严格反馈系统约束迭代学习控制

针对一类含有状态约束和任意初态的严格反馈非线性系统,本文提出了基于二次分式型障碍李雅普诺夫函数的误差跟踪学习控制算法.二次分式型障碍李雅普诺夫函数保证了系统跟踪误差在迭代过程中限制于预设的界内,进而保持状态在约束区间内.引入一级数收敛序列用于处理扰动对系统跟踪性能的影响.构造期望误差轨迹解决了系统的初值问题.经迭代学习后,所设计的学习控制器能够实现系统输出在预指定作业区间上精确跟踪参考信号.最后的仿真结果验证了所提控制算法的有效性.     

一类非线性系统模糊自适应固定时间量化反馈控制

研究了一类严格反馈不确定非线性系统的模糊自适应实际固定时间量化反馈控制问题. 基于李雅普诺夫有限时间稳定理论、自适应模糊控制理论及反演控制算法, 提出了一种非线性系统模糊自适应实际固定时间量化反馈跟踪控制方案. 所设计的控制方案能够保证闭环系统的输出跟踪误差在固定时间内收敛于原点的一个充分小邻域内, 且闭环系统内所有信号均有界. 最后, 数值示例验证了设计方案的有效性.     

航天器全状态约束输出反馈控制

针对无角速度测量的刚性航天器姿态跟踪问题,提出一种全状态约束输出反馈控制方法.建立修正罗德里格参数描述的系统模型,提出能够适用于约束与非约束情况的改进型障碍李雅普诺夫函数(MBLF),拓展传统对数型障碍李雅普诺夫函数的适用范围.构造二阶辅助系统,将控制输入和饱和输入之间的差作为构造系统的输入,进而产生信号以补偿饱和的影响.设计状态观测器估计未知状态量,并结合反步法设计输出反馈控制律,保证系统全状态约束性能和姿态跟踪精度.通过李雅普诺夫稳定性分析证明姿态观测误差和跟踪误差能够达到一致最终有界.仿真结果验证所提方法的有效性.     

输入输出受限船舶的轨迹跟踪自适应递归滑模控制

针对输入输出受限, 模型部分不确定和受到未知海洋干扰的全驱动船舶的轨迹跟踪问题, 提出一种基于时 变非对称障碍李雅普诺夫函数的最小参数自适应递归滑模控制策略. 该策略首先设计障碍李雅普诺夫函数约束船 舶轨迹在有限区域内, 利用最小参数法神经网络逼近模型不确定项, 降低系统的计算复杂度, 然后采用指令滤波器 对输入信号进行幅值约束, 同时避免对因反步法导致的微分爆炸问题, 综合考虑船舶位置以及速度误差间的关系设 计递归滑模控制律, 提高系统的鲁棒性, 采用双曲正切函数和Nussbaum函数补偿由输入饱和引起的非线性项, 提高 系统稳定性. 最后通过Lyapunov理论分析证明了全驱动船舶闭环系统中所有信号是一致最终有界的. 仿真结果表 明, 本文所设计的船舶轨迹跟踪控制方案能有效处理船舶模型不确定部分以及未知外界干扰的问题, 能够实现船舶 在输入受限的情况下在有限区域内航行并准确的跟踪期望轨迹, 具有较强的鲁棒性.     

带有输出约束的柔性关节机械臂预设性能自适应控制

针对带有输出约束和模型不确定的柔性关节机械臂系统,提出一种基于时变障碍李雅普诺夫函数的预设性能自适应控制方法.通过构造指数衰减的时变约束边界,提出时变正切型障碍李雅普诺夫函数,能够同时适用于约束与非约束情况,进而拓宽传统对数型障碍李雅普诺夫函数的适用范围.此外,通过预先设置时变边界函数的相关参数,使得系统输出在初始阶段具有较小的超调量和较快的跟踪速度,并能够满足系统的稳态性能要求.在此基础上,结合反演法设计反馈控制律,保证系统的输出约束性能和轨迹跟踪精度.最后,基于李雅普诺夫稳定性定理证明所有闭环信号能够达到一致最终有界,并给出数值仿真对比验证所提出方法的有效性.     

带有时变输出约束的机械臂自适应轨迹跟踪控制

针对带有输出约束和动力学模型参数未知的机械臂系统，提出一种基于时变tan型障碍李雅普诺夫函数的自适应控制方法.首先，通过设置时变约束边界，给出了一个时变tan型障碍李雅普诺夫函数，保证系统在初始误差较大情况下的瞬态性能和稳态性能，拓展了传统对数型障碍李雅普诺夫函数的适用范围.其次，为了处理机械臂动力学模型的不确定性，采用径向基神经网络(RBFNN)拟合未知的动力学模型，设计了基于RBFNN的自适应控制器，在满足约束的情况下提高了系统的鲁棒性.最后，通过二自由度机械臂轨迹跟踪的仿真，验证了所提方法的控制性能优于传统的PD控制器.     

