Robust H∞ Filter Design for 2D Discrete Systems in Roesser Model

This paper investigates the robust H∞ filtering problem for uncertain two-dimensional (2D) systems described by the Roesser model. The parameter uncertainties considered in this paper are assumed to be of polytopie type. A new structured polynomi-ally parameter-dependent method is utilized, which is based on homogeneous polynomially parameter-dependent matrices of arbitrary degree. The proposed method includes results in the quadratic framework and the linearly parameter-dependent framework as special cases for zeroth degree and first degree, respectively. A numerical example illustrates the feasibility and advantage of the proposed filter design methods.     

Gain-scheduled L-one control for linear parameter-varying systems with parameter-dependent delays

This paper deals with the problem of gain-scheduled L-one control for linear parameter-varying (LPV) systems with parameter-dependent delays. The attention is focused on the design of a gain-scheduled L-one controller that guarantees being an asymptotically stable closed-loop system and satisfying peak-to-peak performance constraints for LPV systems with respect to all amplitude-bounded input signals. In particular, concentrating on the delay-dependent case, we utilize parameter-dependent Lyapunov functions (PDLF) to establish peak-to-peak performance criteria for the first time where there exists a coupling between a Lyapunov function matrix and system matrices. By introducing a slack matrix, the decoupling for the parameter-dependent time-delay LPV system is realized. In this way, the sufficient conditions for the existence of a gain-scheduled L-one controller are proposed in terms of the Lyapunov stability theory and the linear matrix inequality (LMI) method. Based on approximate basis function and the gridding technique, the corresponding controller design is cast into a feasible solution problem of the finite parameter linear matrix inequalities. A numerical example is given to show the effectiveness of the proposed approach.     

Robust H_∞ control for discrete-time polytopic uncertain systems with linear fractional vertices

The robust H∞ control problem for discrete-time uncertain systems is investigated in this paper. The uncertain systems are modelled as a polytopic type with linear fractional uncertainty in the vertices. A new linear matrix inequality (LMI) characterization of the H∞ performance for discrete systems is given by introducing a matrix slack variable which decouples the matrix of a Lyapunov function candidate and the parametric matrices of the system. This feature enables one to derive sufficient conditions for discrete uncertain systems by using parameter-dependent Lyapunov functions with less conservativeness. Based on the result, H∞ performance analysis and controller design are carried out. A numerical example is included to demonstrate the effectiveness of the proposed results.     

Sampled-data extended state observer for uncertain nonlinear systems

In this paper, we present a sampled-data nonlinear extended state observer (NLESO) design method for a class of nonlinear systems with uncertainties and discrete time output measurement. To accommodate the inter-sample dynamics, an inter-sample output predictor is employed in the structure of the NLESO to estimate the system output in the sampling intervals, where the prediction is used in the proposed observer instead of the system output. The exponential convergence of the sampled-data NLESO is also discussed and a sufficient condition is given by the Lyapunov method. A numerical example is provided to illustrate the performance of the proposed observer.     

Robust H-infinity filtering for uncertain discrete-time systems using parameter-dependent Lyapunov functions

This paper deals with H-infinity filtering of discrete-time systems with polytopic uncertainties. The un- certain parameters are supposed to reside in a polytope. By using the parameter-dependent Lyapunov function approach and introducing some slack matrix variables, a new sufficient condition for the H-infinity filter design is presented in terms of solutions to a set of linear matrix inequalities (LMIs). In contrast to the existing results for H-infinity filter design, the main advantage of the proposed design method is the reduced conservativeness. An example is provided to demonstrate the effectiveness of the proposed method.     

A simple approach for stability margin of discrete systems

In this paper, a technique is presented to determine the stability margin of the discrete systems using recursive algorithm for power of companion matrix and Gerschgorin Theorem and hence sufficient condition of stability is obtained. The method is illustrated with an example and it is compared with other methods proposed in the literature. The results have applications in the filter design.     

一类离散非线性不确定时滞系统的鲁棒滑模滤波

This paper is concerned with the problem of robust sliding-mode filtering for a class of uncertain nonlinear discrete-time systems with time-delays. The nonlinearities are assumed to satisfy global Lipschitz conditions and parameter uncertainties are supposed to reside in a polytope. The resulting filter is of the Luenberger type with the discontinuous form. A sufficient condition with delay-dependency is proposed for existence of such a filter. And the desired filter can be found by solving a set of matrix inequalities. The resulting filter adapts for the systems whose noise input is real functional bounded and not be required to be energy bounded. A numerical example is given to illustrate the effectiveness of the proposed design method.     

一类欠驱动机械系统的动态及其稳定控制

Abstract The control of underactuated mechanical systems is very complex for the loss of its control inputs. The model of underactuated mechanical systems in a potential field is built with Lagrangian method and its structural properties are analyzed in detail. A stable control approach is proposed for the class of underactuated mechanical systems. This approach is applied to an unde ractuated double-pendulum-type overhead crane and the simulation results illustrate the correctness of dynamics analysis and validity of the proposed control algorithm.     

Robust H-infinity filtering for time-delay systems with missing measurements： a parameter-dependent approach

Robust H-infinity filtering for a class of uncertain discrete-time linear systems with time delays and missing measurements is studied in this paper. The uncertain parameters are supposed to reside in a convex polytope and the missing measurements are described by a binary switching sequence satisfying a Bernoulli distribution. Our attention is focused on the analysis and design of robust H-infinity filters such that, for all admissible parameter uncertainties and all possible missing measurements, the filtering error system is exponentially mean-square stable with a prescribed H-infinity disturbance attenuation level. A parameter-dependent approach is proposed to derive a less conservative result. Sufficient conditions are established for the existence of the desired filter in terms of certain linear matrix inequalities （LMIs）. When these LMIs are feasible, an explicit expression of the desired filter is also provided. Finally, a numerical example is presented to illustrate the effectiveness and applicability of the proposed method.     

New results on discrete-time delay systems identification

A new approach for simultaneous online identification of unknown time delay and dynamic parameters of discrete-time delay systems is proposed in this paper.The proposed algorithm involves constructing a new generalized regression vector and defining the time delay and the rational dynamic parameters in the same vector.The gradient algorithm is used to deal with the identification problem.The effectiveness of this method is illustrated through simulation.     

Further control synthesis for time-delay systems with actuator saturation

This paper is concerned with the control synthesis problem for systems with time-varying delay and actuator saturation. A new controller design method is proposed in which auxiliary feedback matrix method is adopted to handle the saturation term in the system. The improvement of the proposed method lies in the application of delay partitioning idea to further enlarge the estimated domain of attraction. All the results are given in terms of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness and superiority of our obtained results.     

具有干扰输入的不确定奇异系统的有限时间控制器设计

The concept of finite-time stability for linear singular system isinduced in this paper. Finite-time control problem is considered for linear singular systems with time-varying parametricuncertainties and exogenous disturbances. The disturbance satisfies a dynamical system with parametric uncertainties. Asufficient condition is presented for robust finite-ime stabilization via state feedback. The condition is translated to a feasibility problem involving restricted linear matrix inequalities (LMIs). A detailed solving method is proposed for the restricted linear matrix inequalities. Finally, an example is given to show the validity of the results.     

Adaptive output regulation for cyber-physical systems under time-delay attacks

In this paper, we present an output regulation method for unknown cyber-physical systems (CPSs) under time-delay attacks in both the sensor-to-controller (S-C) channel and the controller-to-actuator (C-A) channel. The proposed approach is designed using control inputs and tracking errors which are accessible data. Reinforcement learning is leveraged to update the control gains in real time using policy or value iterations. A thorough stability analysis is conducted and it is found that the proposed controller can sustain the convergence and asymptotic stability even when two channels are attacked. Finally, comparison results with a simulated CPS verify the effectiveness of the proposed output regulation method.     

