一类不确定系统的最优极小极大鲁棒控制

讨论一类不确定系统的极小极大鲁棒动态输出反馈控制问题.给出不确定系统的极小极大鲁棒控制的定义.利用线性矩阵不等式(LMI)处理方法和Lyapunov稳定性理论,得到了在干扰和不确定性最大的情形下极小极大输出反馈控制器存在的充分条件.引入凸优化技术, 求得最优极小极大控制器.它不仅保证闭环系统渐近稳定, 且使得闭环系统性能指标的上界最小.仿真算例说明了所设计的控制器具有较强的干扰抑制功能.     

Guaranteed robust nonlinear minimax estimation

Minimax parameter estimation aims at characterizing the set of all values of the parameter vector that minimize the largest absolute deviation between the experimental data and the corresponding model outputs. It is well known, however, to be extremely sensitive to outliers in the data resulting, e.g., of sensor failures. In this paper, a new method is proposed to robustify minimax estimation by allowing a prespecified number of absolute deviations to become arbitrarily large without modifying the estimates. By combining tools of interval analysis and constraint propagation, it becomes possible to compute the corresponding minimax estimates in an approximate but guaranteed way, even when the model output is nonlinear in its parameters. The method is illustrated on a problem where the parameters are not globally identifiable, which demonstrates its ability to deal with the case where the minimax solution is not unique.     

On numerical solution of some problems of minimax control

Consideration was given to two classes of problems of optimal control with functionals of the discrete maximum type. The problem of improving the permissible controls was solved on the basis of a system of integral equations in the functional variation parameters. The necessary optimality conditions were due to the inconsistency of the introduced system of relations. The improvement iteration required solution of special optimal control problems minimizing the final state norm.     

基于鲁棒控制Lyapunov 函数的非线性预测控制

针对一类约束不确定性非线性仿射系统,提出一种可保证闭环系统鲁棒镇定的非线性模型预测控制算法.利用鲁棒控制Lyapunov函数得到改进的Sontag公式,并以此为基础,构造一种计算有效的单自由度鲁棒预测控制器.以Matlab语言为仿真工具,对一开环不稳定振荡器进行了仿真研究,结果表明,利用该控制算法得到的闭环系统不仅渐近稳定于原点,而且所得控制量和系统状态都满足系统约束,从而验证了控制算法的有效性.     

Universal controllers for robust control problems

In this paper we discuss the construction of “universal” controllers for a class of robust stabilization problems. We give a general theorem on the construction of these controllers, which requires that a certain nonlinear inequality is solvablepointwisely or, equivalently, that arobust control Lyapunov function does exist. The constructive procedure producesalmost smooth controllers. The robust control Lyapunov functions extend to uncertain systems the concept of control Lyapunov functions. If such a robust control Lyapunov function also satisfies a small control property, the resulting stabilizing controller is also continuous in the origin of the state space. Applications of our results range from optimal to robust control.     

Nonlinear least-absolute-values and minimax model fitting

Up to now, the least-absolute-values (l₁) and minimax (l_∞) criteria have seldom been used in model fitting, even if they are preferable for theoretical reasons: their non-differentiability causes numerical and analytical difficulties. Fortunately, a recent reformulation of l₁ and l_∞ as equivalent constrained differentiable problems enables one to numerically solve them using standard nonlinear programming software. This paper exploits this reformulation for analytical purposes. In particular, limits are derived concerning the ability of l₁ and l_∞ to resolve highly similar, nonlinearly parametric terms from error corrupted observations. Examples are exponentials of nearly equal decay in compartmental models and almost coinciding peaks in spectroscopy.     

Penalty method for nonsmooth minimax control problems with interdependent variables

The minimum of a nonsmooth functional is sought on the solutions of a dynamic system with an indeterminate parameter and phase constraints. The original problem is reduced to a minimization problem in which the nonsmoothness is removed automatically. Compatibility conditions for the penalty coefficients are established and a maximum principle is derived.Translated from Kibernetika, No. 4, pp. 52–56, July–August, 1989.     

汽轮发电机主汽门开度非线性鲁棒控制

由于外界干扰以及各种故障的影响, 电力系统的结构并不是不变的, 因此其中的一些重要参数, 如输电线路电抗等, 总是包含某种不确定性. 针对含有参数不确定性的汽轮机调速系统, 使用自适应逆推方法及Lyapunov方法设计了非线性鲁棒汽门控制器, 基于该方法的设计, 设计过程系统、简明, 且不必对原系统进行线性化, 通过对单机无穷大系统的仿真表明了该控制器的有效性.     

航天器姿态机动的非线性鲁棒$H_infty$控制方法

针对存在动力学参数摄动及外部干扰的航天器姿态机动问题,给出一种新的非线性鲁棒$H_\infty$控制方法.通过适当处理和假设,将航天器姿态模型表示为具有凸多面体不确定性的多项式型状态空间方程,并基于Lyapunov稳定性和平方和(SOS)理论,建立非线性状态反馈$H_\infty$控制设计的可解性条件.最后,通过数值仿真实例验证了所提出方法的有效性.     

Nonlinear control for robust rejection of periodic disturbances

Consider a linear plant with a strictly proper rational transfer function and an input which is a known periodic waveform of unknown amplitude and phase. In the case when the periodic waveform has infinitely many non-zero harmonics, it is pointed out that for a wide class of linear, time-invariant (infinite-dimensional) controllers, it is not possible to robustly stabilize the plant and achieve asymptotic rejection of the disturbance. For a specific linear plant and a triangular wave disturbance, it is shown by construction that the problem is soluble with a nonlinear controller. Robustness is measured using the gap metric robust stability margin.     