基于神经网络的非线性系统稳定自适应控制

本文针对一类具有未知非线性函数和未知虚拟系数非线性函数的二阶非线性系统 ,提出了一种基于神经网络的稳定自适应输出跟踪控制方法 .用李雅普诺夫稳定性分析方法证明了本文的神经网络自适应控制器能够使受控系统稳定 ,并使输出跟踪误差随时间趋于无穷而收敛到零 .仿真算例证明了该算法的有效性     

基于神经网络观测器的船舶轨迹跟踪递归滑模 动态面输出反馈控制

针对三自由度全驱动船舶速度向量不可测问题,考虑船舶模型参数和外部环境扰动均未知的情况,提出一种基于神经网络观测器的船舶轨迹跟踪递归滑模动态面输出反馈控制方法.该方法设计神经网络自适应观测器估计船舶速度向量,且利用神经网络逼近模型参数不确定项,综合考虑船舶位置和速度误差之间关系构造递归滑模面,再采用动态面控制技术设计轨迹跟踪控制律和参数自适应律,并引入低频增益学习方法消除外界扰动导致的高频振荡控制信号.选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

基于RBF神经网络的船舶自适应区域到达控制

针对全驱动海洋水面船舶的区域到达控制问题,结合反演法,提出了一种基于RBF神经网络的自适应区域达到控制算法。与传统的定点控制方法不同,区域到达控制概念中将控制目标设定为以期望点为中心的空间区域。控制器的设计实现主要采用了目标势能函数、反演设计方法和李雅普诺夫稳定性理论。利用RBF神经网络,对全驱动船舶模型中的不确定函数及外部环境扰动进行有效逼近。通过李雅普诺夫理论,对所提出的船舶区域到达控制算法进行了稳定性分析,并证明了闭环系统的一致最终有界性。仿真研究结果验证了所设计的区域到达控制器的有效性。     

输入饱和约束下多智能体的有限时间一致

为解决多智能体系统在有限时间跟踪控制过程中受输入饱和影响的问题, 本文研究了输入饱和约束下的 二阶线性多智能体系统的有限时间一致和跟踪控制. 首先在无向通信拓扑下, 利用齐次函数, 设计了基于单饱和函 数的有限时间一致和跟踪控制器. 然后, 应用李雅普诺夫稳定性理论和代数图论等方法证明了控制算法的稳定性. 最后, 给出了能够使多智能体系统实现有限时间一致和跟踪的充分条件. 仿真结果验证了当系统存在输入饱和约束 时, 控制器能使多智能体在有限时间内完成跟踪任务.     

一类机械臂系统自适应有限时间有界H∞跟踪控制

考虑一种电机驱动的单连杆机械臂系统在受到输出约束时的自适应有限时间H∞跟踪控制问题.一个有限时间有界H∞性能的新概念被提出,并结合障碍Lyapunov函数(BLF)、神经网络自适应技术、有限时间控制理论和H_∞控制理论,提出了一种该系统在输出受限条件下的自适应神经有限时间有界H_∞跟踪控制器设计方法,避免了许多有限时间控制文献中控制器设计时出现的奇点问题,实现了该机械臂系统的有限时间有界H_∞轨迹跟踪控制.所设计的控制器保证了系统跟踪误差能够被约束在预先给定的范围内,并且所有状态能在有限时间内收敛到平衡状态的邻域内,同时对外部扰动具有H_∞性能.仿真结果验证了所设计控制器的有效性和优越性.     

Finite-time command filter-based adaptive tracking control for nonstrict feedback nonlinear systems with full-state restrictions and unmodeled dynamics

The finite-time command filter tracking control for a class of nonstrictly feedback nonlinear systems with unmodeled dynamics and full-state constraints is investigated in this paper. The hyperbolic tangent function is used as a nonlinear mapping technique to solve the obstacle of the full-state constraints. A new adaptive finite time control method is proposed through command filtering reverse engineering, and the shortcomings of the dynamic surface control (DSC) method are overcome by the error compensation mechanism. Dynamic signal is designed to handle dynamical uncertain terms. Normalization signal is designed to handle input unmodeled dynamics. Unknown nonlinear functions are approximated by radial basis function neural networks. Based on the Lyapunov stability theory, it is proved that all signals in the closed-loop system are semi-globally consistent and finally bounded and the output tracking error converges in finite time. Two numerical examples are utilized to verify the effectiveness of the proposed control approach.     

