共查询到19条相似文献,搜索用时 218 毫秒
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针对机器人在参数变化和外界工作环境的刚度变化时,系统的控制质量会因常规PID控制器没有自适应能力而明显变差,甚至无法工作,提出了一种具有混合H2/H∞性能指标的CMAC控制方法,采用CMAC神经网络加强系统对参数不确定性的补偿,引入混合优化策略来优化CMAC神经网络的结构和权值,保证了系统对外界干扰在给定干扰衰减度下具有鲁棒稳定性的同时,还能使系统达到良好的动态性能,满足一定的H2最优性能指标。仿真结果表明,本文所提控制方案在大量参数不确定性及外部扰动存在的情况下,仍能满足性能要求。 相似文献
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变速变桨距风电系统的功率水平控制 总被引:10,自引:2,他引:8
为实现风电系统的功率水平控制,该文基于奇异摄动理论和逆系统方法设计了一种非线性桨距角鲁棒控制器。该控制器由逆系统标称部分和鲁棒补偿部分组成,逆系统标称控制器可以使非仿射型非线性标称风机模型的输入-输出动态跟踪其参考模型动态;鲁棒补偿输入可以消除参数不确定性、风速检测误差和发电机转矩扰动对系统输出功率的影响。理论分析和仿真实验证明了该控制器的稳定性,结果表明,该控制器可以在风速波动时有效控制风电系统的输出功率水平,并且对参数化和非参数化扰动具有较强的鲁棒性。 相似文献
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自抗扰控制器在动态电压恢复器中的应用 总被引:1,自引:0,他引:1
为了提高动态电压恢复器(dynamic voltage restorer,DVR)系统的动态性能和鲁棒性,根据自抗扰控制器(ADRC)的原理设计了DVR自抗扰控制方案.自抗扰控制器的设计不需要精确的DVR参数和数学模型,将电网电压和负载电流视为系统的未知干扰,用扩张状态观测器对未知扰动进行观测,然后利用非线性反馈控制律进行补偿,使系统的控制律今与系统的给定输入和输出有关,减少了控制过程中的检测量,将复杂的控制过程加以简化.仿真和实验表明,自抗扰控制器对系统模型的不确定性和外扰具有较强的适应性和鲁棒性,控制系统具有优良的动态性能. 相似文献
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基于递归模糊神经网络的感应电机无速度传感器矢量控制 总被引:25,自引:16,他引:25
该文提出了一种控制性能较好的递归模糊神经网络(RFNN)无速度传感器感应电机矢量控制方法,该方法使用模型参考自适应方法辨识转子磁场位置和转速,采用递归模糊神经网络控制器作为转矩控制器来近似系统最优控制器输出。仿真实验表明,当系统参数动态变化或受到外部不确定性因素的影响时,利用神经网络来在线动态的调整网络的隶属函数参数以及神经网络递归权值,使系统仍将具有很好的动静态性能。 相似文献
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针对电动伺服机构的模糊控制与扰动补偿问题进行研究,首先依据动力学理论建立电动伺服机构系统模型并利用Simulink软件搭建仿真模型。然后充分分析了系统所受到的摩擦力矩、齿槽力矩、时滞等非线性扰动,设计前馈控制器进行补偿。其次为了进一步改善系统的控制性能,在位置环PID控制器基础上引入模糊控制来动态调整PID控制参数。最后利用BP神经网络实现对量化因子和比例因子的实时整定,改善由于模糊规则及模糊输出论域的不对称性导致在正负行程上效果不一致的问题。从动态响应能力、跟随性能、抗干扰能力、频域响应等方面分别对传统PID控制器、模糊PID控制器和模糊BP网络PID控制器的控制性能进行仿真对比分析,结果表明模糊BP神经网络PID控制器提高了系统响应速度,改善了系统控制品质,可以为航天电动伺服机构结构和控制器设计提供借鉴。 相似文献
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针对内置式永磁同步电机(IPMSM)参数非线性及不确定性导致的电机转矩难以准确估测和控制等问题,研究了基于ELM(极限学习机)神经网络的IPMSM的矢量控制转矩观测器。利用ELM神经网络较强的泛化能力和逼近能力,同时根据IPMSM的矢量控制要求,设计出电流到转矩的非线性映射;将神经网络输出的估测的转矩作为反馈转矩输入PI转矩控制器,并通过PI电流控制器调节q轴电流;使实际电机转矩等于转矩命令,从而完成对电机的转矩控制。实验结果表明,该观测器有效地减少了90%的转矩脉动,具有良好的动态和静态性能,同时对系统参数不确定性和非线性具有较好的适应性。 相似文献
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基于CMAC的双馈水轮发电机系统控制策略研究 总被引:3,自引:2,他引:3
针对可调速双馈水轮发电机系统的不确定性、非线性和参数时变的特点,提出了一种采用小脑模型(CMAC)神经网络的自适应控制策略。该控制策略以系统动态误差和给定信号量作为CMAC的激励信号,并与自适应神经网络控制器相结合构成系统的复合控制。该文对双馈水轮发电机系统的稳态调节和暂态特性进行了数字仿真研究,并与常规的PID控制进行比较。结果表明,基于CMAC的自适应控制策略对系统模型结构和参数变化、负荷扰动都具有很好的适应性和鲁棒性,控制品质优良,是一种适于在线学习控制的双馈水轮发电机系统控制方法。 相似文献
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Chian‐Song Chiu Kuang‐Yow Lian 《International Journal of Adaptive Control and Signal Processing》2007,21(5):415-433
This paper proposes a robust adaptive motion/force tracking controller for holonomic constrained mechanical systems with parametric uncertainties and disturbances. First, two types of well‐known holonomic systems are reformulated as a unified control model. Based on the unified control model, an adaptive scheme is then developed in the presence of pure parametric uncertainty. The proposed controller guarantees asymptotic motion and force tracking without the need of extra conditions. Next, when considering external disturbances, control gains are designed by solving a linear matrix inequality (LMI) problem to achieve prescribed robust performance criterion. Indeed, arbitrary disturbance/parametric error attenuation with respect to both motion and force errors along with control input penalty are ensured in the L2‐gain sense. Finally, applications are carried out on a two‐link constrained robot and two planar robots transporting a common object. Numerical simulation results show the expected performances. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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Sliding mode disturbance observer‐based adaptive control for uncertain MIMO nonlinear systems with dead‐zone 下载免费PDF全文
Mou Chen Shao‐dong Chen Qing‐xian Wu 《International Journal of Adaptive Control and Signal Processing》2017,31(7):1003-1018
