全局稳定的机器人CMAC标称补偿跟踪控制研究

提出了一种新的小脑模型(Cerebellar Model Articulation Controller,CMAC)神经网络标称补偿控制器.采用二阶扩展B样条CMAC网络平滑逼近机器人标称模型,消除了常规神经网络控制对输入的严格假设.为了确保系统闭环的全局稳定性,采用Lyapunov直接法设计网络权值的更新律,并引入非线性反馈项完全抵消补偿的残留项.未知的CMAC逼近误差和系统随机干扰,通过一个简洁的鲁棒自适应律估计.最后,针对两自由度机器人的仿真实例验证了所提算法的有效性.     

基于反推滑模的船舶动力定位有限时间控制

考虑存在未知外界干扰的船舶动力定位控制问题,提出一种基于有限时间理论的自适应反推非奇异快速终端滑模算法,并对未知干扰进行自适应估计.利用有限时间Lyapunov理论证明了设计的控制律能够保证闭环系统的状态在有限时间内收敛到平衡点附近小的邻域内.仿真结果表明,与传统渐近稳定控制律相比,设计的控制策略保证闭环系统具有更快的收敛速度及更好的稳定性和鲁棒性.此外,通过对干扰的自适应补偿,进一步降低了系统的稳态误差,增强了系统的抗干扰能力.     

基于改进的FNNs移动机械臂输出反馈跟踪控制

针对包含电机动态模型的移动机械臂系统,提出一种鲁棒自适应输出反馈控制方法.将误差符号函数鲁棒积分反馈与神经网络前馈结构相结合用于控制器的设计,然后利用神经网络去逼近机器人和电机系统的不确定项,设计鲁棒项实时补偿网络误差.通过Lyapunov稳定性分析证明闭环系统所有信号半全局一致有界.最后仿真实验表明,控制方法对系统动态不确定性和外界干扰有很好的鲁棒性,可实现移动机械臂的输出反馈跟踪控制.     

六自由度并联机器人分散鲁棒非线性控制

针对六自由度并联机器人动力学特点,提出了一种新型分散鲁棒非线性控制方法.与传统PD控制策略和已有分散鲁棒非线性控制方法相比,由于控制律中增加了广义误差的小数幂项,改进后的方法除保持原方法的优点外,还具有较强的终端收敛能力,能够保证跟踪误差以更快的速度全局一致收敛到一个更小的剩余集.通过选择合适的控制器参数,可使剩余集趋于0.运用Lyapunov方法分析了系统稳定性,给出了系统稳定性条件.最后,仿真结果验证了该方法的有效性.     

基于单网络评判学习的非线性系统鲁棒跟踪控制

针对一类具有不确定性的连续时间非线性系统,提出一种基于单网络评判学习的鲁棒跟踪控制方法.首先建立由跟踪误差与参考轨迹构成的增广系统,将鲁棒跟踪控制问题转换为镇定设计问题.通过采用带有折扣因子和特殊效用项的代价函数,将鲁棒镇定问题转换为最优控制问题.然后,通过构建评判神经网络对最优代价函数进行估计,进而得到最优跟踪控制算法.为了放松该算法的初始容许控制条件,在评判神经网络权值更新律中增加一个额外项.利用Lyapunov方法证明闭环系统的稳定性及鲁棒跟踪性能.最后,通过仿真结果验证该方法的有效性和适用性.     

带扰动补偿的无抖振离散重复控制器设计

针对周期离散系统的跟踪控制问题,提出一种有限时间单调收敛的无抖振吸引律,讨论扰动补偿措施并将其嵌入吸引律形成理想误差动态用于设计离散重复控制器.通过分析补偿误差上界说明扰动补偿措施能抑制重复控制未能消除的扰动,通过推导控制器稳态误差带说明吸引律的收敛性可使系统具有鲁棒稳定性.针对伺服电机系统的仿真与实验验证了设计工作的有效性.     

一类不确定系统基于滑模干扰补偿的广义预测控制

广义预测控制(GPC)是基于非线性机理模型的一种优化控制策略, 但当系统存在内部不确定性、建模动态误差和外部干扰的情况下, 采用基于标称系统模型的GPC方法的系统性能将显著下降. 为此, 针对一类不确定非线性系统, 首先分析设计了一种基于不确定模型的理想GPC控制律; 同时设计了一种滑模干扰补偿器(SMDC)对系统的复合干扰进行估计, 将其输出作为补偿控制与标称GPC控制律结合以消除不确定性和外干扰的影响, 并利用Lyapunov理论分析了闭环复合系统的性能; 最后将其应用于一种高超声速飞行器(HSV)姿态控制系统, 仿真结果表明该方法具有很好的鲁棒特性和干扰衰减特性.     