Uncertainty modeling and robust minimax LQR control of multivariable nonlinear systems with application to hypersonic flight

For a class of multi‐input and multi‐output nonlinear uncertainty systems, a novel approach to design a nonlinear controller using minimax linear quadratic regulator (LQR) control is proposed. The proposed method combines a feedback linearization method with the robust minimax LQR approach in the presence of time‐varying uncertain parameters. The uncertainties, which are assumed to satisfy a certain integral quadratic constraint condition, do not necessarily satisfy a generalized matching condition. The procedure consists of feedback linearization of the nominal model and linearization of the remaining nonlinear uncertain terms with respect to each individual uncertainty at a local operating point. This two‐stage linearization process, followed by a robust minimax LQR control design, provides a robustly stable closed loop system. To demonstrate the effectiveness of the proposed approach, an application study is provided for a flight control problem of an air‐breathing hypersonic flight vehicle (AHFV), where the outputs to be controlled are the longitudinal velocity and altitude, and the control variables are the throttle setting and elevator deflection. The proposed method is used to derive a linearized uncertainty model for the longitudinal motion dynamics of the AHFV first, and then a robust minimax LQR controller is designed, which is based on this uncertainty model. The controller is synthesized considering seven uncertain aerodynamic and inertial parameters. The stability and performance of the synthesized controller is evaluated numerically via single scenario simulations for particular cruise conditions as well as a Monte‐Carlo type simulation based on numerous cases. It is observed that the control scheme proposed in this paper performs better, especially from the aspect of robustness to large ranges of uncertainties, than some controller design schemes previously published in the literature. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

带有干扰的线性时变系统的非线性鲁棒控制

研究了含有未知时变参数和有界干扰的单输入单输出线性时变系统的鲁棒控制问题.系统时变参数只要求光滑有界而不限制为慢时变或参数上界已知.利用时变的状态变换得到新的动态系统,基于Backstepping方法,设计出一种非线性鲁棒控制器.通过适当选择控制器参数,可以保证闭环系统是全局渐近稳定的.仿真例子表明了算法的有效性.     

小型无人直升机姿态非线性鲁棒控制设计

本文针对小型无人直升机的姿态控制问题,通过系统参数辨识,获得了较为准确的无人直升机姿态动力学模型.并根据无人直升机的动态特性,设计了基于神经网络前馈与滑模控制的非线性鲁棒姿态控制律,该控制律对直升机模型的先验知识要求较低.利用基于Lyapunov的分析方法证明,设计的控制律能够实现对无人直升机姿态角的半全局指数收敛镇定控制,并能确保闭环系统的稳定性.基于姿态飞行控制实验平台的实时飞行控制实验结果表明,提出的控制设计取得了很好的姿态控制效果,并对系统不确定性和外界风扰动具有较好的鲁棒性.     

Hybrid dynamical systems: robust control synthesis problems

The paper presents a new approach to robust control synthesis problems for hybrid dynamical systems. The hybrid system under consideration is a composite of a continuous plant and a discrete event controller. State and output feedback problems are considered. The main results are given in terms of the existence of suitable solutions to a dynamic programming equation and a Riccati differential equation of the H^∞ filtering type. These results show a connection between the theories of hybrid dynamical systems and robust and nonlinear control.     

时滞离散系统的非线性准滑模鲁棒控制

针对一类变时滞离散系统,基于一种新型的非线性时变准滑模面设计方案,研究了系统的鲁棒滑模控制问题.首先,给出了该种非线性切换函数的一般形式,该类准滑模面具有时变的特征,且能够动态地改善系统的运动品质.利用自由权矩阵与线性矩阵不等式技术,给出了该类时滞离散系统非线性准滑模面的设计方法,并得到了稳定的非线性准滑模面存在的充分条件;其次,基于一种改进的离散趋近律方法,设计了相应的准滑模控制器,以保证系统的状态在有限时间内到达准滑模,从而将此类非线性系统的滑模变结构控制的分析与综合问题推广到了时滞非线性系统.最后,仿真结果表明,在本文所设计的准滑模面与准滑模变结构控制器的作用下,系统的状态是稳定的,且具有响应速度快、超调量小、调节时间较小等优点,从而说明了本文所设计方法的有效性.     

Nonlinear system modeling and robust predictive control based on RBF-ARX model

An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems with unknown steady state. First, the nonlinear system is identified off-line by RBF-ARX model possessing linear ARX model structure and state-dependent Gaussian RBF neural network type coefficients. On the basis of the RBF-ARX model, a combination of a local linearization model and a polytopic uncertain linear parameter-varying (LPV) model are built to approximate the present and the future system's nonlinear behavior, respectively. Subsequently, based on the approximate models, a min–max robust MPC algorithm with input constraint is designed for the output-tracking control of the nonlinear system with unknown steady state. The closed-loop stability of the MPC strategy is guaranteed by the use of parameter-dependent Lyapunov function and the feasibility of the linear matrix inequalities (LMIs). Simulation study to a NO_x decomposition process illustrates the effectiveness of the modeling and robust MPC approaches proposed in this paper.     

Nonlinear robust constrained control of partially uncontrollable and open-loop unstable systems

In this paper we consider a bilinear matrix inequality (BMI) based method, which provides an ellipsoidal estimate of the region of attraction, for nonlinear constrained robust stabilization. Robustness against model uncertainty is handled. The method is based on an uncertain multi-model representation of the plant parameterized by affine local models and their respective supports in the state-space, and an associated piecewise affine state-feedback structure. We show that the method is applicable to uncertain constrained systems that are partially uncontrollable and open-loop unstable.     

Spherical approximations for minimax optimization problems

A general iterative method for the solution of convex minimax optimization problems is proposed. At each iteration, the functions are locally approximated by spheres and the resulting minimax problem is solved. We tested the algorithm on three different minimax location problems in the plane. Computational results with these problems are favorable.