Showing papers by "Huijun Gao published in 2013"

Network-Induced Constraints in Networked Control Systems—A Survey

Abstract: Networked control systems (NCSs) have, in recent years, brought many innovative impacts to control systems. However, great challenges are also met due to the network-induced imperfections. Such network-induced imperfections are handled as various constraints, which should appropriately be considered in the analysis and design of NCSs. In this paper, the main methodologies suggested in the literature to cope with typical network-induced constraints, namely time delays, packet losses and disorder, time-varying transmission intervals, competition of multiple nodes accessing networks, and data quantization are surveyed; the constraints suggested in the literature on the first two types of constraints are updated in different categorizing ways; and those on the latter three types of constraints are extended.

Distributed H ∞ Filtering for a Class of Markovian Jump Nonlinear Time-Delay Systems Over Lossy Sensor Networks

Abstract: This paper is concerned with the distributed H∞ filtering problem for a class of discrete-time Markovian jump nonlinear time-delay systems with deficient statistics of mode transitions. The system measurements are collected through a lossy sensor network subject to randomly occurring quantization errors and randomly occurring packet dropouts. The description of deficient statistics of mode transitions that account for known, unknown, and uncertain transition probabilities is comprehensive. A distributed filter design scheme is outlined by explicitly characterizing the filter gains in terms of some matrix inequalities. Simulation results demonstrate the effectiveness of the proposed filtering scheme.

Dissipativity-Based Sliding Mode Control of Switched Stochastic Systems

Abstract: This technical brief is concerned with dissipativity analysis and dissipativity-based sliding mode control (SMC) of continuous-time switched stochastic systems. Firstly, a sufficient condition is proposed to guarantee the mean-square exponential stability and strict dissipativity for the switched stochastic system. Then, an integral-type sliding surface function is designed for establishing a sliding mode dynamics, which can be formulated by a switched stochastic system with an external disturbance/uncertainty. Dissipativity analysis and synthesis are both investigated for the sliding mode dynamics, and consequently sufficient conditions are derived, which pave the way for solving the dissipativity analysis and control problems. Moreover, a SMC law is synthesized to drive the system trajectories onto the predefined sliding surface in a finite time. Finally, the efficiency of the theoretical findings is demonstrated by an illustrative example.

Saturated Adaptive Robust Control for Active Suspension Systems

Abstract: This paper investigates the problem of vibration control in vehicle active suspension systems, whose aim is to stabilize the attitude of the vehicle and improve ride comfort. In response to uncertainties in systems and the possible actuator saturation, a saturated adaptive robust control (ARC) strategy is proposed. Specifically, an antiwindup block is added to adjust the control strategy in a manner conducive to stability and performance preservation in the presence of saturation. Furthermore, the proposed saturated ARC approach is applied to the half-car active suspension systems, where nonlinear springs and piecewise linear dampers are adopted. Finally, the typical bump road inputs are considered as the road disturbances in order to illustrate the effectiveness of the proposed control law.

Adaptive Backstepping Control for Active Suspension Systems With Hard Constraints

Abstract: This paper proposes an adaptive backstepping control strategy for vehicle active suspensions with hard constraints. An adaptive backstepping controller is designed to stabilize the attitude of vehicle and meanwhile improve ride comfort in the presence of parameter uncertainties, where suspension spaces, dynamic tire loads, and actuator saturations are considered as time-domain constraints. In addition to spring nonlinearity, the piecewise linear behavior of the damper, which has different damping rates for compression and extension movements, is taken into consideration to form the basis of accurate control. Furthermore, a reference trajectory is planned to keep the vertical and pitch motions of car body to stabilize in predetermined time, which helps adjust accelerations accordingly to high or low levels for improving ride comfort. Finally, a design example is shown to illustrate the effectiveness of the proposed control law.

Quantised recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements

Abstract: This article is concerned with the recursive finite-horizon filtering problem for a class of nonlinear time-varying systems subject to multiplicative noises, missing measurements and quantisation effects. The missing measurements are modelled by a series of mutually independent random variables obeying Bernoulli distributions with possibly different occurrence probabilities. The quantisation phenomenon is described by using the logarithmic function and the multiplicative noises are considered to account for the stochastic disturbances on the system states. Attention is focused on the design of a recursive filter such that, for all multiplicative noises, missing measurements as well as quantisation effects, an upper bound for the filtering error covariance is guaranteed and such an upper bound is subsequently minimised by properly designing the filter parameters at each sampling instant. The desired filter parameters are obtained by solving two Riccati-like difference equations that are of a recursive form...

