神经网络在气动机械手控制器中的应用

气动机械手控制系统是一个非线性系统,采用常规的PID控制方法难以获得较好的控制效果.将具有自学习和自适应能力的单神经元模型与常规的PID控制算法相结合,设计了单神经元自适应PID控制器,并将其应用于机械手气动压力伺服系统中,控制器采用DSP实现.运行结果表明,该控制器能够适应被控对象在较大范围内的变化,具有较强的鲁棒性,其控制品质优于常规PID控制器.     

基于LMI和扰动观测器的电动伺服系统RBF神经网络控制

为了提高电动伺服系统的加载力跟踪精度,基于线性矩阵不等式(LMI)设计扰动观测器和控制器。针对系统中的非线性因素,采用RBF神经网络逼近系统的数学模型;在建立系统跟踪目标模型的基础上,根据LMI设计扰动观测器对控制器进行多余力的补偿,利用李雅普诺夫函数证明扰动观测器和控制器的收敛;在MATLAB/Simulink中搭建仿真模型,分析扰动观测器和RBF神经网络在不同工况下对系统相应量的精准估计,且误差均满足所设定的性能指标,同时与PID控制相比较,证明所提控制策略的控制性能更优。     

汽车电子节气门的模型参考-扰动观测复合控制

为了提高电子节气门在外界扰动和参数不确定情况下的控制精度，提出了模型参考自适应控制与扰动观测器结合的复合控制方法。分析了电子节气门控制系统的工作原理，建立了电子节气门数学模型。以典型二阶系统为参考模型，给出了控制律构造方法，使用Lyapunov稳定性推导了控制律参数的自适应律，因而设计了模型参考自适应控制器。为了消除外界扰动和系统参数不确定性，设计了扰动观测器对扰动和参数进行实时估计。将模型参考自适应控制器与扰动观测器结合，提出了复合控制器的构造方法。经仿真和实验验证，存在外界扰动和参数不确定性时，复合控制器的控制精度和控制速度均优于模型参考自适应控制器，说明了复合控制器在电子节气门控制中的有效性，且复合控制器具有较强的鲁棒性。     

基于线性扩张状态观测器的挖掘机电液系统滑模控制（英文）

无人驾驶挖掘机器人实际工作环境恶劣,为提高铲斗在负载扰动下的轨迹跟踪精度,建立了挖掘机电液系统非线性数学模型,设计了基于线性扩张状态观测器的滑模控制器（Sliding mode controller based on linear extended state observer, SMC-LESO）。根据铲斗油缸活塞杆位移信号,利用设计的线性扩张状态观测器对系统的速度、加速度及负载扰动和不确定因素进行估计,解决了系统参数难以测量的问题。在此基础上,设计了滑模控制器,并证明了该控制器的Lyapunov稳定性。为进一步验证该控制器的有效性,建立了挖掘机电液比例控制系统的联合仿真模型,并与PID控制器、滑模控制器的轨迹跟踪精度进行对比。仿真结果表明,该控制器可以有效抑制扰动,位移跟踪精度高、鲁棒性强。     

四辊卷板机侧辊位移跟踪控制

针对四辊卷板机侧辊位移跟踪控制存在负载变化、参数摄动和未建模动态等不确定性问题,提出一种基于非线性扰动观测器的自适应滑模控制策略。采用非线性扰动观测器在线获取并补偿等效扰动;针对引入非线性扰动观测器后的系统,采用反步法设计自适应滑模控制器,利用自适应律动态补偿扰动观测误差,以降低滑模控制器的切换增益。该设计方法放宽了滑模切换增益对系统不确定性上界的先验性要求,降低了外界干扰和系统不确定性对侧辊位移跟踪性能的影响。同时,采用李雅普洛夫方法证明了侧辊位移跟踪闭环控制系统的稳定性。根据工程实际参数进行仿真,结果表明,该控制策略对系统的不确定性,特别是负载变化具有较强的鲁棒性,可以满足侧辊位移快速、精确跟踪的要求。     

基于扩张状态观测器和非线性状态误差反馈设计自抗扰振动控制器

智能桁架结构作为一种控制结构一体化的新型空间结构,具有几何、物理可自调性,但这类结构同时具有柔性大、阻尼小、低频模态密集、模态耦合程度高以及存在多种不确定性和耦合等特点,给振动控制带来很大挑战。基于此,研究如何基于扩张状态观测器和非线性状态误差反馈设计不依赖于模型的自抗扰振动控制器。采用扩张状态观测器实时估计对象模型摄动及外扰的总和作用量,并在控制信号中补偿掉,实现不确定非线性系统的动态补偿线性化。针对线性的"积分器串联型"对象,设计非线性状态误差反馈控制律。对不确定参数空间83杆智能桁架结构进行自抗扰振动控制仿真研究。结果表明在外部扰动或模型结构参数发生变化时,自抗扰振动控制器仍可以获得理想的控制效果,具有很好的适应性和鲁棒性。     

