两栖机器人轮桨腿驱动机构多目标优化设计

提出了一种既能够在陆地上爬行,又能够在一定深度的水下浮游和在海底爬行的新概念轮桨腿一体化两栖机器人;多运动模式和复合移动机构是该机器人的突出特点．分析了轮桨腿复合式驱动机构的运动机理,并采用多目标优化设计理论和算法,对驱动机构的爬行性能和浮游特性进行了综合优化,得到了两栖机器人驱动机构的结构优化参数．虚拟样机的仿真结果证明了该轮桨腿一体化两栖机器人驱动机构的综合运动性能良好,对非结构环境具有一定的适应能力．     

系统级软件FMEA计算机辅助设计研究

软件失效模式和影响分析(SFMEA)是提高软件可靠性的一种重要方法.针对人工SFMEA分析费时费力的问题,着眼于SFMEA的分析过程,对系统级SFMEA计算机辅助设计及相应辅助工具的实现展开了研究,介绍了系统级SFMEA分析的具体步骤,提出了对软件功能单元建模辅助生成软件约定层次,由已有通用失效模式库和相邻层次分析结果辅助获取软件失效模式、软件失效原因和纠正措施辅助设计等,并在此基础上开发了相应的辅助工具,从而减少SFMEA的分析工作量,提高SFMEA分析的效率.     

引入趋近律的功率因数校正滑模控制仿真研究

提出一种引入趋近律的滑模变结构控制(SMVSC)方法来实现有源功率因数校正(APFC),减少电流的谐波成分.SMVSC是一种解决非线性时变系统(如APFC系统)问题的良好办法,但是实际应用中SMVSC的"抖振"现象问题必须要解决好.引入趋近律来削弱APFC系统在滑模控制中的"抖振"现象,并且依此推导出了控制APFC系统中的功率开关的PWM占空比.采用Matlab平台进行仿真验证控制策略控制APFC电路几乎得到单位功率因数,同时超调减小、响应时间缩短.     

基于Backstepping的倒立摆鲁棒跟踪控制

针对内部参数不确定及存在外部干扰的非线性倒立摆系统,提出了基于Backstepping方法的滑模变结构控制律,并且采用RBF神经网络逼近系统不确定非线性函数,同时引入滑模误差对其神经网络权值进行在线自适应调整,使神经网络的逼近速度加快,改善了动态性能.该控制律能保证倒立撰轨迹跟踪误差的快速收敛性以及对外部扰动和内部参数不确定的不敏感性,最后给出的仿真实例证明了该理论分析结果的正确性,控制效果良好.     

基于T-S模型的输出反馈滑模控制及应用

为了使系统满足品质要求并对不确定因素具有较强的鲁棒性,采用滑模控制方法。建立被控对象T-S模糊模型。针对系统状态不完全可测特点,采用迭代线性矩阵不等式方法,利用输出信息构造滑模面。根据系统品质要求,采用参数鲁棒设计方法,配置所需极点,确定相应在参数空间的映射关系。应用平行分布补偿算法,根据映射产生的参数样本训练T-S模糊控制器,实现等效控制律。在此基础上应用高增益方法设计切换控制律,保证系统对满足匹配条件的不确定因素具有较理想的自适应性。最后,直升机模型的仿真结果表明,方法具有很好的控制效果。     

时变不确定非线性系统自适应滑模跟踪控制

针对一般的具有时变且界未知的非线性不确定性的单输入多输出非线性系统,提出一种自适应滑模跟踪控制器的框架.在该框架内,系统的时变且界未知的非线性不确定性可以通过函数逼近技术(FAT)表示成为一组正交基函数序列的组合,并通过滑模控制技术和直接Lyapunov方法获得基函数系数的更新律以及对不确定性逼近误差的在线自适应补偿,从而得到自适应的滑模控制律.所提出的基于函数逼近技术的自适应滑模跟踪控制策略在直流电机跟踪控制系统实验装置上进行了实际控制实验,并进行了性能的对比与分析.     

Robust output feedback learning control for induction motor servo drives

This paper addresses the output feedback tracking control problem for induction motor servo drives with mechanical uncertainties: rotor angle, rotor speed and stator currents are assumed to be available for feedback. A robust adaptive learning control is designed under the assumption that the reference profile for the rotor angle is periodic with known period: it ‘learns’ the periodic disturbance signal by identifying the Fourier coefficients of any truncated approximation; ??₂ and ??_∞ transient performances are guaranteed in the ‘learning phase’. It is shown that, for any motor initial condition belonging to an arbitrary given compact set, by properly setting the control parameters: (i) the rotor position and flux modulus tracking errors exponentially converge to residual sets, which may be arbitrarily reduced by increasing the number of terms in the truncated Fourier series; (ii) when the unknown periodic disturbance can be represented by a finite Fourier series, the rotor position and flux modulus tracking errors exponentially converge to zero. As in field oriented‐control, the control algorithm generates references for the magnetizing flux component and for the torque component of the stator current leading to significant simplifications for current‐fed motors. Copyright © 2008 John Wiley & Sons, Ltd.     

A backstepping controller for path‐tracking of an underactuated autonomous airship

In this paper we propose a nonlinear control approach for the path‐tracking of an autonomous underactuated airship. A backstepping controller is designed from the airship nonlinear dynamic model including wind disturbances, and further enhanced to consider actuators saturation. Control implementation issues related to airship underactuation are also addressed, namely control allocation and an attitude reference shaping to obtain a faster error correction with smoother input requests. The results obtained demonstrate the capacity of an underactuated unmanned airship to execute a realistic mission including vertical take‐off and landing, stabilization and path‐tracking, in the presence of wind disturbances, with a single robust control law. Copyright © 2008 John Wiley & Sons, Ltd.     

ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

This paper presents an approach to improve the performance of intelligent sliding model control achieved by the use of a fundamental constituent of soft computing, named Adaptive Linear Element (ADALINE). The proposed scheme is based on the fractional calculus. A previously considered tuning scheme is revised according to the rules of fractional order differintegration. After a comparison with the integer order counterpart, it is seen that the control system with the proposed adaptation scheme provides (1) better tracking performance, (2) suppression of undesired drifts in parameter evolution and (3) a very high degree of robustness and insensitivity to disturbances. The claims are justified through some simulations utilizing the dynamic model of a two degrees of freedom (DOF) direct drive robot arm and overall, the contribution of the paper is to introduce the fractional order calculus into a robust and nonlinear control problem with some outperforming features that are absent when the integer order differintegration operators are adopted.     

Fuzzy boundary layer tuning for sliding mode systems as applied to the control of a direct drive robot

Chattering in the control signal is a significant problem in sliding mode control (SMC). The boundary layer approach is one of the many modifications proposed in the literature to avoid the chattering. In this approach, instead of the discontinuous SMC, a continuous feedback control law is employed in a boundary layer around the sliding surface. The thickness of the boundary layer is an important design parameter. This paper proposes a fuzzy online tuning method to adjust the boundary layer thickness for the best system performance without chattering. The method features the measurement of the chattering in the control signal. The paper validates the performance of the algorithm by experiments on a direct drive robot with a range of different payloads.