具有角速度和输入约束的航天器姿态协同控制

提出了基于有向图的航天器姿态协同控制算法, 并且系统的角速度和控制输入满足有界性的约束. 当外部扰动存在时, 设计了自适应算法估计扰动的上界, 采用滤波器补偿的方法处理控制输入饱和问题, 并且设计了新的自适应姿态协同控制算法. 对于所设计的控制算法, 给出了稳定性分析, 证明了系统具有几乎全局渐近稳定性. 进一步把控制算法推广到时变通信时滞情况, 当控制器参数满足一定条件时, 仍然能够保证编队系统的几乎全局渐近稳定性. 通过数值仿真, 验证了所提出的控制方案的有效性.     

姿态变化一致有界的姿态稳定控制器设计

针对航天器姿态稳定控制问题, 设计一种迭代学习姿态控制器. 将连续非周期运动的姿态跟踪过程分解为队列重复运动, 采用前一周期的姿态跟踪误差修正后一周期的控制输入, 分别对未知参数和干扰构建有界迭代学习律, 给出航天器姿态稳定控制器, 并从理论上分析了闭环系统的渐近稳定性和姿态跟踪误差的一致有界性. 通过在轨捕获非合作目标过程中航天器姿态跟踪控制问题的数值仿真, 验证了迭代学习控制器的鲁棒性和强抗干扰性.

     

考虑执行器性能约束的刚体航天器鲁棒姿态跟踪控制

针对存在模型不确定性、外界干扰力矩和执行器性能受限等约束条件下的刚体航天器姿态跟踪控制问题进行研究,并基于滑模控制、反步控制、自适应控制、辅助系统和动态面控制等方法设计相应的鲁棒姿态跟踪控制算法.利用自适应控制实现了对具有多项式形式上界函数的系统未知不确定性进行在线估计和补偿;通过建立描述执行器动态特性的低通滤波模型,并结合辅助系统方法,以确保执行器输出控制力矩的幅值及其变化率均满足一定的饱和约束;通过引入动态面控制法,避免期望虚拟控制信号的一阶导数项直接出现在控制器中,简化了闭环姿态跟踪控制器的设计形式.最后,通过数值仿真验证了所提出控制算法的有效性和可行性.     

考虑输入受限的航天器安全接近姿轨耦合控制

针对存在外部扰动和输入受限的航天器安全接近的问题,当扰动上界未知时,基于积分滑模控制理论设计了抗饱和的有限时间自适应姿轨耦合控制器.控制器的设计过程中采用了新型的避碰函数限制追踪航天器运动区域进而保证接近过程中航天器的安全性,同时通过辅助系统和自适应算法分别处理了输入受限和扰动上界未知.借助李雅普诺夫理论证明了在控制器的作用下系统状态在有限时间内收敛,且能够保证追踪航天器在实现航天器接近的过程中不与目标航天器发生碰撞.最后通过数字仿真进一步验证了所设计控制器的有效性.     

未知时变惯量航天器自适应姿态跟踪容错控制

针对存在未知时变惯量不确定性、执行机构衰退故障和外部干扰力矩的非刚体航天器系统,研究了航天器自适应姿态跟踪容错控制问题,结合非线性鲁棒控制方法、自适应方法、容错控制理论和参数估计方法,提出了一种鲁棒自适应姿态跟踪容错控制器。所设计的控制器克服了执行器故障、惯量不确定性以及外界干扰对系统稳定性的影响,保证了航天器姿态及角速度能够跟踪上时变的期望状态,实现了跟踪误差系统最终一致有界稳定。最后通过数字仿真验证了所提方法的有效性,并且与已有方法进行了对比,说明了所提方法的优越性。     

卫星姿态直接自适应模糊预测控制

对具有模型不确定性和未知外干扰的卫星姿态系统提出了多输入多输出直接自适应模糊预测跟踪控制设计方法. 此方法先基于卫星姿态动力学模型设计出非线性广义预测控制律, 再构造直接自适应模糊控制器逼近预测控制律中因模型不确定性引起的未知项. 文中证明了所设计的控制律能使卫星跟踪给定的期望姿态轨迹, 跟踪误差收敛到原点的小邻域内. 仿真结果验证了此方法的有效性.     

受限指令预设性能自适应反演控制器设计

针对一类含参数不确定性的输入和状态受限的非线性系统,提出一种受限指令预设性能自适应反演控制器设计方法.采用自适应反演方法,同时考虑输入和状态受限构建受限指令滤波器,以解决“计算膨胀”的问题;引入伪控制减缓方法对误差进行补偿;最后考虑了补偿跟踪误差的瞬态性能.通过Lyapunov理论对所设计的控制器进行稳定性分析,并通过仿真实例验证了所提出方法的正确性和有效性.     

飞轮安装偏差的过驱动航天器有限时间姿态控制

针对反作用飞轮安装存在偏差的过驱动航天器姿态跟踪问题, 提出一种有限时间姿态补偿控制策略. 通过设计自适应滑模控制器保证实现对不确定性转动惯量与外部干扰的鲁棒控制, 同时实现对飞轮安装偏差的补偿控制, 并应用Lyapunov 稳定性理论证明了该控制器能够在有限时间内实现姿态跟踪控制. 最后, 将该控制器应用于某型航天器的姿态跟踪控制, 仿真结果验证了所提出方法的有效性.     

航天器平移及姿态机动自适应终端滑模控制

研究了用于航天器平移及姿态机动的自适应终端滑模控制方法.通过在广义准坐标下建立拉格朗日方程得到了刚体航天器平移及姿态耦合运动的动力学方程.能对存在模型不确定性和环境扰动下的航天器实现平移和姿态机动.该自适应过程包括对不确定性和干扰的估计、有效抑制传统滑模控制的抖振现象.利用李雅普诺夫稳定性理论证明了控制器的可达性和稳定性.通过航天器的位置以及姿态跟踪的数值仿真,验证了所设计控制器的有效性和准确性.     

接近非合作目标的部分状态反馈姿轨联合控制

飞行器在轨精度跟踪优化问题,根据在轨服务任务需求,针对非合作翻滚目标航天器的自主接近跟踪问题,首先从采用视线坐标系表示的轨道动力学方程和修正罗德里格斯参数表示的姿态动力学方程出发,建立了航天器六自由度类拉格朗日动力学方程;然后仅利用相对位置和相对姿态反馈信息并针对航天器惯性参数不确定性,采用自适应非线性输出反馈控制和神经网络逼近控制方法设计了姿轨联合控制器,并通过Lyapunov直接法证明了该闭环系统的全局渐进稳定性;最后通过仿真验证了采用的方法能够实现对非合作目标的精确跟踪.     

Nonlinear Robust Tracking Control of a Quadrotor UAV on SE(3)

This paper provides nonlinear tracking control systems for a quadrotor unmanned aerial vehicle (UAV) that are robust to bounded uncertainties. A mathematical model of a quadrotor UAV is defined on the special Euclidean group, and nonlinear output‐tracking controllers are developed to follow (i) an attitude command, and (ii) a position command for the vehicle center of mass. The controlled system has the desirable properties that the tracking errors are uniformly ultimately bounded, and the size of the ultimate bound can be reduced arbitrarily by control system parameters. Numerical examples illustrating complex maneuvers are provided.     

Saturated Adaptive Output-Constrained Control of Cooperative Spacecraft Rendezvous and Docking

This paper investigates the robust relative pose control for spacecraft rendezvous and docking with constrained relative pose and saturated control inputs. A barrier Lyapunov function is used to ensure the constraints of states, so that the computational singularity of the inverse matrix in control command can be avoided, while a linear auxiliary system is introduced to handle with the adverse effect of actuator saturation. The tuning rules for designing parameters in control command and auxiliary system are derived based on the stability analysis of the closed-loop system. It is proved that all closed-loop signals always keep bounded, the prescribed constraints of relative pose tracking errors are never violated, and the pose tracking errors ultimately converge to small neighborhoods of zero. Simulation experiments validate the performance of the proposed robust saturated control strategy.      

Robust global uniform asymptotic stabilization of underactuated surface vessels with unknown model parameters

The full‐state stabilization scheme is proposed for the control of an underactuated surface vessel with unknown modeling parameters. By knowing only the upper/lower bounds of model parameters, the designed controller is the first one able to globally uniformly asymptotically stabilize all the states of the vessel to zero. The virtual surge velocity control law is first derived, which makes the Lyapunov function at the kinematic level non‐increasing, irrelevant to the yaw velocity, leaving a freedom for choosing the virtual yaw velocity control law to stabilize the other state variables. After finishing the design of virtual velocity law, the back‐stepping approach and the Lyapunov redesign technique are combined to obtain the actual force/torque control law despite parameter uncertainties. Simulation examples are given to illustrate the effectiveness of the proposed control law, showing that all the states and the control inputs are globally uniformly asymptotically convergent to zero under parameter uncertainties and are globally bounded under unknown external bounded disturbances. Copyright © 2013 John Wiley & Sons, Ltd.     

