Robust noise attenuation under stochastic noises and worst-case unmodelled dynamics

This paper investigates noise attenuation problems for systems with unmodelled dynamics and unknown noise characteristics. A unique methodology is introduced that employs signal estimation in one phase, followed by control design for noise rejection. The methodology enjoys certain advantages in its simple control design process, accommodation of unmodelled dynamics, and non-conservative noise rejection performance. Under mild information on unmodelled dynamics, we first derive robust performance bounds on noise attenuation with respect to unmodelled dynamics without noise estimation errors. Then more general results are presented for systems that are subject to both stochastic signal estimation errors and unmodelled dynamics. Examples are also presented to demonstrate our findings.     

具有不确定噪声的随机非线性系统的鲁棒自适应跟踪

研究了一类随机非线性系统的鲁棒自适应跟踪问题.文中利用随机控制Lyapunov设计方法,对于受方差不确定Wiener噪声干扰的参数严格反馈形式的系统,给出了参数自适应律和控制律,使得跟踪误差在4次均方意义下收敛到一个小范围内.     

Robust stability of stochastic genetic regulatory networks with time-varying delays: a delay fractioning approach

This paper is concerned with the problem of the globally asymptotically mean square stability for a class of delayed genetic regularity networks (GRNs) with both parameter uncertainties and stochastic disturbances, where the time delays are belong to given intervals and assumed to be time varying. Based on choosing an appropriate and novel Lyapunov functional, a “delay fractioning” approach that is different from the existing ones is introduced. By utilizing $It\hat{o}\hbox{'}s$ differential formula and using the linear matrix inequality (LMI) method, we derive a robust asymptotical stability criterion in mean square sense for uncertain GRNs with time-varying delays. All the stability conditions are given in terms of LMIs. One example and its simulation are provided to show the advantages of the obtained result.     

Robust filtering under stochastic parametric uncertainties

This paper is concerned with a polynomial approach to robust deconvolution filtering of linear discrete-time systems with random modeling uncertainties. The modeling errors appear in the coefficients of the numerators and denominators of both the input signal and system transfer function models in the form of random variables with zero means and known upper bounds of the covariances. The robust filtering problem is to find an estimator that minimizes the maximum mean square estimation error over the random parameter uncertainties and input and measurement noises. The key to our solution is to quantify the effect of the random parameter uncertainties by introducing two fictitious noises for which a simple way is given to calculate their covariances. The optimal robust estimator is then computed by solving one spectral factorization and one polynomial equation as in the standard optimal estimator design using a polynomial approach. An example of signal detection in mobile communication is given to illustrate the effectiveness of our approach.     

Robust H∞ sliding mode control scheme for uncertain fractional stochastic systems: Nonlinear analysis and design

This paper investigates the $H_{\infty }$ sliding mode control (SMC) design for fractional stochastic systems. We study a very general category of stochastic systems that are nonlinear and driven by fractional Brownian motion (fBm). A robust $H_{\infty }$ SMC scheme is presented for a fractional stochastic model with external disturbance, state- and disturbance-dependent noise, and uncertainties, which ensures that the closed-loop system is stochastically stable. We propose a novel sliding surface and then prove its reachability in the state space. Furthermore, the conditions for the stochastic stability of the sliding motion are derived via nonlinear Hamilton–Jacobi (HJ)-type inequalities. In addition, an $H_{\infty }$ SMC method is developed for a special class of fractional stochastic models, and two sets of linear matrix inequality (LMI) conditions are obtained, which are sufficient for stochastic stability. Eventually, the validity of the results is validated via a simulation example.     

Robust design based on constraint networks

This paper describes a robust design method using constraint networks. As opposed to the traditional statistical robust methodology, the proposed method gives a valid model to analyze parameter uncertainties so as to predict conflicts in concurrent design. The mathematical model, which reflects the requirements of robust design, is given in the paper. A general consistency algorithm is designed using interval arithmetic to refine the intervals. This paper also proves that the consistency algorithm is arc consistent if the constraint network is integrated. The constraint network uses the consistency algorithm to verify the design process early in the process and to assist the designers in determining design variables to reduce the multidisciplinary iterations in concurrent design. The quantitative effect of downstream constraints can be analyzed before determining design parameters and potential conflicts can be predicted. A layout design example shows the validity of the method.     