An evaluation framework for energy aware buildings using statistical model checking

Cyber-physical systems are to be found in numerous applications throughout society.The principal barrier to develop trustworthy cyber-physical systems is the lack of expressive modelling and specification formalisms supported by efficient tools and methodologies.To overcome this barrier,we extend in this paper the modelling formalism of the tool UPPAAL-SMC to stochastic hybrid automata,thus providing the expressive power required for modelling complex cyber-physical systems.The application of Statistical Model Checking provides a highly scalable technique for analyzing performance properties of this formalisms.A particular kind of cyber-physical systems are Smart Grids which together with Intelligent,Energy Aware Buildings will play a major role in achieving an energy efficient society of the future.In this paper we present a framework in UPPAAL-SMC for energy aware buildings allowing to evaluate the performance of proposed control strategies in terms of their induced comfort and energy profiles under varying environmental settings(e.g.weather,user behavior etc.).To demonstrate the intended use and usefulness of our framework,we present an application to the Hybrid Systems Verification Benchmark.     

Nonlinear robust H-infinity filtering for a class of uncertain systems via convex optimization

A new approach for robust H-infinity filtering for a class of Lipschitz nonlinear systems with time-varying uncertainties both in the linear and nonlinear parts of the system is proposed in an LMI framework. The admissible Lipschitz constant of the system and the disturbance attenuation level are maximized simultaneously through convex multi-objective optimization. The resulting H-infinity filter guarantees asymptotic stability of the estimation error dynamics with exponential convergence and is robust against nonlinear additive uncertainty and time-varying parametric uncertainties. Explicit bounds on the nonlinear uncertainty are derived based on norm-wise and element-wise robustness analysis.     

Modeling and control for wireless networked control system

In this paper, the stabilization problem is considered for the class of wireless networked control systems (WNCS). An indicator is introduced in the WNCS model. The packet drop sequences in the indicator are represented as states of a Markov chain. A new discrete Markov switching system model integrating 802.11 protocol and new scheduling approach for wireless networks with control systems are constructed. The variable controller can be obtained easily by solving the linear matrix inequality (LMI) with the use of the Matlab toolbox. Both the known and unknown dropout probabilities are considered. Finally, a simulation is given to show the feasibility of the proposed method.     

Traffic chaos and its p rediction based on a nonlinear car-f ollowing model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car’s movement in a single lane. Traffic chaos is a promising field ,and chaos theory has been applied to identify and predict its chaotic movement . A simulated traffic flow is generated using a car-following model (GM model) , and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN (radial basis function neural network) is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent . The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short- time traffic flow series.     

Frequency-domain generelaized singular peruturbation method for relative error model order reduction

A new mixed method for relative error model order reduction is proposed. In the proposed method the frequency domain balanced stochastic truncation method is improved by applying the generalized singular perturbation method to the frequency domain balanced system in the reduction procedure. The frequency domain balanced stochastic truncation method, which was proposed in [15] and [17] by the author, is based on two recently developed methods, namely frequency domain balanced truncation within a desired frequency bound and inner-outer factorization techniques. The proposed method in ttiis paper is a carry over of the frequency-domain balanced stochastic truncation and is of interest for practical model order reduction because in this context it shows to keep the accuracy of the approximation as high as possible without sacrificing the computational efficiency and important system properties. It is shown that some important properties of the frequency domain stochastic balanced reduction technique are extended to the proposed reduction method by using the concept and properties of the reciprocal systems. Numerical results show the accuracy, simplicity and flexibility enhancement of the method.     

基于语义单元的足球视频事件检测

A semantic unit based event detection scheme in soccer videos is proposed in this paper. The scheme can be characterized as a three-layer framework. At the lowest layer, low-level features including color, texture, edge, shape, and motion are extracted. High-level semantic events are defined at the highest layer. In order to connect low-level features and high-level semantics, we design and define some semantic units at the intermediate layer. A semantic unit is composed of a sequence of consecutives frames with the same cue that is deduced from low-level features. Based on semantic units, a Bayesian network is used to reason the probabilities of events. The experiments for shoot and card event detection in soccer videos show that the proposed method has an encouraging performance.     

Traffm chaos and its prediction based on a nonlinear car-following model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car＇s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been applied to identify and predict its chaotic movement. A simulated traffic flow is generated using a car-following model（ GM model）, and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN （radial basis function neural network） is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series