Leader–follower type relative position keeping in satellite formation flying via robust exponential stabilization

Satellite formation flying is an essential technology in both the current and the future aerospace industry. Conducting a fast and safe flying formation between satellites is always a key requirement for successful mission accomplishment. This paper addresses a leader–follower type relative position keeping in satellite formation flying via a novel robust exponential stabilization. This paper establishes an exponentially convergent relative position keeping taking account of various external disturbances that cannot be modeled into current formation dynamics, along with a set of time‐varying model parameters and measurement noises. The condition for an exponential convergence is established by decomposing the positive definite matrix used in Lyapunov function candidate into several element‐wise submatrices. Through numerical simulations, the effectiveness of the proposed method is illustrated. Copyright © 2011 John Wiley & Sons, Ltd.     

Formation flying orbit design for the distributed synthetic aperture radar satellite

Formation flying orbit design is one of the key technologies for system design and performance analysis of the distributed SAR satellites. The approximately analytic solution of the passive stable formation flying orbit elements is explored based on the expansion form of Kepler's equation. A new method of orbital parameters design for three-dimensional formation flying SAR satellites is presented, and the precision of the orbital elements is analyzed. Formation flying orbit elements are calculated for the L-Band distributed SAR satellites using the formulas deduced in this paper. The accuracy of the orbital elements is validated by the computer simulation results presented in this paper.     

Formation control for multi‐agent systems through an iterative learning design approach

This paper deals with formation control problems for multi‐agent systems by using iterative learning control (ILC) design approaches. Distributed formation ILC algorithms are presented to enable all agents in directed graphs to achieve the desired relative formations perfectly over a finite‐time interval. It is shown that not only asymptotic stability but also monotonic convergence of multi‐agent formation ILC can be accomplished, and the convergence conditions in terms of linear matrix inequalities can be simultaneously established. The derived results are also applicable to multi‐agent systems that are subject to stochastic disturbances and model uncertainties. Furthermore, the feasibility of convergence conditions and the effect of communication delays are discussed for the proposed multi‐agent formation ILC algorithms. Simulation results are given for uncertain multi‐agent systems to verify the theoretical study. Copyright © 2012 John Wiley & Sons, Ltd.     

应用控制系统设计工具箱PIMCSD求解卫星编队重构问题

利用线性时变系统终端约束最优控制方法,为椭圆轨道卫星编队的队形重构控制问题设计了均衡耗能最优控制器.由于描述椭圆轨道卫星编队相对运动的Lawden方程是时变方程,给卫星编队重构的最优控制器设计带来一定的困难.利用基于精细积分算法的控制系统设计工具箱-PIMCSD进行系统设计,求解卫星编队重构所需的时变最优反馈控制律和前馈控制律,最后给出了由三颗卫星组成的编队队形重构控制仿真计算结果.     

基于T-H方程的编队卫星碰撞概率与规避策略研究

基于T-H方程,对椭圆轨道下编队卫星碰撞预测以及规避机动进行了研究。通过递推编队卫星的初始状态协方差矩阵,将碰撞概率密度在危险域内积分获得编队卫星的碰撞概率。当碰撞概率大于安全阈值时,采用瞬时校正速度的控制策略对卫星施加最小脉冲速度修正量,在所预测的碰撞点沿碰撞概率梯度产生偏移,到达安全等高线上,从而降低碰撞概率。仿真结果表明,该预测方法有效,规避策略可行。     

Iterative learning control design with high‐order internal model for discrete‐time nonlinear systems

In this paper, a high‐order internal model (HOIM)‐based iterative learning control (ILC) scheme is proposed for discrete‐time nonlinear systems to tackle the tracking problem under iteration‐varying desired trajectories. By incorporating the HOIM that is utilized to describe the variation of desired trajectories in the iteration domain into the ILC design, it is shown that the system output can converge to the desired trajectory along the iteration axis within arbitrarily small error. Furthermore, the learning property in the presence of state disturbances and output noise is discussed under HOIM‐based ILC with an integrator in the iteration axis. Two simulation examples are given to demonstrate the effectiveness of the proposed control method. Copyright © 2016 John Wiley & Sons, Ltd.     

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.      

Consensus seeking via iterative learning for multi‐agent systems with switching topologies and communication time‐delays

This paper deals with the high‐precision consensus seeking problem of multi‐agent systems when they are subject to switching topologies and varying communication time‐delays. By combining the iterative learning control (ILC) approach, a distributed consensus seeking algorithm is presented based on only the relative information between every agent and its local (or nearest) neighbors. All agents can be enabled to achieve consensus exactly on a common output trajectory over a finite time interval. Furthermore, conditions are proposed to guarantee both exponential convergence and monotonic convergence for the resulting ILC processes of multi‐agent consensus systems. In particular, the linear matrix inequality technique is employed to formulate the established convergence conditions, which can directly give formulas for the gain matrix design. An illustrative example is included to validate the effectiveness of the proposed ILC‐motivated consensus seeking algorithm. Copyright © 2016 John Wiley & Sons, Ltd.     

地球J_2摄动下卫星编队飞行自主相对定轨研究

卫星编队飞行自主相对定轨作为实现编队队形保持和控制的前提,是编队任务必须解决的关键技术之一.设计了一种应用于卫星编队的自主相对轨道确定方案,不同于目前广泛采用的C-W方程,采用了包含摄动影响的相对运动方程的解析表达式描述编队飞行,使得模型的精度有了很大提高.利用星间距离信息和方位信息作为观测量,设计扩展卡尔曼滤波器实现环绕星相对轨道的自主确定,仿真结果表明使用本方案得到的定轨精度比采用C-W方程提高一个数量级,验证了这种导航方案的有效性,为实现编队飞行卫星高精度自主相对定轨提供了一个有效的方案.     

Adaptive ILC algorithms of nonlinear continuous systems with non-parametric uncertainties for non-repetitive trajectory tracking

In this article, two adaptive iterative learning control (ILC) algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced.