四旋翼飞行器自适应动态面轨迹跟踪控制

针对具有未知外界扰动和系统不确定性集总未知非线性的四旋翼飞行器,提出了一种采用自适应不确定性补偿器的自适应动态面轨迹跟踪方法.通过将四旋翼飞行器系统分解为位置、欧拉角和角速率3个动态子系统,使各子系统虚拟控制器设计能充分考虑欠驱动约束;结合动态面控制技术,通过采用一阶低通滤波器,避免对虚拟控制信号求导;进而设计自适应不确定性补偿器,处理未知外界扰动和系统不确定性,最终确保闭环控制系统的稳定性、跟踪误差一致最终有界和系统所有状态信号有界.仿真研究和实验结果验证了本文提出控制方法的有效性和优越性.     

基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制

针对三自由度全驱动船舶存在模型不确定和未知外部环境扰动的情况,设计出一种基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应神经网络控制方法.该方法综合考虑船舶位置和速度误差之间关系设计递归滑模面,引入神经网络对船舶模型不确定部分进行逼近,设计带σ-修正泄露项的自适应律对神经网络逼近误差与外界环境扰动总和的界进行估计,并应用一种非线性增益函数构造动态面控制律,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、精度高,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.     

Adaptive robust motion/force control of holonomic-constrained nonholonomic mobile manipulators.

In this paper, adaptive robust force/motion control strategies are presented for mobile manipulators under both holonomic and nonholonomic constraints in the presence of uncertainties and disturbances. The proposed control is robust not only to parameter uncertainties such as mass variations but also to external ones such as disturbances. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance and the bounded constraint force. Simulation results validate that the motion of the system converges to the desired trajectory, and the constraint force converges to the desired force.     

基于模糊不确定观测器的四旋翼飞行器自适应动态面轨迹跟踪控制

针对具有未知外界扰动和系统不确定性的四旋翼飞行器,提出了一种基于模糊不确定观测器（Fuzzy uncertainty observer,FUO）的自适应动态面轨迹跟踪控制方法.通过将四旋翼飞行器系统分解为位置、姿态角和角速率三个动态子系统,使得各子系统虚拟控制器能够充分考虑欠驱动约束;采用一阶低通滤波器重构虚拟控制信号及其一阶导数,实现四旋翼跟踪控制设计的迭代解耦;设计了一种模糊不确定观测器,用以估计和补偿未知外界扰动与系统不确定性,从而确保闭环系统的稳定性和跟踪误差与其他系统信号的一致有界性.仿真研究验证了所提出的控制方法的有效性和优越性.     

基于干扰观测器的机械臂广义模型预测轨迹跟踪控制

为实现对多自由度机械臂关节运动精确轨迹跟踪,提出一种基于非线性干扰观测器的广义模型预测轨迹跟踪控制方法。针对机械臂轨迹跟踪运动学子系统,采用广义预测控制（Generalized Predictive Control,GPC）方法设计期望的虚拟关节角速度。对于机械臂轨迹跟踪动力学子系统,考虑机械臂的参数不确定性和未知外界扰动,利用GPC方法设计关节力矩控制输入,基于非线性干扰观测器方法实时估计和补偿系统模型中的不确定性。在李雅普诺夫稳定性理论框架下证明了机械臂关节角位置和角速度的跟踪误差最终收敛于零的小邻域。数值仿真验证了所提出控制方法的有效性和优越性。     

输入受限四旋翼飞行器的模糊自适应动态面轨迹跟踪控制

针对输入受限条件下四旋翼飞行器的轨迹跟踪控制问题,考虑系统存在模型动态不确定和未知外界干扰的情况,提出一种模糊自适应动态面轨迹跟踪控制方法.该方法设计干扰观测器估计位置模型中复合扰动项,利用模糊系统逼近姿态模型中不确定项和外界干扰,并引入双曲正切函数和辅助系统处理输入受限问题,结合反演法和动态面技术设计轨迹跟踪控制器,以降低控制算法的复杂性,最后选取李雅普诺夫函数证明闭环系统所有信号一致最终有界.应用大疆M100飞行器模型进行仿真验证,结果表明所设计的控制器能够有效处理模型动态不确定和未知外界干扰问题,避免飞行器工作过程中因输入饱和导致执行器失效现象,精确地完成轨迹跟踪控制任务.     

自适应神经网络四旋翼无人机有限时间轨迹跟踪控制

针对带有模型不确定性和未知外部干扰的四旋翼无人机轨迹跟踪控制问题,提出一种基于径向基(radial basis function, RBF)神经网络的自适应全局快速终端滑模控制方法,确保系统对期望轨迹的有限时间跟踪。该方法考虑到全局快速终端滑模控制在实际应用中的适应性和抖振问题,利用RBF神经网络替代等效控制量,以神经网络的在线学习能力补偿系统内部的不确定性和未知的外部干扰,有效地降低了系统的抖振;根据Lyapunov方法导出的自适应律在线调整神经网络权值,以保证闭环系统的稳定性。通过一系列仿真算例和飞行实验验证了该方法的有效性与可行性,结果表明:该控制方法相对于滑模控制的抖振更小,具有更好的收敛性和抗干扰能力,同时对模型的参数摄动具有更强的鲁棒性。     

基于收缩反步的不确定机械臂轨迹跟踪控制

针对不确定机械臂系统的轨迹跟踪控制问题,基于干扰观测器原理,提出了一种收缩反步控制算法.首先,采用非线性观测器对系统的模型不确定项和未知外部干扰部分进行观测.然后,使用收缩反步控制求解出控制输入力矩,从而实现对参考轨迹的精确跟踪,并分析二阶闭环系统的增量稳定性和Lyapunov方程解的原点指数稳定性.最后,将上述所提控制律应用于2-DOF机械臂,通过收缩反步与滑模控制的对比仿真,证明其有效性.     

Robust Neural Network Motion Tracking Control of Piezoelectric Actuation Systems for Micro/Nanomanipulation

This paper presents a robust neural network motion tracking control methodology for piezoelectric actuation systems employed in micro/nanomanipulation. This control methodology is proposed for tracking of desired motion trajectories in the presence of unknown system parameters, nonlinearities including the hysteresis effect and external disturbances in the control systems. In this paper, the related control issues are investigated, and a control methodology is established including the neural networks and a sliding control scheme. In particular, the radial basis function (RBF) neural networks are chosen for function approximations. The stability of the closed-loop system, as well as the convergence of the position and velocity tracking errors to zero, is assured by the control methodology in the presence of the aforementioned conditions. An offline learning procedure is also proposed for the improvement of the motion tracking performance. Precise tracking results of the proposed control methodology for a desired motion trajectory are demonstrated in the experimental study. With such a motion tracking capability, the proposed control methodology promises the realization of high-performance piezoelectric actuated micro/nanomanipulation systems.      

基于扩张观测器的欠驱动船舶轨迹跟踪低频学习自适应动态面输出反馈控制

针对速度不可测的三自由度欠驱动船舶轨迹跟踪控制问题,考虑船舶存在模型参数不确定项以及外界环境干扰未知情况,提出一种基于扩张观测器的欠驱动船舶轨迹跟踪低频学习自适应动态面输出反馈控制策略.该策略构造扩张观测器估计船舶速度向量,利用神经网络算法逼近模型参数不确定项,然后采用动态面控制技术避免对虚拟控制律直接求导,简化控制律计算过程,并引入低频增益学习技术消除外界扰动导致控制信号产生高频振荡,最后选取李雅普诺夫函数证明该控制律能够保证船舶跟踪闭环系统中所有误差信号一致最终有界.仿真结果表明,本文所设计控制器对船舶模型参数不确定项及外界环境干扰具有较强的鲁棒性,能够实现对船舶轨迹的有效跟踪.     

基于扰动观测器的机械臂自适应反演滑模控制

机械臂的动力学模型通常包含一定的结构不确定性,并受到外界未知干扰的影响。针对现有模型的不确定性特点,提出了一种基于非线性扰动观测器的自适应反演滑模控制方法,解决机械臂的轨迹跟踪控制问题。对于外界干扰,利用非线性扰动观测器进行观测补偿,无需上界先验知识;对于结构不确定性,引入反演滑模控制,同时设计自适应律,保证闭环系统的稳定性并增强系统的动态适应性。仿真结果证明,所提出的方法可以有效克服系统不确定性,降低控制输入信号的抖振,最终实现期望轨迹的快速精确跟踪。     

Adaptive NN state error PCH trajectory tracking control for unmanned surface vessel with uncertainties and input saturation

A novel robust state error port controlled Hamiltonian (PCH) trajectory tracking controller of an unmanned surface vessel (USV) subject to time-varying disturbances, dynamic uncertainties and control input saturation is presented. The proposed control scheme combines the advantages of the high robustness and energy minimization of the state error PCH approach and the approximation capability of adaptive radial basis function neural networks (RBFNNs). Adaptive RBFNNs are used to the time-varying disturbances of the environment and unknown dynamics uncertainties of the USV model. The state error PCH control approach is designed such that the system can optimize energy consumption, and the state error PCH technique makes the designed trajectory tracking controller be easy to implement in practice. To handle the effect of the control input saturation, a Gaussian error function model is employed. It has been demonstrated that the proposed approach can maintain the USV's trajectory at the desired trajectory, while the closed-loop control system can guarantee the uniformly ultimate boundedness. The energy consumption model of the USV is constructed to reveal to the energy consumption. Simulation results demonstrate the effectiveness of the proposed controller.     

