对干扰力矩对低速运动的影响进行有效地抑制,改善了转台的低速运动性能。     

转台伺服系统低速性能分析

首先分析了摩擦干扰力矩和电机齿槽力矩对一般伺服系统速率平稳性的影响,在此基础上,针对转台伺服系统的实际设计,分析了摩擦干扰力矩和电机齿槽力矩对转台伺服系统低速性能的影响,提出了改善转台低速平稳性的技术途径。同时又研究了角位置误差引起的转台速率波动问题。     

摩擦速度对铜基摩擦材料摩擦磨损性能影响

采用粉末冶金技术制备了铜-石墨-SiO2烧结材料,通过定速摩擦试验机,在摩擦速度为7.8～47.1 m/s的范围内,研究摩擦速度、第三体与摩擦磨损性能的关系.结果表明,摩擦第三体的状态与摩擦速度密切相关,并明显影响摩擦磨损性能.在摩擦顺序从低速开始向高速进行的条件下,随摩擦速度的提高,摩擦表面第三体由颗粒状分布向密实状态转变,表面微观硬度提高,摩擦系数下降,磨损率变化不明显.这归因于低速条件下摩擦副间的啮合程度大,使摩擦系数处于较高值.随速度增加,致密状第三体的易流动性具有润滑和平滑作用,起到降低摩擦系数的作用;在摩擦顺序从高速开始向低速进行条件下,摩擦表面被高速摩擦形成的致密第三体所覆盖,致密第三体的稳定性具有降低摩擦系数波动的作用.但磨损率在摩擦速度较低时出现快速增加.原因在于随摩擦速度的降低,摩擦温度降低,致密第三体脆性增加,致密第三体的大面积破裂和剥落提高了磨损率.     

一种陀螺稳定平台自适应模糊-PID复合控制方法

机械摩擦、器件工作饱和区等不确定因素会导致陀螺稳定平台系统参数的波动和非线性特性,为解决非线性因素对稳定平台控制系统性能的影响,提出了一种自适应模糊-PID复合控制方法。引入自适应因子δ实现模糊控制和PID控制的复合,误差较大时增强模糊控制的作用以加快系统响应,误差较小时增强PID控制的作用以实现无静差调节。采用自调整量化因子ker(er)、kec(ec)实现基本论域的在线调整,提高了模糊控制器的灵敏度。仿真结果表明,在干扰冲击和短时常值干扰情况下,自适应模糊-PID复合控制与常规模糊控制相比,抗干扰能力显著增强,平台稳定精度提高0.4'左右。     

一种自适应模糊PID复合控制在液压仿真转台中的应用

针对液压仿真转台伺服系统的非线性特点，提出了一种模糊控制与局部积分控制相结合的复合控制方式。当系统的偏差较大时主要采用模糊控制器对系统的偏差进行快速调节以加快系统的响应过程；当系统的偏差小于某一值时，加入积分控制以保证系统的精度。为了提高模糊控制器的性能，采用了规则可调整的模糊控制器。实验结果表明：该方法能有效地克服液压伺服系统的非线性和参数的不稳定性以及外部干扰对系统的影响，具有较高的控制精度和鲁棒性能，完全适合于液压仿真转台伺服系统的控制。     

硬脂酸钾固体润滑薄膜的制备及其摩擦磨损性能研究

采用浸渍-提拉法制备出硬脂酸钾薄膜,用DF-PM型静-动摩擦磨损试验机和UMT-2MT型摩擦磨损试验机考察了在低速滑动和高速滑动条件下硬脂酸钾薄膜的摩擦磨损性能,采用扫描电子显微镜和X射线能量色散谱仪观察分析了薄膜及偶件磨损表面的形貌及其典型元素的面分布情况.结果表明,以GCr15钢球为偶件在高速滑动和以氮化硅球作为偶件在高、低速滑动条件下,薄膜具有较好的摩擦磨损性能.由于钢球和氮化硅陶瓷球表面粗糙度及其化学状态存在差异,硬脂酸钾更容易在氮化硅球表面形成转移膜,从而具有更低的摩擦系数和更长的耐磨寿命.     

带有摩擦耗能元件的框架结构动力分析

本文提出粘性屈服模型来模拟摩擦耗能元件的力-速度关系,该模型是连续变化的,克服了库仑摩擦模型不连续导致数值计算复杂的缺点,在进行摩擦耗能体系的动力分析中,采用缩减自由度技术,并作适当的变换,则带有摩擦耗能元件体系的动力分析归结为求解微分代数方程,本文采用增量型Rosenbrock二级三阶半隐式Runge-Kutta法求解该方程,以考虑框架和支撑的材料和几何非线性。对带有摩擦耗能元件的钢框架进行了弹塑性动力分析,研究了支撑刚度与结构层刚度的比值、摩擦力的大小以及地震波类型等参数对体系的影响。     