基于Lu Gre模型的伺服系统自适应控制研究

为减小摩擦等非线性因素对机床进给系统运行平稳性的影响,建立了基于Lu Gre模型的系统动态特性模型,并设计了非线性单观测器估计接触面的变形量。设计自适应摩擦补偿控制器补偿摩擦、系统惯量和外界扰动等,并利用Lyapunov定理证明了系统的全局渐进稳定性。仿真结果表明,自适应参数kz取值在0. 1-0. 12之间、γ1取值在1. 9-2. 1之间时,位置跟踪误差达到了10-3级,速度与位移输出迅速趋于稳定,具有良好的跟踪精度和响应速度,可以有效抑制摩擦和外界扰动对系统的影响。     

基于扰动观测器的反推控制在磁轴承中的应用

针对磁轴承转子系统在运行时因转子质量不平衡导致的同频扰动问题,提出了将反推控制算法和扰动观测器相结合形成一个复合控制器的控制策略.对磁轴承转子系统中的同频信号进行观测,并在输入信号中对同频扰动信号进行抑制.建立了转子动力学模型,设计了反推控制器和扰动观测器,通过MATLAB仿真,结果表明:该复合控制器比反推控制器对扰动...     

航空相机镜筒位置控制的扰动估计与补偿

在航空相机镜筒速度内环设计了自抗扰控制器,以减小镜筒内偏心凸轮机构的运动对镜筒的扰动.采用扩张状态观测器对扰动进行实时估计,并生成扰动补偿量抵消扰动的影响.首先,分析了凸轮机构的运动对镜筒扰动的特点.然后,建立了镜筒的数学模型,基于带宽的单参数化设计思想设计了扩张状态观测器及带扰动补偿的控制律,设计了镜筒位置控制器.最后,在镜筒上对控制器的抗扰动性能进行了验证,并与目前航空相机常用的一阶平方滞后超前校正法进行了比较.结果表明,采用自抗扰控制能将凸轮机构对镜筒的扰动偏差减小为传统方法的50％左右.在提高系统抗扰动性能的同时,控制器增益减小约2个数量级,大大降低了对控制器增益的要求,提高了系统的稳定性,对改善航空相机控制系统的抗扰动性能及降低控制器设计难度具有较高的实用价值.     

基于趋近律离散滑模控制的柔性梁振动抑制

研究了基于趋近律的离散滑模控制在智能柔性悬臂梁振动控制中的应用.以压电陶瓷为作动器,电阻应变片为传感器,采用有限元方法和模态截断技术建立结构动力学模型.由于柔性结构系统受到不确定外部扰动和量测噪声的影响以及参数的不确定性,滑模变结构控制可以实现滑动模态与系统的外干扰和参数摄动无关,即滑动模态的不变性.结构振动控制在系统状态由于外部干扰的影响偏离平衡状态,在控制器作用下能使系统趋于零状态.采用趋近律离散滑模控制方法设计状态调节器.由于状态量不能直接测量,故利用离散卡尔曼滤波技术构造状态估计器.采用试验模态测试方法得到结构的前4阶固有频率和阻尼比,与有限元方法的结果比较,说明该模型的正确性.使用dSPACE实时仿真系统和MATLAB/Simulink搭建控制系统,进行了振动主动控制试验.试验结果表明,所设计的控制器能有效抑制结构的振动响应.     

Tracking control of robot manipulators via output feedback linearization

This paper presents a robot manipulator tracking controller based on output feedback linearization. A sliding mode perturbation observer (SPO) is designed to estimate unmeasurable states and system perturbations that involve system nonlinearities, disturbances and unmodelled dynamics. The use of SPO allows to input/output linearize and decouple the strongly coupled nonlinear robot manipulator system merely by the feedback of joint angles. The controller design does not need an accurate model of the robot manipulator. Simulation studies are undertaken based on a two-link robot manipulator to evaluate the proposed approach. The simulation results show that the proposed controller has more superior tracking control performance, with payload changing in a wide range, in comparison with a sliding mode controller (SMC) designed based on state feedback linearization with full states available. Selected from Journal of Shenzhen University (Science & Engineering), 2005, 22(3)     

Learning from adaptive neural network output feedback control of a unicycle-type mobile robot

This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.     

Trajectory tracking control of a 3-CRU translational parallel robot based on PD+robust controller

In order to suppress the external disturbances existing in the trajectory tracking process of the 3-CRU parallel robot, a trajectory tracking control method based on PD+robust controller is proposed in this paper. The kinematic model of the 3-CRU parallel robot is established to solve the kinetic energy and potential energy of the system. The basic dynamic model of the 3-CRU parallel robot is obtained based on Lagrangian formulation, and the complete dynamic model of the parallel robot is established by introducing Coulomb and viscous friction. Based on the analysis of the factors affecting the stability of PD controller, a trajectory tracking control method based on PD+robust controller is proposed, and it is proved theoretically that the system converges stably and has a good external disturbance suppression effect. The method has the characteristics of easy implementation and strong applicability of PD controller and strong robustness of robust controller. Experimental results prove the effectiveness of this method.