机械臂鲁棒自适应运动控制

针对具有不确定性的机械臂系统,文中阐述了一种基于势函数和Lyapunov稳定性理论的鲁棒自适应控制方法．它是通过合理选择与控制目标相关的势函数,并根据模型中不确定性的实时变化,在控制器中引入可在线可调参数,使得控制器机械臂能够跟踪给定的有界参考信号,跟踪误差收敛到包含零点的很小的邻域内．同时该闭环系统的所有状态半全局最终一致有界（SGUUB）．仿真研究表明该方法的有效性．     

Robust adaptive attitude control for flexible spacecraft in the presence of SGCMG friction nonlinearity

This paper investigates attitude maneuver control issues of a flexible spacecraft with pyramid‐type single gimbaled control moment gyroscopes (SGCMGs) as the actuator. The LuGre friction model is adopted to precisely describe the nonlinearity of the SGCMG gimbal friction. Aiming at restraining the adverse effects of the friction existed in SGCMG on the attitude control performance, a robust adaptive attitude controller is proposed, and projection‐based adaptive laws are presented to estimate the friction parametric uncertainties and the bound of friction nonlinearity. By treating the flexible mode coupling effect and external disturbances as lump disturbances, the inertia uncertainties and the bound of the lump disturbances are also estimated and compensated simultaneously to reduce their adverse effect on the system. With the Lyapunov technique, the states of flexible spacecraft control system are proved to be uniformly ultimately bounded. Numerical simulations demonstrate the effectiveness of the proposed scheme.     

Robust adaptive control of strict-feedback nonlinear systems with unmodelled dynamics and time-varying delays

This paper presents a novel robust adaptive neural control scheme which can be taken as a robustification of the adaptive backstepping design. The considered class of uncertainties contains unknown non-symmetric dead-zone inputs, time-varying delay uncertainties, unknown dynamic disturbances and unmodelled dynamics. The radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear functions obtained by Young’s inequality. By constructing exponential Lyapunov-Krasovskii functionals, the upper bound functions of the time-varying delay uncertainties are compensated for. Using Young’s inequality and RBFNNs, the assumptions with respect to unmodelled dynamics are relaxed. It is demonstrated that the proposed controller guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error eventually converges to a neighbourhood of zero.     

Robust output tracking for non-linear systems

The problem of output tracking for a single-input single-output non-linear system in the presence of uncertainties is studied. The notions relative degree and minimum-phase for non-linear systems are reviewed. Given a bounded desired tracking signal with bounded derivatives, a control law is designed for minimum-phase non-linear systems which results in tracking of this signal by the output. This control law is modified in the presence of uncertainties associated with the model vector fields to reduce the effects of these uncertainties on the tracking errors. Two types of uncertainties are considered: those satisfying a generalized matching condition but otherwise unstructured, and linear parametric uncertainties. It is shown that for systems with the first type of uncertainty, high-gain control laws can result in small tracking errors of O(?), where e is a small design parameter. An alternative scheme based on variable structure control strategy is shown to yield zero tracking errors. Adaptive control techniques are used for systems with linear parametric uncertainties. For systems with relative degree larger than one, a new adaptive control scheme is presented which is considerably simpler than the augmented error scheme suggested previously by Narendra et al. (1978) for linear systems and by Sastry and Isidori (1987) for non-linear systems. Contrary to the augmented error scheme, however, this scheme results in small rather than zero tracking errors.     

Robust adaptive neural sliding mode control of MEMS triaxial gyroscope with angular velocity estimation

In this paper, a robust adaptive sliding mode control strategy of micro electro-mechanical system (MEMS) triaxial gyroscope using radial basis function (RBF) neural network is presented for the system identification of MEMS gyroscope. A key property of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. An adaptive RBF neural network controller is used to learn the unknown upper bound of model uncertainties and external disturbances. The adaptive RBF neural network is incorporated into the adaptive sliding mode control in the Lyapunov sense, and the stability of the proposed adaptive neural sliding mode control can be established. The dynamics and angular velocities of gyroscope can be identified in real time. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme, showing that the designed control system has better robust performance in its insensitivity to system nonlinearities; moreover, system parameters including angular velocity can be consistently estimated and tracking errors converge to zero asymptotically.     

Neural-Network-Based Decentralized Adaptive Control for a Class of Large-Scale Nonlinear Systems With Unknown Time-Varying Delays

A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov–Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.      

Computation of discriminating kernel and robust capture basin with regulation map by interval methods

This paper proposes a novel algorithm that characterizes the robust capture basin and the discriminating kernel for constrained nonlinear systems with uncertainties based on viability theory. For nonlinear systems with constrained inputs and bounded uncertainties, the viability kernel is the largest set of states possessing a possibility to be viable in a set, and the capture basin is the largest set of states possessing a possibility to reach a target in a finite time, and keeping viable in a set before reaching the target. However, in the viability theory, both control and uncertainty in a parameterized system are considered as parameters: the discriminating kernel and the proposed robust capture basin link viability theory with robust control, which take both control and uncertainties into account. For the constrained uncertain nonlinear systems, the discriminating kernel is the largest set of states that is robust invariant in a set with proper control, and the robust capture basin is the largest set of states reaching their target in finite time with proper control despite of uncertainties and keeping viable in a set before reaching the target. Furthermore, we map all the states to optimal regulatory control such that the systems are regulated by a regulation map. To compute the robust capture basin and the discriminating kernel, we use interval methods to provide guaranteed solutions. The proposed algorithms in this paper approximate an outer approximation of the minimum reachable target and inner approximations of the robust capture basin and the discriminating kernel in a guaranteed way.