Robust stochastic stability of uncertain discrete-time impulsive Markovian jump delay systems with multiplicative noises

In this paper, the robust stochastic stability is investigated for a class of uncertain discrete-time impulsive Markovian jump delay systems with multiplicative noises. Using the method of stochastic Lyapunov functionals construction, it is shown that impulses can stabilise the original impulse-free unstable systems. Moreover, the stability property of the impulse-free systems can be retained in the cases of appropriately large impulsive time interval. Some numerical examples are exploited to demonstrate the effectiveness and the superiority of the proposed results.     

随机不确定系统的鲁棒H∞滤波

研究在同时具有参数和随机不确定的情况下的鲁棒H∞估计问题.假设系统的方程由Ito随机微分方程描述,不确定的参数是范数有界的,外部干扰是随机不确定的.通过解一个线性矩阵不等式,可以设计鲁棒H∞滤波器,最后给出的一个例子对理论分析进行了阐述.     

Robust energy-to-peak filter design for stochastic time-delay systems

This paper considers the robust energy-to-peak filtering problem for uncertain stochastic time-delay systems. The stochastic uncertainties appear in both the dynamic and the measurement equations and the state delay is assumed to be time-varying. Attention is focused on the design of full-order and reduced-order filters guaranteeing a prescribed energy-to-peak performance for the filtering error system. Sufficient conditions are formulated in terms of linear matrix inequalities (LMIs), and the corresponding filter design is cast into a convex optimization problem which can be efficiently handled by using standard numerical algorithms. In addition, the results obtained are further extended to more general cases where the system matrices also contain uncertain parameters. The most frequently used ways of dealing with parameter uncertainties, including polytopic and norm-bounded characterizations, have been taken into consideration, with convex optimization problems obtained for the design of desired robust energy-to-peak filters.     

Robust design of stochastic controllers for nonlinear systems

The previous results are generalized on the stabilizing property of a control scheme designed for a class of discrete-time nonlinear stochastic systems. First, the existing controller is made more robust with a prescribed degree of stability by proper modifications. Next, the inherent robustness is illustrated by a design utilizing erroneous noise characteristics. Reconsideration of the stability analysis used allows one to treat a larger class of nonlinear stochastic systems with more general structures     

Mean-square filter design for stochastic polynomial systems with Gaussian and Poisson noises

This paper addresses the mean-square finite-dimensional filtering problem for polynomial system states with both, Gaussian and Poisson, white noises over linear observations. A constructive procedure is established to design the mean-square filtering equations for system states described by polynomial equations of an arbitrary finite degree. An explicit closed form of the designed filter is obtained in case of a third-order polynomial system. The theoretical result is complemented with an illustrative example verifying performance of the designed filter.     

Robust active queue management design: A loop-shaping approach

In this paper, a novel systematic design procedure is presented for robust active queue management (AQM). The congestion control law is obtained through an interactive loop-shaping process that manipulates the system frequency response to meet robust stability and performance requirements in the presence of uncertain network conditions. A sufficient condition leading to a satisfactory level of robust performance against high-frequency parasitics that naturally affect the desired requirements is then derived. A feature of the technique is that the uncertain phase information and round-trip time-delay that are inherent to the system dynamics are fully addressed in the design equations, resulting in minimal conservatism and/or over-design. Simulation results using the ns2 simulator are provided to illustrate the effectiveness of the proposed method.     

Robust output feedback control of nonlinear stochastic systems using neural networks

We present an adaptive output feedback controller for a class of uncertain stochastic nonlinear systems. The plant dynamics is represented as a nominal linear system plus nonlinearities. In turn, these nonlinearities are decomposed into a part, obtained as the best approximation given by neural networks, plus a remaining part which is treated as uncertainties, modeling approximation errors, and neglected dynamics. The weights of the neural network are tuned adaptively by a Lyapunov design. The proposed controller is obtained through robust optimal design and combines together parameter projection, control saturation, and high-gain observers. High performances are obtained in terms of large errors tolerance as shown through simulations.     

Robust controller design for discrete unstable non-minimum-phase delayed stochastic processes

Control of unstable non-minimum-phase delayed stochastic processes is a challenging problem. In this work based on the Diophantine equation and using pole-placement technique, a discrete control scheme for such processes has been proposed. Robust stability of the suggested control structure has been shown. Advantages of the proposed scheme over the existing algorithms have been shown through computer simulations. It has been shown that performance of the proposed scheme for handling model mismatch and colored noise is superior to the previous work proposed in the literature.     