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

With regard to precision/ultra-precision motion systems, it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances. In this paper, to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control (ILC), a novel real-time iterative compensation (RIC) control framework is proposed for precision motion systems without changing the inner closed-loop controller. Specifically, the RIC method can be divided into two parts, i.e., accurate model prediction and real-time iterative compensation. An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times. In light of predicted errors, a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation. Both the prediction and compensation processes are finished in a real-time motion control sampling period. The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions. Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability, which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.      

基于无源性的全方位移动机器人自抗扰控制

针对全方位移动机器人轨迹跟踪控制中存在的外界干扰和系统参数不确定性问题,提出基于无源性的自抗扰控制方法.该方法通过扩张状态观测器对系统扰动进行估计,并在基于无源性的控制器中加入扰动补偿项以减小外界干扰和参数不确定性对系统的影响;进而,利用系统的无源特性和Lyapunov 理论证明在该控制器作用下闭环系统有界输入有界输出稳定.仿真结果表明,所提出的控制方法响应速度较快,控制精度较高,对系统外扰和模型参数不确定性具有较强的鲁棒性     

Robust Adaptive Control of Cooperating Mobile Manipulators With Relative Motion

In this paper, coupled dynamics are presented for two cooperating mobile robotic manipulators manipulating an object with relative motion in the presence of uncertainties and external disturbances. Centralized robust adaptive controls are introduced to guarantee the motion, and force trajectories of the constrained object converge to the desired manifolds with prescribed performance. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The tracking of the constraint trajectory/force up to an ultimately bounded error is achieved. The proposed adaptive controls are robust against relative motion disturbances and parametric uncertainties and are validated by simulation studies.     

四旋翼无人飞行器的轨迹跟踪与滑模事件驱动控制

四旋翼飞行器作为一个典型的欠驱动的系统,具有强耦合、非线性等特性.针对飞行器外部干扰、和通信资源受限条件下的轨迹跟踪控制问题,进行滑模事件驱动控制方法的研究.首先,分析动力学特性,通过时间尺度分解方法将系统解耦成位置子系统和姿态子系统.其次,将位置子系统转化为严格反馈形式,设计反步滑模控制器,实现位置轨迹稳定跟踪;针对姿态子系统存在时变有界扰动及通信受限,设计滑模事件驱动控制律,在抑制干扰的同时实现对虚拟姿态跟踪指令的跟踪.根据Lyapunov分析方法证明了所设计控制器的稳定性,并通过理论分析证明闭环控制系统不会出现Zeno现象.最后,仿真结果验证了滑模事件驱动控制律在存在外部扰动和通信受限时四旋翼无人飞行器轨迹跟踪的鲁棒性.     

Decentralized adaptive tracking control of robot manipulators

An asymptotically stable decentralized adaptive control scheme is presented to enable accurate trajectory tracking without requiring specific knowledge about the robot dynamics. The scheme is based on expressing the robot dynamics as the product of individual joint quantities, and bounds on certain robot parameters. Parameter adaptation laws are derived using the Lyapunov theory, and asymptotic stability of tracking errors, and boundedness of parameter estimates are established. The control system is shown to be robust to torque disturbances affecting the system and to a class of unmodeled dynamics. The structure of the controller and the performance of the closed-loop system are analyzed. Simulations results using the complete dynamic model of a six degree of freedom industrial robot are presented to demonstrate the excellent tracking performance of the proposed adaptive control scheme. © 1996 John Wiley & Sons, Inc.     

Observer-based Adaptive Fuzzy Backstepping Tracking Control of Quadrotor Unmanned Aerial Vehicle Powered by Li-ion Battery

In this paper, an Adaptive Fuzzy Backstepping Control (AFBC) approach with state observer is developed. This approach is used to overcome the problem of trajectory tracking for a Quadrotor Unmanned Aerial Vehicle (QUAV) under wind gust conditions and parametric uncertainties. An adaptive fuzzy controller is directly used to approximate an unknown nonlinear backstepping controller which is based on the exact model of the QUAV. Besides, a state observer is constructed to estimate the states. The stability analysis of the whole system is proved using Lyapunov direct method. Uniformly Ultimately Bounded (UUB) stability of all signals in the closed-loop system is ensured. The proposed control method guarantees the tracking of a desired trajectory, attenuates the effect of external disturbances such as wind gust, and solves the problem of unavailable states for measurement. Extended simulation studies are presented to highlight the efficiency of the proposed AFBC scheme.     

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.