一种新型稳定平台伺服控制系统

深入研究了三自由度并串联混合机构稳定平台,设计了一个非线性自适应控制器。考虑到实际系统工作中存在摩擦、负载扰动和动力学参数误差,分离出动力学模型中的未建模动力学参数、摩擦力参数和负载扰动,建立了关于待辨识参数的线性动力学模型。运用Lyapunov方法设计了一个非线性自适应控制器。构建了并串联光电稳定平台伺服系统实验平台。分别将所设计的控制器与计算力矩控制器分别在高速和低速扰动情况进行了实验,实验表明所提出非线性自适应控制器在低速0.006(°)/s时,跟踪精度分别为滚转轴0.071°、俯仰轴0.064°、偏转轴0.038°,在20(°)/s高速状态下,跟踪精度分别为滚转轴0.045°、俯仰轴0.042°、偏转轴0.029°,其控制效果明显好于传统控制。     

无刷直流力矩电机系统的自适应扰动力矩补偿研究

陀螺漂移测试转台无刷直流力矩电机系统中存在波动力矩和负载力矩振动，这严重地影响了转台速率平稳度。为提高转台速率状态位置跟踪精度，设计了一种自适应补偿方法。该方法包含一个参自适应律和等效PID控制律，它利用前馈补偿原理，来估计电机中未知参数以及波动力矩和负载力矩参数并给与补偿。该自适应补偿方法保证了闭环系统全局稳定性和对期望位置信号的渐进跟踪。仿真结果证明：该方法有效地提高了转台速率状态跟踪精度。     

抑制线振动台扰动的鲁棒自适应重复控制

针对线振动台系统中存在的非线性动态摩擦力及周期性纹波推力扰动,为获得线振动台较高的跟踪精度及鲁棒性能,提出了鲁棒自适应重复控制方法.该方法的控制律包含参数自适应控制、等效PID控制、重复空制和滑模控制.滑模控制用来镇定不确定性系统和保证自适应重复学习过程收敛,参数自适应律用来估计未知模型参数并予以补偿,重复控制用来抑制周期性扰动,提高周期性位置信号的跟踪性能.Lyapunov理论证明该控制律保证了闭环系统渐近稳定性.通过对线振动台系统仿真研究表明该控制律的有效性.     

机械轴承控制系统中的摩擦补偿

摩擦是机械轴承控制系统中最主要的扰动因素,也是影响系统低速跟踪性能的最主要因素。针对系统中动态摩擦力矩的影响,为了进一步提高系统的位置跟踪性能,提出一种重复自适应控制方法。该方法的控制律包含一个参数自适应律、等效PD控制律和一个重复控制律,其中,参数自适应律用来估计未知模型参数并予以补偿,而插入的重复控制器用来提高系统运行曲线的跟踪性能。系统中摩擦模型采用摩擦参数非一致性变化的LuGre动态模型。Lyapunov方法证明该补偿方法保证了闭环系统全局稳定性和对期望位置信号的渐近跟踪。最后,通过对控制系统的仿真研究验证了所提出方法的有效性。     

基于单向滑模的永磁同步电机摩擦自适应控制

针对摩擦条件下永磁同步电机伺服系统的高精度位置控制问题进行了研究。利用单向滑模控制算法和广义麦克斯威尔滑动(GMS)摩擦模型,设计了具备摩擦前馈补偿功能的力矩控制器,对GMS模型的参数进行了自适应调节以补偿摩擦力变化。通过设计适当的趋近率,使得该控制器在保证系统稳定的同时,产生连续的期望电流信号,消除了普通滑模带来的抖振问题,同时采用反步法反推控制电压获得了保证系统总体稳定的控制信号。最后的仿真实验结果表明,提出的方法有利于提高摩擦条件下永磁同步电机控制的控制精度。     