     

Robust disturbance rejection control of a biped robotic system using high-order extended state observer

This study addressed the problem of robust control of a biped robot based on disturbance estimation. Active disturbance rejection control was the paradigm used for controlling the biped robot by direct active estimation. A robust controller was developed to implement disturbance cancelation based on a linear extended state observer of high gain class. A robust high-gain scheme was proposed for developing a state estimator of the biped robot despite poor knowledge of the plant and the presence of uncertainties. The estimated states provided by the state estimator were used to implement a feedback controller that was effective in actively rejecting the perturbations as well as forcing the trajectory tracking error to within a small vicinity of the origin. The theoretical convergence of the tracking error was proven using the Lyapunov theory. The controller was implemented by numerical simulations that showed the convergence of the tracking error. A comparison with a high-order sliding-mode-observer-based controller confirmed the superior performance of the controller using the robust observer introduced in this study. Finally, the proposed controller was implemented on an actual biped robot using an embedded hardware-in-the-loop strategy.     

Simulation and experimental control of a 3-RPR parallel robot using optimal fuzzy controller and fast on/off solenoid valves based on the PWM wave

In this paper, a robust optimal fuzzy controller based on the Pulse Width Modulation (PWM) technique is proposed to control a laboratory parallel robot using inexpensive on/off solenoid valves. The controller coefficients are determined using Modified Cuckoo Optimization Algorithm. The objective function of this method is considered such that the results show the position tracking by the robot with less force and more efficiency. Regarding the results of experimental tests, the control strategy with on/off valves indicates good performance such that the maximum value of RMS of error for a circular path with increasing force on the system is 3.1mm. Furthermore, the results show the superiority of the optimal fuzzy controller compared with optimal PID controller in tracking paths with different conditions and uncertainties.     

基于模糊神经网络的不确定机器人实时轨迹跟踪控制的研究

针对机器人建模的不精确性以及扰动的存在给机器人控制增加难度的问题,提出了一种基于模糊神经网络的不确定机器人实时轨迹跟踪控制方法。该控制方法的控制器由模糊神经网络(FNN)控制器和CMAC控制器组成,FNN控制器代替传统的计算力矩法,CMAC控制器在线补偿控制误差,有效补偿机器人存在的各种不确定性。对二自由度机器人的仿真结果表明了所提出的控制方法的可行性。     

基于计算力矩法的3-RRRT并联机器人控制研究

为了实现3-RRRT并联机器人的轨迹跟踪控制,在运动学分析的基础上,应用拉格朗日方程建立了3-RRRT并联机器人的动力学模型.设计了基于计算力矩算法的控制律,并利用Matlab软件进行了控制器仿真.仿真表明计算力矩控制可以使3-RRRT并联机器人实现全局稳定的动态轨迹控制.     

蛇形机器人跟踪误差预测的自适应轨迹跟踪控制器

为了满足蛇形机器人轨迹跟踪运动的精度需要,消除外界干扰对机器人跟踪误差的影响,提出了一种蛇形机器人跟踪 误差预测的自适应轨迹跟踪控制器。 所提出的控制器实现了机器人干扰变量、摩擦系数和控制参数的预测,并用预测值和虚拟 控制函数来补偿系统的控制输入,抵消了蛇形机器人在轨迹跟踪过程中的侧滑角,避免了干扰变量对机器人带来的负面影响, 提高了轨迹跟踪的误差稳定性与控制精度。 在建立蛇形机器人模型后,利用积分形式的侧滑角补偿项改进了视线法,并设计了 蛇形机器人的自适应轨迹跟踪控制器。 使机器人的位置误差在 10 s 内实现收敛,角度误差小于 0. 03 rad,预测值误差在 5 s 内 收敛。 通过仿真实验,验证了所提出的控制器的有效性和优越性。     

一种3自由度并联柔索驱动柔性操作臂的建模与控制

以一种3自由度并联柔索驱动机器人为研究对象,研究这种并联柔性系统的控制规律。考虑到运动描述的唯一性及易测性,取约束关节的位置作为广义关节变量,用以描述操作臂的运动状态。在此基础上分析了操作臂的运动学和静力学关系,建立了并联柔性系统的动力学模型。然后,以轨迹控制为目标,分别基于刚性模型和柔性模型设计控制器,并对系统的性能进行分析和仿真。结果表明:基于刚性模型控制器的性能较差,而基于柔性模型的奇异摄动方法则可以获得较好的控制效果。     

Track velocity control of crawler type underwater mining robot through shallow-water test

The concept of continuous mining for manganese nodules suggests three sub-operations in total mining: collecting, lifting, onboard treatment. The combination of three components could be shaped by self-propelled seafloor mining robot, flexible conduit and buffer, lifting pumps and pipe, and mining platform. Particularly, the self-propelled robot tracking the mining paths on the seafloor is the key to accomplish the continuous mining. This paper discusses track velocity control of remotely operated mining robot, which is a basic and indispensable requirement for path tracking. The track velocity control is realized by PI controller from gain tuning formulas based on the model identification. First, to investigate the nature of the tracking system, a laboratory test is executed with the robot hung in air by overhead crane. Next, the transfer function of the tracking system is identified by the open-loop response and the closed-loop response. Through familiar tuning formulas based on the identified system parameters, PI gains are tuned. Finally, among the tuned PI gains, the one of best performance is set as the track velocity controller.