Distributed Synchronization in Networks of Agent Systems With Nonlinearities and Random Switchings

Abstract: In this paper, the distributed synchronization problem of networks of agent systems with controllers and nonlinearities subject to Bernoulli switchings is investigated. Controllers and adaptive updating laws injected in each vertex of networks depend on the state information of its neighborhood. Three sets of Bernoulli stochastic variables are introduced to describe the occurrence probabilities of distributed adaptive controllers, updating laws and nonlinearities, respectively. By the Lyapunov functions method, we show that the distributed synchronization of networks composed of agent systems with multiple randomly occurring nonlinearities, multiple randomly occurring controllers, and multiple randomly occurring updating laws can be achieved in mean square under certain criteria. The conditions derived in this paper can be solved by semi-definite programming. Moreover, by mathematical analysis, we find that the coupling strength, the probabilities of the Bernoulli stochastic variables, and the form of nonlinearities have great impacts on the convergence speed and the terminal control strength. The synchronization criteria and the observed phenomena are demonstrated by several numerical simulation examples. In addition, the advantage of distributed adaptive controllers over conventional adaptive controllers is illustrated.

Static-Output-Feedback ${\mathscr H}_{\bm \infty }$ Control of Continuous-Time T - S Fuzzy Affine Systems Via Piecewise Lyapunov Functions

Abstract: This paper investigates the problem of robust H∞ output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints. The objective is to design a suitable constrained piecewise affine static output feedback controller, guaranteeing the asymptotic stability of the resulting closed-loop fuzzy control system with a prescribed H∞ disturbance attenuation level. Based on a smooth piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, some new results are developed for static output feedback controller synthesis of the underlying continuous-time T-S fuzzy affine systems. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, three examples are provided to illustrate the effectiveness of the proposed methods.

Adaptive Robust Vibration Control of Full-Car Active Suspensions With Electrohydraulic Actuators

Abstract: This paper investigates the problem of vibration suppression in vehicular active suspension systems, whose aim is to stabilize the attitude of the vehicle and improve the riding comfort. A full-car model is adopted, and electrohydraulic actuators with highly nonlinear characteristics are considered to form the basis of accurate control. In this paper, the H∞ performance is introduced to realize the disturbance suppression by selecting the actuator forces as virtual inputs, and an adaptive robust control technology is further used to design controllers which help real force inputs track virtual ones. The resulting controllers are robust against both actuator parametric uncertainties and uncertain actuator nonlinearities. The stability analysis for the closed-loop system is given within the Lyapunov framework. Finally, a numerical example is given to illustrate the effectiveness of the proposed control law, where different road conditions are considered in order to reveal the closed-loop system performance in detail.

Finite-Horizon $H_{\infty} $ Filtering With Missing Measurements and Quantization Effects

Abstract: In this paper, a new H∞ filtering approach is developed for a class of discrete time-varying systems subject to missing measurements and quantization effects. The missing measurements are modeled via a diagonal matrix consisting of a series of mutually independent random variables satisfying certain probabilistic distributions on the interval [0,1] . The measured output is quantized by a logarithmic quantizer. Attention is focused on the design of a stochastic H∞ filter such that the H∞ estimation performance is guaranteed over a given finite-horizon in the simultaneous presence of probabilistic missing measurements, quantization effects as well as external non-Gaussian disturbances. A necessary and sufficient condition is first established for the existence of the desired time-varying filters in virtue of the solvability of certain coupled recursive Riccati difference equations (RDEs). Owing to its recursive nature, the proposed RDE approach is shown to be suitable for online application without the need of increasing the problem size. The simulation experiment is carried out for the mobile robot localization problem with non-Gaussian disturbances, missing measurements and quantization effects. The effectiveness of the proposed method is demonstrated in the numerical example.

Nonsynchronized Robust Filtering Design for Continuous-Time T–S Fuzzy Affine Dynamic Systems Based on Piecewise Lyapunov Functions

Abstract: This paper investigates the problem of robust H∞ state estimation for a class of continuous-time nonlinear systems via Takagi-Sugeno (T-S) fuzzy affine dynamic models. Attention is focused on the analysis and design of an admissible full-order filter such that the resulting filtering error system is asymptotically stable with a guaranteed H∞ disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the filter implementation with state-space partition may not be synchronous with the state trajectories of the plant. Based on piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality linearization techniques, some new results are presented for the filtering design of the underlying continuous-time T-S fuzzy affine systems. Illustrative examples are given to validate the effectiveness and application of the proposed design approaches.

Robust Stability and Stabilization of Uncertain T–S Fuzzy Systems With Time-Varying Delay: An Input–Output Approach

Abstract: An input-output approach to the stability and stabilization of uncertain Takagi-Sugeno (T-S) fuzzy systems with time-varying delay is proposed in this paper. The time-varying parameter uncertainties are assumed to be norm-bounded, and the delay is intervally time varying. A novel method is employed to approximate the time-varying delay, based on which the considered system is transformed into a feedback interconnection form. The new formulation of the system is comprised of a forward subsystem with constant time delay and a feedback subsystem embedding the uncertainties. By applying the scaled small-gain theorem to the converted system, less conservative stability and stabilization criteria are obtained. Moreover, the applicability of the proposed approach to the robust case is simpler since both delay and parameter uncertainties are processed in a unified framework. Numerical experiments are performed to illustrate the advantage of the proposed techniques.

Gain-Constrained Recursive Filtering With Stochastic Nonlinearities and Probabilistic Sensor Delays

Abstract: This paper is concerned with the gain-constrained recursive filtering problem for a class of time-varying nonlinear stochastic systems with probabilistic sensor delays and correlated noises. The stochastic nonlinearities are described by statistical means that cover the multiplicative stochastic disturbances as a special case. The phenomenon of probabilistic sensor delays is modeled by introducing a diagonal matrix composed of Bernoulli distributed random variables taking values of 1 or 0, which means that the sensors may experience randomly occurring delays with individual delay characteristics. The process noise is finite-step autocorrelated. The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant, where the filter gain satisfies a certain equality constraint. A new recursive filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the pre-specified filter gain constraint. A simulation example is provided to illustrate the effectiveness of the proposed filter design approach.

$H_{\infty}$ Consensus and Synchronization of Nonlinear Systems Based on A Novel Fuzzy Model

Abstract: This paper investigates the H∞ consensus control problem of nonlinear multiagent systems under an arbitrary topological structure. A novel Takagi-Sukeno (T-S) fuzzy modeling method is proposed to describe the problem of nonlinear follower agents approaching a time-varying leader, i.e., the error dynamics between the follower agents and the leader, whose dynamics is evolving according to an isolated unforced nonlinear agent model, is described as a set of T-S fuzzy models. Based on the model, a leader-following consensus algorithm is designed so that, under an arbitrary network topology, all the follower agents reach consensus with the leader subject to external disturbances, preserving a guaranteed H∞ performance level. In addition, we obtain a sufficient condition for choosing the pinned nodes to make the entire multiagent network reach consensus. Moreover, the fuzzy modeling method is extended to solve the synchronization problem of nonlinear systems, and a fuzzy H∞ controller is designed so that two nonlinear systems reach synchronization with a prescribed H∞ performance level. The controller design procedure is greatly simplified by utilization of the proposed fuzzy modeling method. Finally, numerical simulations on chaotic systems and arbitrary nonlinear functions are provided to illustrate the effectiveness of the obtained theoretical results.

Asymptotic stability and stabilisation of uncertain delta operator systems with time-varying delays

Abstract: This study focuses on the asymptotic stability and stabilisation of uncertain linear systems with time-varying delays via delta operator approach. By employing a new model formulation, the time-delayed delta operator system is transformed into an interconnected system for which the uncertainties can become easy to deal with. Based on a two-term approximation of delayed state and scaled small gain theorem, new delay-dependent sufficient conditions of robust asymptotic stability and state-feedback stabilisation of an uncertain delta operator time-delayed system are established by using a novel Lyapunov-Krasovskii functional. The criteria obtained unify some previously suggested relevant methods seen in literature for achieving asymptotic stability and stabilisation of both continuous and discrete systems into the delta operator framework. Numerical examples presented explicitly demonstrate the advantages and effectiveness of the proposed methods.

Recent Advances on Recursive Filtering and Sliding Mode Design for Networked Nonlinear Stochastic Systems: A Survey

Abstract: Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Multiobjective Identification of Controlling Areas in Neuronal Networks

Abstract: In this paper, we investigate the multiobjective identification of controlling areas in the neuronal network of a cat's brain by considering two measures of controllability simultaneously. By utilizing nondominated sorting mechanisms and composite differential evolution (CoDE), a reference-point-based nondominated sorting composite differential evolution (RP-NSCDE) is developed to tackle the multiobjective identification of controlling areas in the neuronal network. The proposed RP-NSCDE shows its promising performance in terms of accuracy and convergence speed, in comparison to nondominated sorting genetic algorithms II. The proposed method is also compared with other representative statistical methods in the complex network theory, single objective, and constraint optimization methods to illustrate its effectiveness and reliability. It is shown that there exists a tradeoff between minimizing two objectives, and therefore pareto fronts (PFs) can be plotted. The developed approaches and findings can also be applied to coordination control of various kinds of real-world complex networks including biological networks and social networks, and so on.