多自由度非线性结构体系在支承随机运动干扰下的模糊动力可靠性

本文在考虑了支承随机运动干扰的模糊性和结构随机振动反应允许界限模糊性的基础上,提出了多自由度非线性结构体系模糊动力可靠性的一般计算方法。文中提出了支承随机运动干扰的强度等级和谱密度形状类别模糊子集的评定方法,寻得了考虑界限模糊性的体系动力可靠性的计算公式,并最终得到了非线性结构体系模糊动力可靠性的评定方法。更合理地解决了非线性体系在支承随机运动干扰下的安全估计问题。     

离子液体添加剂对硬脂酸钾固体润滑薄膜摩擦磨损性能的影响

采用浸渍-提拉方法在单晶硅片上制备硬脂酸钾以及添加离子液体的硬脂酸钾复合薄膜,采用DF-PM型静-动摩擦磨损试验机考察薄膜在低速滑动条件下的摩擦磨损性能,采用扫描电子显微镜和X射线能量散射谱仪观察分析薄膜及其偶件表面的磨损形貌及典型元素面分布.结果表明:在相对低速滑动条件下,在羟基化硅基底上制备的复合薄膜的摩擦磨损性能优于硬脂酸钾薄膜;在载荷为1.0 N条件下,含离子液体质量分数40.0%的复合薄膜的耐磨寿命超过5 000次,而硬脂酸钾薄膜仅为100次左右;随着复合薄膜中离子液体含量增加,复合薄膜更容易在偶件钢球表面形成有效转移薄膜,使复合薄膜摩擦磨损性能得以提高.     

理论力学多面摩擦问题的程式化解法

为有效克服试凑法、穷举法在处理多面摩擦临界状态问题时造成的重复计算,通过引入临界状态集和偏离度两个新概念,提出一种求解多面摩擦临界状态问题的程式化方法.通过一个较为复杂的多面摩擦问题验证了方法的有效性.结果表明,该方法显著降低试凑不同临界状态带来的计算量,可作为摩擦问题计算机辅助求解的一种有效算法.     

System modeling and tracking control of mobile manipulator subjected to dynamic interaction and uncertainty

Trajectory tracking of a mobile manipulator is a challenging research because of its complex nonlinearity and dynamics. This paper presents an adaptive control strategy for trajectory tracking of a mobile manipulator system that consists of a wheeled platform and a modular manipulator. When a robot system moves in the presence of sliding, it is difficult to accurately track its trajectory by applying the backstepping approach, even if we employ a non-ideal kinematic model. To address this problem, we propose using a combination of adaptive fuzzy control and backstepping approach based on a dynamic model. The proposed control scheme considers the dynamic interaction between the platform and manipulator. To accurately track the trajectory, we propose a fuzzy compensator in order to compensate for modeling uncertainties such as friction and external disturbances. Moreover, to reduce approximation errors and ensure system stability, we include a robust term to the adaptive control law. Simulation results obtained by comparing several cases reveal the presence of the dynamic interaction and confirm the robustness of the designed controller. Finally, we demonstrate the effectiveness and merits of the proposed control strategy to counteract the modeling uncertainties and accurately track the trajectory.     

Adaptive interval type-2 fuzzy sliding mode control for unknown chaotic system

In this paper, a novel adaptive interval type-2 fuzzy sliding mode control (AIT2FSMC) methodology is proposed based on the integration of sliding mode control and adaptive interval type-2 fuzzy control for chaotic system. The AIT2FSMC system is comprised of a fuzzy control design and a hitting control design. In the fuzzy control design, an interval type-2 fuzzy controller is designed to mimic a feedback linearization (FL) control law. In the hitting control design, a hitting controller is designed to compensate the approximation error between the FL control law and the interval type-2 fuzzy controller. The parameters of the interval type-2 fuzzy controller, as well as the uncertainty bound of the approximation error, are tuned adaptively. The adaptive laws are derived in the sense of Lyapunov stability theorem, thus the stability of the system can be guaranteed. The proposed control system compared to adaptive fuzzy sliding mode control (AFSMC). Simulation results show that the proposed control systems can achieve favorable performance and robust with respect to system uncertainties and external disturbances.     

Intelligent tracking control of a DC motor driver using self-organizing TSK-type fuzzy neural networks

In this paper, a self-organizing Takagi–Sugeno–Kang (TSK) type fuzzy neural network (STFNN) is proposed. The self-organizing approach demonstrates the property of automatically generating and pruning the fuzzy rules of STFNN without the preliminary knowledge. The learning algorithms not only extract the fuzzy rule of STFNN but also adjust the parameters of STFNN. Then, an adaptive self-organizing TSK-type fuzzy network controller (ASTFNC) system which is composed of a neural controller and a robust compensator is proposed. The neural controller uses an STFNN to approximate an ideal controller, and the robust compensator is designed to eliminate the approximation error in the Lyapunov stability sense without occurring chattering phenomena. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived to speed up the convergence rates of the tracking error. Finally, the proposed ASTFNC system is applied to a DC motor driver on a field-programmable gate array chip for low-cost and high-performance industrial applications. The experimental results verify the system stabilization and favorable tracking performance, and no chattering phenomena can be achieved by the proposed ASTFNC scheme.     