不确定性系统的非因果鲁棒学习控制

针对不确定性系统提出一种非因果鲁棒学习控制方法。该学习控制律的非因果学习部分通过标称系统的优化指标得到,鲁棒部分通过设计鲁棒加权来实现。首先,不考虑鲁棒部分的具体形式,推导出标称系统描述的学习控制律的鲁棒收敛性条件;然后,设计与系统不确定性相关的鲁棒加权,由鲁棒收敛性条件得到鲁棒加权的设计原则;最后,通过仿真实验验证了所提出方法的有效性,并分析了不同形式不确定性系统鲁棒设计的保守性。     

具有饱和执行器线性系统的综合PLCöVSC 控制

基于LQ理论和变结构方法,导出一种带有饱和执行机构和不确定性线性系统的混合PLC／VSC控制律．引入一组嵌套的Lyapunov级集,并根据分段Lyapunov函数导出控制律,该控制律含有不连续控制项和趋近率项．利用Lyapunov函数方法分析了闭环系统的稳定性．结合简化单摆系统验证了设计方法的有效性,在抗干扰能力和减少超调量上反映出所提出的控制器具有比PLC和PLC／LHG更佳的性能．     

基于干扰观测器的高超音速飞行器鲁棒反步控制

针对高超音速飞行器严格反馈不确定非线性MIMO系统,提出一种基于干扰观测器的鲁棒反步控制方法。该方法采用超扭曲算法设计干扰观测器以估计系统复合干扰,观测误差有限时间收敛。设计非线性反步控制律,引入鲁棒项使得系统满足干扰到性能输出的L2增益不超过设定的正实数,满足耗散不等式,使闭环系统跟踪误差一致最终有界稳定。仿真结果表明,所设计的控制律可以有效抑制系统复合干扰的影响,设计方法可行。     

多输入多输出非线性不确定系统连续高阶滑模控制

针对一类MIMO非线性不确定系统,提出一种新的连续高阶滑模控制算法.引入状态反馈使得系统高阶滑模控制问题等效转换为多变量不确定积分链的有限时间稳定问题,首先针对标称系统设计有限时间到达连续控制律,实现系统状态快速收敛,然后采用多变量非解耦形式超螺旋算法克服系统不确定性,实现鲁棒性,最终使得系统控制作用连续、滑模抖振得以大大抑制.基于二次型Lyapunov函数证明系统的有限时间稳定性.针对三阶不确定系统有限时间稳定和气垫船圆形航迹跟踪问题分别进行了仿真,验证了所提算法的有效性、鲁棒性.     

Output feedback second order sliding mode control for spacecraft attitude and translation motion

This paper studies an output feedback control problem for spacecraft position and attitude control when uncertainties related to system parameters and external disturbances are present. Firstly, a new finite-time control law is designed using second order sliding mode concepts. In the presence of external disturbances and inertia uncertainties, the new control law provides finite-time convergence and high tracking precision. Secondly, a new sliding-mode-based filter is developed to estimate the first time derivatives of attitude and position in finite time. Instead of the translational and angular velocity variables, the estimated derivative values are used for the controller design. The proposed controller with this filter is an output feedback controller since translational and angular velocity measurements are not required. The closed-loop system under this controller is non-homogeneous and the stability is proven by using concepts of a strong Lyapunov function and Lyapunov stability theory. The trajectories of the closed-loop system can be controlled to converge to a ball centered at the origin that can be made as small as desired. Numerical simulations of position and attitude control of spacecraft are given to demonstrate the performance of the proposed controller and filter.     

输出约束的有限时间自适应区间二型模糊输出反馈PMSM伺服控制

针对永磁同步电机驱动的伺服系统在不确定性摩擦和未知负载的影响下难以达到高精度的控制效果,提出一种基于区间二型模糊系统的带有输出约束的有限时间自适应输出反馈控制方案.首先,构建一个基于非线性扰动观测器的区间二型模糊状态观测器,分别完成对于未知扰动和速度的估计,区间二型模糊系统完成对于非线性摩擦的逼近;然后,在此基础上,结合滤波误差补偿机制和有限时间技术,引入障碍Lyapunov函数和反步控制技术设计输出约束的自适应区间二型模糊输出反馈控制器;最后,根据Lyapunov稳定性理论提出严格的稳定性分析,保证闭环系统的所有信号均是有限时间内有界的,并通过数值仿真和实验验证了所提出方法的有效性.     

Robust stability and stabilization of discrete-time non-linear systems: The LMI approach

The purpose of this paper is to convert the problem of robust stability of a discrete-time system under non-linear perturbation to a constrained convex optimization problem involving linear matrix inequalities (LMI). The nominal system is linear and time-invariant, while the perturbation is an uncertain non-linear time-varying function which satisfies a quadratic constraint. We show how the proposed LMI framework can be used to select a quadratic Lyapunov function which allows for the least restrictive non-linear constraints. When the nominal system is unstable the framework can be used to design a linear state feedback which stabilizes the system with the same maximal results regarding the class of non-linear perturbations. Of particular interest in this context is our ability to use the LMI formulation for stabilization of interconnected systems composed of linear subsystems with uncertain non-linear and time-varying coupling. By assuming stabilizability of the subsystems we can produce local control laws under decentralized information structure constraints dictated by the subsystems. Again, the stabilizing feedback laws produce a closed-loop system that is maximally robust with respect to the size of the uncertain interconnection terms.     

Robust limit cycle control in a class of nonlinear discrete-time systems

This paper studies generation of robust periodic solutions in a class of nonlinear discrete-time system. The sustained oscillations, with the desired frequency and amplitude, are achieved through the creation of the appropriate elliptic limit cycle in the phase plane of the uncertain closed-loop discrete-time system. In the first step, the nominal control law is designed to enforce the trajectories of the nominal closed-loop system to converge to the desired limit cycle. Next, considering uncertain terms, an additional robustifying term is designed. This term is added to the nominal controller to sustain the desirable stable oscillations in the presence of uncertain terms. The resulted robust controller brings the trajectories of the uncertain closed-loop discrete-time system to a boundary layer (with adjustable width) around the desired limit cycle. Moreover, the domain of attraction of the limit cycle and also the ultimate boundary layer around it are calculated via the Lyapunov analysis. Additionally, in order to verify the applicability of the proposed method, it is implemented on the discretised model of a spring–damper system. Computer simulations confirm the theoretical results in generating robust stable oscillations.     

参数不确定离散时间系统的有限时间输出反馈预见控制器设计

基于参数依赖的Lyapunov函数方法及LMI技巧,研究一类参数不确定离散时间系统的有限时间输出反馈预见控制问题.首先,采用预见控制理论中误差系统的方法,引入差分算子和离散提升技术,构造出包含未来目标值信号和干扰信号的信息的扩大误差系统,将有限时间预见控制问题转化为扩大误差系统的有限时间稳定性问题;然后,针对所推导出的扩大误差系统设计输出反馈预见控制器,通过改造输出方程以充分利用可预见信号的未来信息,并通过LMI技巧给出闭环系统有限时间稳定的条件及预见控制器的设计方法.研究结果表明,通过求解LMI,可确定静态输出反馈预见控制器增益矩阵.数值仿真表明了所提出方法的有效性.     

冷带轧机厚控系统自适应鲁棒输出反馈动态控制器设计

冷带轧机厚控系统可被认为是一个受外界干扰的线性不确定时滞系统.本文首先设计了标称系统下的鲁棒输出反馈动态控制器,以改善闭环系统的动静态性能;其次,在系统不需要满足不确定性匹配条件的情况下,将参数和外部扰动不确定性综合考虑.应用Lyapunov稳定性理论设计了系统不确定性上界参数的自适应估计器和系统的自适应控制器,保证了闭环系统的渐近稳定性,减小了设计的保守性;两者结合实现了板带出口厚度的有效控制.最后通过一个仿真实例说明本文所提出的自适应鲁棒控制器的有效性.     

Decentralized tube‐based model predictive control of uncertain nonlinear multiagent systems

This paper addresses the problem of decentralized tube‐based nonlinear model predictive control (NMPC) for a general class of uncertain nonlinear continuous‐time multiagent systems with additive and bounded disturbance. In particular, the problem of robust navigation of a multiagent system to predefined states of the workspace while using only local information is addressed under certain distance and control input constraints. We propose a decentralized feedback control protocol that consists of two terms: a nominal control input, which is computed online and is the outcome of a decentralized finite horizon optimal control problem that each agent solves at every sampling time, for its nominal system dynamics; and an additive state‐feedback law which is computed offline and guarantees that the real trajectories of each agent will belong to a hypertube centered along the nominal trajectory, for all times. The volume of the hypertube depends on the upper bound of the disturbances as well as the bounds of the derivatives of the dynamics. In addition, by introducing certain distance constraints, the proposed scheme guarantees that the initially connected agents remain connected for all times. Under standard assumptions that arise in nominal NMPC schemes, controllability assumptions, communication capabilities between the agents, it is guaranteed that the multiagent system is input‐to‐state stable with respect to the disturbances, for all initial conditions satisfying the state constraints. Simulation results verify the correctness of the proposed framework.     

Output feedback stabilization for time-delay nonlinear interconnected systems using neural networks.

In this paper, dynamic output feedback control problem is investigated for a class of nonlinear interconnected systems with time delays. Decentralized observer independent of the time delays is first designed. Then, we employ the bounds information of uncertain interconnections to construct the decentralized output feedback controller via backstepping design method. Based on Lyapunov stability theory, we show that the designed controller can render the closed-loop system asymptotically stable with the help of the changing supplying function idea. Furthermore, the corresponding decentralized control problem is considered under the case that the bounds of uncertain interconnections are not precisely known. By employing the neural network approximation theory, we construct the neural network output feedback controller with corresponding adaptive law. The resulting closed-loop system is stable in the sense of semiglobal boundedness. The observers and controllers constructed in this paper are independent of the time delays. Finally, simulations are done to verify the effectiveness of the theoretic results obtained.