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为解决自动调平机构系统稳定性差和调平精度低的问题,设计了一种基于3-UPU并联机构和PI鲁棒滑模控制的调平系统。首先,基于螺旋和反螺旋理论,设计了一种3-UPU并联机构作为系统的调平机构,并建立了机构的动力学方程,为调平控制系统提供了控制对象。然后,在PI控制的基础上,利用鲁棒滑模算法的抗摄动、实时修正系统非线性的功能,设计了一种PI鲁棒滑模控制器,并运用Lyapunov函数证明了控制器的稳定性。最后,分别采用两种控制方法对机构的调平误差进行了仿真分析,结果表明:PI鲁棒滑模控制器有更佳的调平精度和较小的稳态误差,为调平机构的研究提供了一种参考方案。 相似文献
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对二维工作台控制系统进行了研究,采用滚珠丝杆副和直线滚动导轨副的传动方式,选用直线光栅尺作为工作台的反馈元件,控制系统的核心为工控机和运动控制卡,利用固高GE400运动控制卡发出脉冲信号控制伺服驱动器对工作台机械部分进行实时的操控。在VC++6.0设计环境下开发工作台的控制软件,实现了多轴控制。使用双频激光仪获得工作台的系统误差曲线,采取水平分割的方法对误差曲线进行分割,获得各区间的误差补偿数据,通过软件控制对工作台各行程区间进行误差补偿,通过理论计算和实验测得补偿后的数据对比,验证了该补偿方法的可靠性,对于提升二维工作台的精度具有积极的意义。 相似文献
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针对目前国内外二维弹道修正的情况,概述了二维弹道修正常见的几种方式,并分析了脉冲修正方式的影响因素,提出了我国弹道修正机构技术基本的发展思路。 相似文献
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针对机械臂的实时控制问题,基于约束预测控制,提出了一种机械臂实时运动控制方法。介绍了机械臂动力学模型并进行了线性化处理,以降低算法复杂度、保证实时性。设计了轨迹跟踪控制器和约束预测控制器,其中轨迹跟踪控制器采用最优反馈控制律,可确保机械臂按参考轨迹运动;而约束预测控制器则在考虑机械臂物理约束的情况下,为跟踪控制器提供最优参考轨迹。以DSP作为核心控制器,搭建了机械臂控制系统,同时给出了硬件和软件设计方法。以梯形和三次多项式轨迹为例,进行了系统功能测试,测试结果表明了所述控制系统的可行性和有效性。 相似文献
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电传动自卸车辆的辅助电气主要完成车辆仪表、灯光、雨刮、液压逻辑等控制功能以及为整车弱电设备提供电源,主要采用点对点模式,原理简单但存在着整车线束数量多、检修困难、难以维护保养等诸多弊端。针对存在的问题,提出分布集中式的辅助电气模式;对系统布线进行设计,根据各部分功能和布置的不同,采用分布式和传统点对点连接方式相结合的方法,降低整车线束的复杂程度;对辅助电气系统进行设计,设计控制器和端口程序,并对主控制器线路和液压控制器线路进行设计,使其满足整车的使用需求;对辅助电路进行仿真实验,选取机油压力检测和整车紧急双移线试验进行各辅助电路测试。结果可知:机油泵转速、机油温度和机油压力的检测准确度高,误差小于1%;整车实际运行轨迹与理想双移线运行轨迹有较好的吻合;辅助电路能够实现设计的控制机理,顺畅的信号流,CAN总线的网络能够保证正确的通信,准确性和实时性得到实现;为此类设计提供参考。 相似文献
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随着自动驾驶技术的快速发展,精确的轨迹跟踪已经成为汽车工业和学术领域公认的实现自主车辆运动控制的核心技术之一。为提高自主车辆轨迹跟踪的实时性与准确性,提出一种应用于自主车辆的线性时变模型预测跟踪控制器(Linear time-varying model predictive controller,LTV-MPC)设计方法。根据运动学原理建立某自主无人小车的二自由度运动学模型,其次,基于该模型构建车辆轨迹跟踪系统的误差模型并利用线性参数化理论对其进行离散化,在模型预测控制框架内将该轨迹跟踪控制器的设计转化为一个线性二次规划最优问题。在一个实际搭建的自主车辆试验平台上对所提出控制器的有效性进行不同预设参考路径轨迹下的实车验证,结果表明,该自主车辆能够对所预设的实际参考道路轨迹进行快速、准确的轨迹跟踪控制,且具有较好的行驶稳定性能。 相似文献
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四轮轮毂电机驱动电动汽车各轮驱动力矩独立可控,可通过控制前轴左右两轮的力矩差实现前轮转向。以四轮轮毂电机驱动智能电动汽车为研究对象,针对线控转向系统执行机构失效时的轨迹跟踪和横摆稳定性协同控制问题,提出一种基于差动转向与直接横摆力矩协同的容错控制方法。该方法采用分层控制架构,上层控制器首先基于时变线性模型预测控制方法求解期望前轮转角和附加横摆力矩,然后考虑转向执行机构建模不确定性以及路面干扰,设计基于滑模变结构控制的前轮转角跟踪控制策略。下层控制器以轮胎负荷率最小化为目标,利用有效集法实现四轮转矩优化分配。最后,分别在高速换道和双移线工况下仿真验证了该控制方法的有效性和实时性。 相似文献
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This paper presents an improved practical controller for enhancing precision motion performance. For practical use, high motion control performance and ease of controller design are desired. A nominal characteristic trajectory following control (NCTF control) has been studied to satisfy the desired response. The NCTF controller consists of a nominal characteristic trajectory (NCT) which is the reference motion of control system and a compensator which makes the motion of the controlled object to follow the NCT. The NCT is easily determined from experimental open-loop time responses of the mechanism. The