基于扰动观测器的卫星姿态控制系统自适应有限时间容错控制

针对具有状态时变时滞、系统不确定性、可建模扰动、运行噪声和执行器故障的卫星姿态控制系统,提出一种基于扰动观测器的自适应有限时间复合主动容错控制策略。针对可建模扰动设计扰动观测器,然后基于扰动估计误差设计了主动容错控制器。该时滞依赖控制器包含反馈控制项、扰动补偿项和快速自适应故障补偿项。提出的容错控制策略不仅保证闭环系统动态方程的有限时间有界性,而且保证闭环测量输出对于系统不确定性、运行噪声、执行器故障等的鲁棒性。给出控制器增益限制矩阵存在的充分条件及其线性矩阵不等式形式,进而给出仿真算例。仿真结果表明,基于扰动观测器方法,设计的自适应有限时间容错控制器能够快速估计可建模扰动,进而有效地实现系统的闭环容错控制。相较于基于非复合的自适应有限容错控制器,提出的方法对于状态变量的估计均方根误差分别降低了28.9%、4.7%和36.0%;对于可建模扰动估计的均方根误差降低了38.8%。仿真验证了所提方法的有效性。     

惯性平台稳定回路基于Backstepping的动态滑模控制(英文)

针对带不匹配不确定非线性干扰的惯性平台稳定回路跟踪控制问题,提出了基于backstepping的动态滑模控制方法。首先,建立了惯性平台稳定回路的等价模型,该模型由一个线性模型加上一个不确定的非线性函数组成。然后,基于backstepping方法设计了带渐近稳定滑模面的动态滑模控制器,解决了模型不匹配的问题,并提高了系统的鲁棒性。进而应用Lyapunov稳定性理论证明了所设计的控制器不仅能保证闭环系统的稳定性,而且可以通过选择适当的控制器参数来调整跟踪误差的收敛率。最后,仿真结果表明,基于backstepping的动态滑模控制方法与PID控制方法相比,提高了系统的跟踪精度,增强了鲁棒性。     

机器人关节非线性摩擦模型关键参数反求

机器人关节非线性摩擦的准确描述对提高机器人轨迹精度、定位精度及其可靠性等具有重要理论意义和科学价值. 然而, 机器人关节通常包含电机、减速器、驱动器和传感器, 是一个复杂的机电耦合系统, 随服役时间及工况的变化, 机器人关节的摩擦参数也存在显著时变效应, 难以准确描述, 造成轨迹精度下降, 为机器人后期精度维护造成巨大困难. 因此, 本文定量评价了摩擦参数对机器人输出力矩的影响, 提出考虑时变效应的机器人关节非线性摩擦参数反求方法. 首先, 建立机器人关节一般非线性摩擦模型. 设计机器人关节恒速跟踪实验, 通过卡尔曼滤波对实验采集的数据进行处理, 进而建立关节速度和驱动电机电流之间的关系, 完成关节一般非线性摩擦模型建立. 其次, 择取非线性摩擦模型关键参数. 建立包含非线性摩擦的机器人动力学模型, 基于激励轨迹计算各关节力矩, 并对其开展灵敏度分析, 择取对关节力矩灵敏性较高的摩擦参数. 再次, 建立关节输出力矩和摩擦参数一一对应的数据集. 基于实际工况构建摩擦参数取值空间, 采用最优拉丁超立方法对摩擦参数采样, 并将其代入机器人动力学模型计算出相应的力矩, 从而求得关节输出力矩和摩擦参数一一对应的数据集. 最后, 建立反问题神经网络并对其进行训练, 实现非线性摩擦模型关键参数反求, 并进行验证. 研究结果表明关节非线性摩擦的准确描述减小了机器人低速运动换向时摩擦力矩突变对机器人轨迹的影响, 显著提升了机器人轨迹精度.      

