Jianchun Xing

Finite-Time Asynchronously Switched Control of Switched Systems with Sampled-Data Feedback

Modern control systems usually employ digital technology for controller implementation. The dynamics of the systems are naturally continuous, while control inputs are usually discrete when digital control is utilized. This paper deals with the finite-time stabilization problem of switched systems with sampled-data state feedback under asynchronous switching. The asynchronous switching idea originates from the fact that switching instants of the controllers lag behind or exceed those of subsystems. The attention is focused on designing an asynchronously switched sampled-data controller that guarantees the finite-time stability of the dynamic system. Especially, we consider the case that the switching time and sampling time are not uniform when the system is working. On the basis of finite-time stability theory and multiply Lyapunov functions approach, a finite-time stability condition related to dwell time and sampling period is established. Then, an asynchronously switched sampled-data controller is designed, and the corresponding switching law is also derived to guarantee the considered system to be finite-time stable. Two numerical examples are provided to show the effectiveness of the developed results.

Finite-time stabilization for discrete-time switched stochastic linear systems under asynchronous switching

This paper deals with the problem of state feedback stabilization with finite-time stochastic stability for a class of discrete-time switched stochastic linear systems under asynchronous switching. The attention is focused on designing the feedback controller that guarantees the finite-time stochastic stability of the dynamic system. The finite-time stochastic stability definition of discrete-time switched stochastic systems is introduced. The asynchronous switching idea originates from the fact that switching instants of the controllers lag behind or exceed those of subsystems. On the basis of the average dwell time method and multiple Lyapunov functions approach, a finite-time stochastic stability condition is established. Then, an asynchronously switched controller is designed and the corresponding switching law is derived to guarantee the considered system be finite-time stochastically stable. Two numerical examples are provided to show the effectiveness of the developed results.

A New Wavelet Thresholding Function Based on Hyperbolic Tangent Function

Thresholding function is an important part of the wavelet threshold denoising method, which can influence the signal denoising effect significantly. However, some defects are present in the existing methods, such as function discontinuity, fixed bias, and parameters determined by trial and error. In order to solve these problems, a new wavelet thresholding function based on hyperbolic tangent function is proposed in this paper. Firstly, the basic properties of hyperbolic tangent function are analyzed. Then, a new thresholding function with a shape parameter is presented based on hyperbolic tangent function. The continuity, monotonicity, and high-order differentiability of the new function are theoretically proven. Finally, in order to determine the final form of the new function, a shape parameter optimization strategy based on artificial fish swarm algorithm is given in this paper. Mean square error is adopted to construct the objective function, and the optimal shape parameter is achieved by iterative search. At the end of the paper, a simulation experiment is provided to verify the effectiveness of the new function. In the experiment, two benchmark signals are used as test signals. Simulation results show that the proposed function can achieve better denoising effect than the classical hard and soft thresholding functions under different signal types and noise intensities.

A Combined Optimal Sensor Placement Strategy for the Structural Health Monitoring of Bridge Structures

Optimal sensor placement is an important part in the structural health monitoring of bridge structures. However, some defects are present in the existing methods, such as the focus on a single optimal index, the selection of modal order and sensor number based on experience, and the long computation time. A hybrid optimization strategy named MSE-AGA is proposed in this study to address these problems. The approach firstly selects modal order using modal participation factor. Then, the modal strain energy method is adopted to conduct the initial sensor placement. Finally, the adaptive genetic algorithm (AGA) is utilized to determine the optimal number and locations of the sensors, which uses the root mean square of off-diagonal elements in the modal assurance criterion matrix as the fitness function. A case study of sensor placement on a numerically simulated bridge structure is provided to verify the effectiveness of the MSE-AGA strategy, and the AGA method without initial placement is used as a contrast experiment. A comparison of these strategies shows that the optimal results obtained by the MSE-AGA method have a high modal strain energy index, a short computation time, and small off-diagonal elements in the modal assurance criterion matrix.

Fuzzy Control-Based Software Self-Adaptation: A Case Study in Mission Critical Systems

Self-adaptation ability is particularly desirable for mission critical software (MCS). This paper proposes a fuzzy control-based approach to provide a systematic, engineering, and intuitive way for programmers to achieve software self adaptation. This approach uses fuzzy logic to handle uncertainty in software, uses natural-language style to describe self-adaptation logic, and makes control visible in software. These greatly shorten the knowledge and semantics gap between control engineering and software engineering, and reduce the realization difficulty of software self-adaptation. We illustrate our approach through the development of an adaptive MCS application in process control systems.