controller parameters can be also determined easily, without any given model parameters. In the present paper, the Continuous Motion NCTF controller for high continuous motion performance is improved in order to enhance the following characteristic of the object motion on NCT and improve the positioning and tracking accuracies of the system. The improved Continuous Motion NCTF controller (referred to as Acceleration Reference-Continuous Motion NCTF controller (AR-CM NCTF controller)) provides the advantages such as the high overshoot reduction characteristics and the low sensitivity disturbance. The AR-CM NCTF controller includes the structure of the Continuous Motion NCTF controller and acceleration reference for the object motion as the additional controller elements. The design procedure of the AR-CM NCTF controller remains easy and practical. In order to confirm the advantages, the AR-CM NCTF controller was examined in positioning and tracking motion performances using the non-contact mechanism. The experimental results prove that the AR-CM NCTF controller achieves the better positioning and tracking performances than the Continuous Motion NCTF controller. 相似文献
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In this paper, a novel Runge–Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. 相似文献
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筒盖系统是潜艇导弹发射的重要装置。筒盖启闭通过电液伺服系统进行控制,筒盖系统通过角度传感器实现其闭环控制,传感器发生故障工况对潜艇的导弹发射将造成严重损害。为了保障筒盖系统安全稳定运行,针对其传感器故障工况设计了PI观测器,实现对筒盖系统传感器故障的实时估计,基于信号重构技术实现了对传感器反馈信号的矫正,并结合鲁棒控制器实现了对筒盖系统传感器故障工况下的安全稳定运行。试验结果表明,该控制器对筒盖系统发生传感器故障工况下的控制实现自愈效果,提高了其控制性能。 相似文献
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Dr Shiuh-Jer Huang Chih-Feng Hu 《The International Journal of Advanced Manufacturing Technology》1996,12(6):450-454
Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. 相似文献
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WangGang RenGuoli YanXiang'an WangGuodong 《机械工程学报(英文版)》2004,17(3):446-449
Some dynamic factors, such as inertial forces and friction, may affect the robot trajectory accuracy. But these effects are not taken into account in robot motion control schemes. Dynamic control methods, on the other hand, require the dynamic model of robot and the implementation of new type controller. A method to improve robot trajectory accuracy by dynamic compensation in robot motion control system is proposed. The dynamic compensation is applied as an additional velocity feedforward and a multilayer neural network is employed to realize the robot inverse dynamics. The complicated dynamic parameter identification problem becomes a learning process of neural network connecting weights under supervision. The finite Fourier series is used to activate each actuator of robot joints for obtaining training samples. Robot control system, consisting of an industrial computer and a digital motion controller, is implemented. The system is of open architecture with velocity feedforward function. The proposed m 相似文献