In this paper, an adaptive integral sliding mode control (ISMC) scheme is developed for a class of uncertain multi‐input and multi‐output nonlinear systems with unknown external disturbance, system uncertainty, and dead‐zone. The research is motivated by the fact that the ISMC scheme against unknown external disturbance and system uncertainty is very important for multi‐input and multi‐output nonlinear systems. The system uncertainty, the unknown external disturbance, and the effect of dead‐zone are integrated as a compounded disturbance, which is well estimated using a sliding mode disturbance observer (SMDO). Then, the adaptive ISMC based on the designed SMDO is presented to guarantee the satisfactory tracking performance in the presence of system uncertainty, external disturbance, and dead‐zone. Finally, the designed adaptive ISMC strategy based on SMDO is applied to the attitude control of the near space vehicle, and simulation results are presented to illustrate the effectiveness of the proposed adaptive ISMC scheme using the SMDO. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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Precision motion control of a small launching platform with disturbance compensation using neural networks 下载免费PDF全文
Jian Hu Lei Liu Yuan‐gang Wang Zhiwei Xie 《International Journal of Adaptive Control and Signal Processing》2017,31(7):971-984
A kind of launching platform driven by two permanent magnet synchronous motors which is used to launch kinetic load to hit the target always faces strong parameter uncertainties and strong external disturbance such as the air current impulsion which would degrade their tracking accuracy greatly. In this paper, a practical method which combines adaptive robust control with neural network‐based disturbance observer is proposed for high‐accuracy motion control of the launching platform. The proposed controller not only accounts for the parametric uncertainties but also takes the external disturbances into account. Adaptive control is designed to compensate the former, while neural network‐based disturbance observer is designed to compensate the latter respectively and both of them are integrated together via a feedforward cancellation technique. A new kind of parametric adaptation and weight adaptation strategy is designed by using the linear combination of the system's tracking error and the weight estimation error as a driving signal for parametric adaptation and disturbance approximation. The stability of the novel control scheme is analyzed via a Lyapunov method and this method presents a prescribed output tracking performance in the presence of both parameter uncertainties and unmodeled nonlinearities. Extensive comparative simulation and experimental results are obtained to verify the high‐performance of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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Yeong-Chan Chang Bor-Sen Chen 《International Journal of Adaptive Control and Signal Processing》1998,12(6):495-526
Both dynamic state feedback as well as output feedback tracking control designs are presented in this paper for constrained robot systems under parametric uncertainties and external disturbances. The previous studies on tracking control design, not considering the velocity measurements, address only the unconstrained robot design. In contrast, a dynamic output feedback controller based on a linear and reduced-order observer that uses only position measurements is proposed here for the first time to treat the trajectory tracking control problem of constrained robot systems. Both adaptive state feedback control schemes and adaptive output feedback control schemes with a guaranteed H∞ performance are constructed. It is shown that all the variables of the closed-loop system are bounded and a pre-assigned H∞ tracking performance is achieved, in the sense that the influence of external disturbance on the tracking motion error can be attenuated to any specified level. Moreover, it is also shown that the motion and force trajectories asymptotically converge to the desired ones as the dynamic model of robot systems is well-known and the external disturbance is neglected. Finally, simulation examples are presented to illustrate the tracking performance of a two-link robotic manipulator with a circular path constraint by the proposed control algorithms. © 1998 John Wiley & Sons, Ltd. 相似文献
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王盟 《国外电子测量技术》2007,26(10):7-9
遥操作机器人就是通过计算机网络利用机器人对远距离环境进行观测和操作.本文为了对不确定环境下的遥操作机器人系统进行较好的控制,研究和分析了基于神经网络的遥操作机器人系统的预测控制算法.该算法通过建立远地从机械手和环境模型,预测从机械手返回的作用力,实现主、从机械手之间的无时滞跟踪.仿真结果表明,此算法能够获得较好的稳定性和操作性能. 相似文献