Fuzzy Self-Adaptation of Mission-Critical Software Under Uncertainty

Mission-critical software (MCS) must provide continuous, online services to ensure the successful accomplishment of critical missions. Self-adaptation is particularly desirable for assuring the quality of service (QoS) and availability of MCS under uncertainty. Few techniques have insofar addressed the issue of MCS self-adaptation, and most existing approaches to software self-adaptation fail to take into account uncertainty in the self-adaptation loop. To tackle this problem, we propose a fuzzy control based approach, i.e., Software Fuzzy Self-Adaptation (SFSA), with a view to deal with the challenge of MCS self-adaptation under uncertainty. First, we present the SFSA conceptual framework, consisting of sensing, deciding and acting stages, and establish the formal model of SFSA to lay a rigorous and mathematical foundation of our approach. Second, we develop a novel SFSA implementation technology as well as its supporting tool, i.e., the SFSA toolkit, to automate the realization process of SFSA. Finally, we demonstrate the effectiveness of our approach through the development of an adaptive MCS application in process control systems. Validation experiments show that the fuzzy control based approach proposed in this work is effective and with low overheads.

Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm

Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO) algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

FAME: A UML-based framework for modeling fuzzy self-adaptive software

Abstract Context: Software Fuzzy Self-Adaptation (SFSA) is a fuzzy control-based software self-adaptation paradigm proposed to deal with the fuzzy uncertainty existing in self-adaptive software. However, as many software engineers lack fuzzy control knowledge, it is difficult for them to design and model this kind of fuzzy self-adaptive software (F-SAS). Therefore, efficient and effective modeling technologies and tools are needed for the SFSA framework. Objective: This paper aims to identify modeling requirements of F-SAS and to provide a modeling framework to specify, design and model F-SAS systems. Such a framework can simplify modeling process of F-SAS and improve the accessibility of software engineers to the SFSA paradigm. Method: This study proposes a modeling framework called Fuzzy self-Adaptation ModEling (FAME). By extending UML, FAME creates three types of modeling views. An analysis view called Fuzzy Case Diagram is created to specify the fuzzy self-adaptation goal and the realization processes of this goal. A structure view called Fuzzy Class Diagram is created to describe the fuzzy concepts and structural characteristics of F-SAS. A behavior view called Fuzzy Sequence Diagram is created to depict the dynamic behaviors of the F-SAS systems. The framework is implemented as a plug-in of Enterprise Architect. Results: We demonstrate the effectiveness and efficiency of the proposed approach by carrying out a subject-based empirical evaluation. The results show that FAME framework can improve modeling quality of F-SAS systems by 44.38% and shorten modeling time of F-SAS systems by 38.41% in comparison with traditional UML. Thus, FAME can considerably ease the modeling process of F-SAS systems. Conclusion: FAME framework incorporates the SFSA concepts into standard UML. Therefore, it provides a direct support to model SFSA characteristics and improves the accessibility of software engineers to the SFSA paradigm. Furthermore, it behaves a good example and provides good references for modeling domain-specific software systems.

Robust control for a class of uncertain switched time delay systems using delta operator

This paper considers the problem of stability and robust stabilization for a class of uncertain time delay switched systems using the delta operator. Based on multiple Lyapunov-Krasovskii function in delta domain, a sufficient condition for the existence of stability of the delta operator time delay switched system is presented, and a new sampling period and delay dependent design approach to robust state feedback controller is addressed. The proposed controller can robustly stabilize the uncertain delta operator time delay switched system for all admissible parameter perturbations. The solution to the controller is formulated in the form of a set of linear matrix inequalities. A numerical example is provided to illustrate the effectiveness of the developed method.

Finite-time stability and stabilization of switched nonlinear systems with asynchronous switching

This paper is concerned with the problem of finite-time stability and stabilization for switched nonlinear systems with asynchronous switching. Firstly, when not all subsystems are finite-time stable (FTS), we propose a finite-time stability criteria to show that if the constraint condition between the settling time and the average dwell time is satisfied, finite-time stability of the system is guaranteed. Then we extend the result to the case which Lipschitzian perturbations exist in the subsystems. When asynchronous switching is considered and not all closed-loop subsystems are finite-time stabilizable, we recur to the generalized inverse of matrices to design a state feedback controller to stabilize the original system. Finally, an example of two container liquid-level system is provided to illustrate the effectiveness of developed result.