Adaptive Neural Dynamic Surface Control for Stochastic Nonlinear Time‐Delay Systems with Input and Output Constraints

This paper presents an adaptive neural tracking control approach for uncertain stochastic nonlinear time‐delay systems with input and output constraints. Firstly, the dynamic surface control (DSC) technique is incorporated into adaptive neural control framework to overcome the problem of ‘explosion of complexity’ in the control design. By employing a continuous differentiable asymmetric saturation model, the input constraint problem is solved. Secondly, the appropriate Lyapunov‐Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown time‐delay terms, RBF neural network is utilized to identify the unknown systems functions, and barrier Lyapunov functions (BLFs) are designed to avoid the violation of the output constraint. Finally, based on adaptive backstepping technique, an adaptive neural control method is proposed, and it decreases the number of learning parameters. Using Lyapunov stability theory, it is proved that the designed controller can ensure that all the signals in the closed‐loop system are 4‐Moment (or 2 Moment) semi‐globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two simulation examples are provided to further illustrate the effectiveness of the proposed approach.     

Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles

This paper studies the problem of stabilizing reference trajectories (also called as the trajectory tracking problem) for underactuated marine vehicles under predefined tracking error constraints. The boundary functions of the predefined constraints are asymmetric and time‐varying. The time‐varying boundary functions allow us to quantify prescribed performance of tracking errors on both transient and steady‐state stages. To overcome difficulties raised by underactuation and nonzero off‐diagonal terms in the system matrices, we develop a novel transverse function control approach to introduce an additional control input in backstepping procedure. This approach provides practical stabilization of any smooth reference trajectory, whether this trajectory is feasible or not. By practical stabilization, we mean that the tracking errors of vehicle position and orientation converge to a small neighborhood of zero. With the introduction of an error transformation function, we construct an inverse‐hyperbolic‐tangent‐like barrier Lyapunov function to show practical stability of the closed‐loop systems with prescribed transient and steady‐state performances. To deal with unmodeled dynamic uncertainties and external disturbances, we employ neural network (NN) approximators to estimate uncertain dynamics and present disturbance observers to estimate unknown disturbances. Subsequently, we develop adaptive control, based on NN approximators and disturbance estimates, that guarantees the prescribed performance of tracking errors during the transient stage of on‐line NN weight adaptations and disturbance estimates. Simulation results show the performance of the proposed tracking control.     

Adaptive neural output feedback finite-time command filtered backstepping control for nonlinear systems with full-state constraints

In this paper, an adaptive neural finite-time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high-order nonlinear systems with full-state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite-time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite-time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.     

Neural network-based sliding mode adaptive control for robot manipulators

This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.     

基于时变终端滑模的任意迭代初态抑制控制EI北大核心CSCD

为解决迭代学习过程中的任意迭代初值和迭代收敛理论证明难的问题,本文构造了一种轨迹跟踪误差初值恒位于滑模面内的时变终端滑模面,将轨迹跟踪误差初值不为零的轨迹跟踪控制问题转换为滑模面初值恒为零的滑模面跟踪控制问题,建立了任意迭代初值与相同迭代初值的迭代学习控制理论连接桥梁.本文提出一种基于时变滑模面的比例–积分–微分(PID)型闭环迭代学习控制策略,基于压缩映射原理证明了迭代学习的收敛性,给出了迭代收敛条件.时变终端滑模面经有限次迭代学习收敛到零,达到轨迹跟踪误差最终稳定在时变滑模面内的目的;Lyapunov稳定理论证明了位于滑模面内的轨迹跟踪误差在有限时间内收敛到原点,达到轨迹局部精确跟踪目的.随机初态下的工业机器人轨迹跟踪控制数值仿真验证了本文方法的有效性和系统对外部强干扰的鲁棒性.     

Finite-time adaptive neural control for uncertain nonlinear time-delay systems with actuator delay and full-state constraints

This paper investigates finite-time adaptive neural tracking control for a class of nonlinear time-delay systems subject to the actuator delay and full-state constraints. The difficulty is to consider full-state time delays and full-state constraints in finite-time control design. First, finite-time control method is used to achieve fast transient performances, and new Lyapunov–Krasovskii functionals are appropriately constructed to compensate time delays, in which a predictor-like term is utilized to transform input delayed systems into delay-free systems. Second, neural networks are utilized to deal with the unknown functions, the Gaussian error function is used to express the continuously differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are employed to guarantee that full-state signals are restricted within certain fixed bounds. At last, based on finite-time stability theory and Lyapunov stability theory, the finite-time tracking control question involved in full-state constraints is solved, and the designed control scheme reduces learning parameters. It is shown that the presented neural controller ensures that all closed-loop signals are bounded and the tracking error converges to a small neighbourhood of the origin in a finite time. The simulation studies are provided to further illustrate the effectiveness of the proposed approach.