Robust and dynamic data aggregation in wireless sensor networks: A cross-layer approach

In-network data aggregation is an effective technique to reduce communication cost in wireless sensor networks. Recent studies have focused on two issues respectively: dynamic aggregation to handle event triggered irregular traffic and robust aggregation to handle packet losses. However, how to achieve both the objectives simultaneously, i.e. dynamic and robust aggregation is still not considered. In this paper, by making use of direct support from MAC layer, we propose a cross-layer approach to realize robust and dynamic data aggregation. A new MAC protocol, DA-MAC is delicately designed to serve such purpose. With channel contention information obtained from DA-MAC, a node can dynamically determine where and when to do aggregation. To cope with packet losses, a virtual overlay, Rings is adopted to forward one packet to multiple nodes. We have conducted numerical analysis to optimize the key parameters and implemented our design in TinyOS based sensor networks. Performance evaluation though simulations and experiments shows that our approach can handle both traffic dynamics and packet losses, with less cost than similar solutions.     

Robust delay-dependent exponential stability for uncertain stochastic neural networks with mixed delays

This paper is concerned with the robust delay-dependent exponential stability of uncertain stochastic neural networks (SNNs) with mixed delays. Based on a novel Lyapunov-Krasovskii functional method, some new delay-dependent stability conditions are presented in terms of linear matrix inequalities, which guarantee the uncertain stochastic neural networks with mixed delays to be robustly exponentially stable. Numerical examples are given to illustrate the effectiveness of our results.     

不确定随机离散分布时滞神经网络的鲁棒稳定性*

采用Its微分公式和不等式分析技巧,研究了一类不确定随机离散分布时滞神经网络的鲁棒稳定性问题。该模型同时考虑了神经网络模型的两种扰动因素,即随机扰动与不确定性扰动。通过构造适当的Lyapunov泛函,以线性矩阵不等式形式给出了系统在均方根意义下的全局鲁棒稳定性判据,能够利用LMI工具箱很容易地进行检验。此外,仿真结果进一步证明了结论的有效性。     

Passive fuzzy controller design via observer feedback for stochastic Takagi-Sugeno fuzzy models with multiplicative noises

This paper investigates the fuzzy control problem of a class of nonlinear continuous-time stochastic systems with achieving the passivity performance. A model-based observer feedback fuzzy control utilizing the concept of so-called parallel distributed compensation (PDC) is employed to stabilize the class of nonlinear stochastic systems that are represented by the Takagi-Sugeno (T-S) fuzzy models. Based on the Lyapunov criteria, the Linear Matrix Inequality (LMI) technique is used to synthesize the observer feedback fuzzy controller design such that the closed-loop system satisfies stability and passivity constraints, simultaneously. Finally, a numerical example is given to demonstrate the applicability and effectiveness of the proposed design method.     

Robust aerodynamic shape design based on an adaptive stochastic optimization framework

Optimization techniques combined with uncertainty quantification are computationally expensive for robust aerodynamic optimization due to expensive CFD costs. Surrogate model technology can be used to improve the efficiency of robust optimization. In this paper, non-intrusive polynomial chaos method and Kriging model are used to construct a surrogate model that associate stochastic aerodynamic statistics with airfoil shapes. Then, global search algorithm is used to optimize the model to obtain optimal airfoil fast. However, optimization results always depend on the approximation accuracy of the surrogate model. Actually, it is difficult to achieve a high accuracy of the model in the whole design space. Therefore, we introduce the idea of adaptive strategy to robust aerodynamic optimization and propose an adaptive stochastic optimization framework. The surrogate model is updated adaptively by increasing training airfoils according to historical optimization results to guarantee the accuracy near the optimal design point, which can greatly reduce the number of training airfoils. The proposed method is applied to a robust aerodynamic shape optimization for drag minimization considering uncertainty of Mach number in transonic region. It can be concluded that the proposed method can obtain better optimal results more efficiently than the traditional robust optimization method and global surrogate model method.     

Robust filtering under nonrandom disturbances: The invariant ellipsoid approach

We present a simple and universal observer-based approach to solving the problem of robust filtering of unknown-but-bounded exogenous disturbances. The heart of this approach is the method of invariant ellipsoids. Application of this technique allows for a reformulation of the original problem in terms of linear matrix inequalities with reduction to semidefinite programming and one-dimensional optimization, which are easy to solve numerically. Continuous-time and discrete-time cases are studied in equal detail. The efficacy of the approach is demonstrated via the double pendulum example.