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。     

航天器编队系统相对位置自适应分布式控制

针对具有参数不确定性以及外部扰动的航天器编队飞行队形跟踪控制问题,基于反步控制策略提出了一种能够实现控制有界的自适应编队控制方法。首先建立航天器相对运动的非线性动力学方程,在不考虑外部干扰情况下,利用饱和函数设计了输入有界的自适应协同控制器;之后进一步考虑存在外部干扰的情况,通过估计扰动上界设计了鲁棒自适应协同控制器,并且采用Lyapunov稳定性分析方法证明了控制系统的稳定性。数值仿真结果表明,提出的控制方法能够满足控制受限并实现航天器队形的协同控制,同时在大约100 s误差收敛到0附近,队形跟踪和队形保持的稳态误差分别小于0.002 m和0.005 m。     

考虑摩擦特性的柔性关节空间机器人自适应CMAC神经网络鲁棒控制

讨论了关节摩擦力矩影响下,具有柔性铰关节的漂浮基空间机器人系统的动力学控制问题.设计了基于高斯基函数的小脑神经网络(CMAC)鲁棒控制器和摩擦力矩补偿器.用奇异摄动理论对系统的动力学模型进行快慢变子系统分解,针对快变子系统,设计力矩微分反馈控制器来抑制机械臂关节柔性引起的振动;对于慢变子系统,设计了基于自适应CMAC神...     

Backstepping control for the optoelectronic stabilized platform based on adaptive fuzzy logic system and nonlinear disturbance observer

A composite controller based on a backstepping controller with an adaptive fuzzy logic system and a nonlinear disturbance observer is proposed in this paper to address the disturbance and uncertainty issues in the control of the optoelectronic stabilized platform. The matched and unmatched disturbances and system uncertainty are included in the stabilized platform model. The system's uncertainty and disturbance are approximated and estimated using an adaptive fuzzy logic system and a nonlinear disturbance observer. Moreover, the backstepping control algorithm is utilized to control the system. The simulations are performed in four states to confirm the viability of the proposed control technique. The proportional integral controller, proportional integral-disturbance observer controller, and fuzzy backstepping controller are contrasted with the proposed controller. It has been noted that the proposed controller's instantaneous disturbance's highest value is 5.1°/s. The maximal value of the coupling output for the two gimbals utilizing the proposed controller, however, is 0.0008°/s and 0.0018°/s, respectively. The findings presented here demonstrate that the backstepping controller, which is based on an adaptive fuzzy logic system and a nonlinear disturbance observer, is capable of precise tracking and dynamic tracking of a stabilized platform under disturbance and uncertainty.

     

Adaptive trajectory tracking of magnetostrictive actuator based on preliminary hysteresis compensation and further adaptive filter controller

An adaptive dynamic surface control (DSC) scheme is proposed for the multi-input multi-output attitude control of near-space hypersonic vehicles (NHV). The proposed control strategy can improve the control performance of NHV despite uncertainties and external disturbances. The proposed controller combines dynamic surface control and radial basis function neural network (RBFNN) and is designed to control the longitudinal dynamics of NHV. The DSC technique is used to handle the problem of “explosion of complexity” inherent to the conventional backstepping method. RBFNN is used to approximate the unknown nonlinear function, and a robustness component is introduced in the controller to cancel the influence of compound disturbance and improve robustness and adaptation of the system. Simulation results show that the proposed strategy possesses good robustness and fast response.     

Nonlinear adaptive neural network control for a model-scaled unmanned helicopter

In this paper, a nonlinear adaptive neural network control is proposed for trajectory tracking of a model-scaled helicopter. The purpose of this research is to reduce the ultimate bounds of tracking errors resulted from small coupling forces (or small parasitic body forces) and aerodynamic uncertainties. The proposed control is designed under backstepping framework, with neural network compensators being added. Updating laws of neural networks are designed through projection algorithm, so that adaptive parameters are bounded. Derivatives of virtual controls are obtained through command filters. It is proved that, by using neural network compensators, tracking errors of the closed-loop system can be restricted within very small ultimate bounds. Superiority of the proposed nonlinear adaptive neural network control over a backstepping control is demonstrated by simulation results.     

Direct adaptive neural control for strict-feedback stochastic nonlinear systems

This note considers the problem of direct adaptive neural control for a class of nonlinear single-input/single-output (SISO) strict-feedback stochastic systems. The variable separation technique is introduced to decompose the coefficient functions of the diffusion term. Radical basis function (RBF) neural networks are used to approximate unknown and desired control signals, then a novel direct adaptive neural controller is constructed via backstepping. The proposed adaptive neural controller guarantees that all the signals in the closed-loop system remain bounded in probability. A main advantage of the proposed controller is that it contains only one adaptive parameter needed to be updated online. Simulation results demonstrate the effectiveness of the proposed approach.     

Adaptive control of a chaotic permanent magnet synchronous motor

This paper proposes a simple adaptive controller design method for a chaotic permanent magnet synchronous motor (PMSM) based on the sliding mode control theory which has given an effective means to design robust controllers for nonlinear systems with bounded uncertainties. The proposed sliding mode adaptive controller does not require any information on the PMSM parameter and load torque values, thus it is insensitive to model parameter and load torque variations. Simulation results are given to verify that the proposed method can be successfully used to control a chaotic PMSM under model parameter and load torque variations.     

Adaptive neural tracking control for a class of perturbed pure-feedback nonlinear systems

This paper focuses on the problem of the adaptive neural control for a class of a perturbed pure-feedback nonlinear system. Based on radial basis function (RBF) neural networks’ universal approximation capability, an adaptive neural controller is developed via the backstepping technique. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the tracking error eventually converges to a small neighborhood around the origin. The main advantage of this note lies in that a control strategy is presented for a class of pure-feedback nonlinear systems with external disturbances being bounded by functions of all state variables. A numerical example is provided to illustrate the effectiveness of the suggested approach.     

The design of nonlinear disturbance attenuation controller for TCSC robust model of power system

The control problem of the single machine infinite bus system with TCSC is dealt with. Based on the maximization of the external disturbances on the system model, an adaptive nonlinear controller for large disturbance attenuation and a parameter updating law are designed by using the backstepping method. The parameter uncertainty of the transmission line is considered, as well as the influences of large external disturbances to the system output are mainly discussed. The nonlinear controller does not have the sensitivity to the influences of external disturbances, but also has strong robustness for system parameters variation. The simulation results show that the control effect of the large disturbance attenuation controller more advantages by comparing with the control performance of conventional nonlinear robust controller.     

Direct adaptive neural control of chaos in the permanent magnet synchronous motor

In this paper, a direct adaptive neural speed tracking control is addressed for the chaotic permanent magnet synchronous motor (PMSM) drive systems via backstepping. Neural networks are directly used to approximate unknown and desired control signals and a novel direct adaptive tracking controller is constructed via backstepping. The proposed adaptive neural controllers guarantee that the tracking error converges to a small neighborhood of the origin. Compared with the conventional backstepping method, the designed neural controller??s structure is very simple. Simulation results show that the proposed control scheme can suppress the chaos of PMSM and guarantees the perfect tracking performance even with the existence of unknown parameters.     

Adaptive neural network control of bilateral teleoperation with constant time delay

This paper proposes a novel approach for bilateral teleoperation systems with a multi degrees-of-freedom (DOF) nonlinear robotic system on the master and slave side with constant time delay in a communication channel. We extend the passivity based architecture to improve position and force tracking and consequently transparency in the face of offset in initial conditions, environmental contacts and unknown parameters such as friction coefficients. The proposed controller employs a stable neural network on each side to approximate unknown nonlinear functions in the robot dynamics, thereby overcoming some limitations of conventional controllers such as PD or adaptive controllers and guaranteeing good tracking performance. Moreover, we show that this new neural network controller preserves the control passivity of the system. Simulation results show that NN controller tracking performance is superior to that of conventional controllers.