Adaptive fuzzy backstepping output feedback control for a class of MIMO time-delay nonlinear systems based on high-gain observer

In this paper, an adaptive fuzzy backstepping output feedback control approach is developed for a class of multiinput and multioutput (MIMO) nonlinear systems with time delays and immeasurable states. Fuzzy logic systems are employed to approximate the unknown nonlinear functions, and an adaptive fuzzy high-gain observer is developed to estimate the unmeasured states. Using the designed high-gain observer, and combining the fuzzy adaptive control theory with the backstepping approach, an adaptive fuzzy output feedback control is constructed recursively. It is proved that all the signals of the closed-loop adaptive control system are semiglobally uniformly ultimately bounded (SUUB) and the tracking error converges to a small neighborhood of the origin.     

Fuzzy-approximation-based adaptive control of the chaotic permanent magnet synchronous motor

This paper focus on the problem of position tracking control for the chaotic permanent magnet synchronous motor drive system with parameter uncertainties. Fuzzy logic systems are used to approximate the nonlinearities and the adaptive backstepping technique is employed to construct controllers. The proposed adaptive fuzzy controllers guarantee that the tracking error converges to a small neighborhood of the origin. Compared with the conventional backstepping, the designed fuzzy controllers?? structure is very simple. Simulation results show that the proposed control scheme can suppress chaos of PMSM and guarantee the perfect tracking performance even under the unknown parameters.     

基于神经网络方法的包装件非线性特性识别的研究

结合模糊集合理论，将结构化神经网络方法用于包装件缓冲垫层非线性特性识别问题．对于两种典型的包装件缓冲垫层材料模型的模拟识别结果表明，据此方法可以较好地获得其非线性特性．模糊自适应技术的引入，提高了网络训练速度，减少了对于训练参数的人为干预，使得结构化神经网络方法更适于实际应用．     

Observer-based adaptive fuzzy backstepping control of MIMO stochastic nonlinear strict-feedback systems

In this paper, an adaptive fuzzy output feedback control approach is proposed for a class of multiinput and multioutput (MIMO) uncertain stochastic nonlinear strict-feedback systems without the measurements of the states. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed for estimating the unmeasured states. Utilizing the designed the fuzzy state observer and by combining the adaptive backstepping control design, an adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded (SUUB) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. A simulation example is provided to show the effectiveness of the proposed approach.     

Adaptive fuzzy backstepping output feedback control of nonlinear uncertain time-delay systems based on high-gain filters

In this paper, an adaptive fuzzy output feedback control approach is developed for a class of SISO uncertain nonlinear strict-feedback systems. The considered nonlinear systems contain unknown nonlinear functions, unknown time-varying delays and unmeasured states. The fuzzy logic systems are first used to approximate the unknown nonlinear functions, and then a high-gain filter is designed to estimate the unmeasured states. Combining the backstepping recursive design technique and adaptive fuzzy control design, an adaptive fuzzy output feedback backstepping control method is developed. It is proved that the proposed adaptive fuzzy control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and both the observer error and tracking error converge to a small neighborhood of the origin. Two key advantages of our scheme are that (i) the high-gain filter is designed to estimate unmeasured states of time-delay nonlinear system, and (ii) the virtual control gains are functions. A simulation is included to illustrate the effectiveness of the proposed approach.     

Explicit ultimate bound sets of a new hyperchaotic system and its application in estimating the Hausdorff dimension

The robust adaptive fuzzy control problem is investigated for a single machine bus system with static var compensator (SVC). This design does not require that speed of the generator rotor and susceptance of the overall system are measured, and also does not require that the parameters of controlled system are known accurately. The fuzzy logic systems are used to approximate the nonlinear functions of the system, and a fuzzy state observer is designed to estimate the speed of the generator rotor and susceptance. By utilizing the fuzzy state observer, and combining the adaptive backstepping technique with adaptive fuzzy control design, an observer-based adaptive fuzzy output-feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the angle of the generator rotor follows a desired value. Simulation results are presented to show the effectiveness of the approach.     

Fuzzy adaptive high-gain-based observer backstepping control for SISO nonlinear systems with dynamical uncertainties

In this paper, a fuzzy adaptive output feedback control approach is developed for a class of SISO strict-feedback nonlinear systems with unmeasured states, unmodeled dynamics, and dynamical disturbances. In the backstepping recursive design, fuzzy logic systems are used to approximate the unknown nonlinear functions, a fuzzy adaptive high-gain observer is designed to estimate the unmeasured states; a dynamic signal is incorporated into the control scheme to dominate the dynamic uncertainties. Using the states estimates and combining the backstepping design technique, a fuzzy adaptive output feedback control is constructed recursively. It is proved that the proposed fuzzy adaptive output feedback control scheme can guarantee the all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SUUB), and the observer and tracking error converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated via an example.