Modeling and Parameter Analysis of the OC3-Hywind Floating Wind Turbine with a Tuned Mass Damper in Nacelle

Abstract: Floating wind turbine will suffer from more fatigue and ultimate loads compared with fixed-bottom installation due to its floating foundation, while structural control offers a possible solution for direct load reduction. This paper deals with the modelling and parameter tuning of a spar-type floating wind turbine with a tuned mass damper (TMD) installed in nacelle. First of all, a mathematical model for the platform surge-heave-pitch motion and TMD-nacelle interaction is established based on D’Alembert’s principle. Both intrinsic dynamics and external hydro and mooring effects are captured in the model, while tower flexibility is also featured. Then, different parameter tuning methods are adopted to determine the TMD parameters for effective load reduction. Finally, fully coupled nonlinear wind turbine simulations with different designs are conducted in different wind and wave conditions. The results demonstrate that the design of TMD with small spring and damping coefficients will achieve much load reduction in the above rated condition. However, it will deteriorate system performance when the turbine is working in the below rated or parked situations. In contrast, the design with large spring and damping constants will produce moderate load reduction in all working conditions.

Stability analysis and control design for 2-D fuzzy systems via basis-dependent Lyapunov functions

Abstract: This paper investigates the problem of stability analysis and stabilization for two-dimensional (2-D) discrete fuzzy systems. The 2-D fuzzy system model is established based on the Fornasini---Marchesini local state-space model, and a control design procedure is proposed based on a relaxed approach in which basis-dependent Lyapunov functions are used. First, nonquadratic stability conditions are derived by means of linear matrix inequality (LMI) technique. Then, by introducing an additional instrumental matrix variable, the stabilization problem for 2-D fuzzy systems is addressed, with LMI conditions obtained for the existence of stabilizing controllers. Finally, the effectiveness and advantages of the proposed design methods based on basis-dependent Lyapunov functions are shown via two examples.

Automated Inspection of E-Shaped Magnetic Core Elements Using K-tSL-Center Clustering and Active Shape Models

Abstract: Automated optical inspection (AOI) has been widely used in industrial Quality Assurance (QA) procedures. Multi-task inspection in high-speed AOI systems is becoming a significant problem in the design. In this paper, the design of an AOI system for E-shaped magnetic core elements is briefly described and several novel algorithms are proposed to realize defects detection by this system. First, this paper proposes a robust k-tSL-center clustering method to classify the interfaces of the element into normal and damaged areas. Second, a modified Active Shape Model (ASM) method is adopted to perform shape distortion detection in real-time. Performance evaluations are carried out on an E-shaped Magnetic Core Image Database, in which all images are captured by the designed AOI system. Experimental results show that the proposed methods are more efficient, robust and accurate than state-of-the-art methods in this application.

Allocation of Actuators and Sensors for Coupled-Adjacent-Building Vibration Attenuation

Abstract: An actuator and sensor allocation approach is proposed for the design of coupled-adjacent-building vibration suppression under seismic excitation. This paper first establishes a full-order model of adjacent buildings with the location information of actuators and sensors. Then, the order of the model is reduced via modal cost analysis, by retaining the modes contributing the most. In view of the fact that the output powers of the actuators are limited, this paper brings forward a mixed H∞/GH2 control. By considering that not all the states of the system can be measured by the sensors, a dynamic output feedback controller is designed. The genetic algorithm is employed to obtain the locations of the actuators and sensors, as well as the corresponding controller. With the proposed approach, the allocation problem is solved, and the vibration of coupled adjacent buildings is attenuated at a sufficiently low level with constrained acting forces. Simulations demonstrate the effectiveness and robustness of the proposed approach in attenuating building vibration under earthquake excitation.

Adaptive Fuzzy Integral Sliding Mode Control for Flexible Air-Breathing Hypersonic Vehicles Subject to Input Nonlinearity

Abstract: This paper proposes a fuzzy integral sliding mode control strategy for flexible air-breathing hypersonic vehicles with input nonlinearity. Based on the complex nonlinear dynamics in the considered vehicles, a control-oriented model, which can retain the dominant features of the higher-fidelity model, is adopted for the control design. First, the T-S fuzzy approach is utilized to model the nonlinear dynamic of flexible air-breathing hypersonic vehicles, and the input nonlinearity model, which comprises dead-zones, sector nonlinearities, and actuator saturation, is considered. Then, based on the constructed T-S fuzzy model, a novel fuzzy integral sliding mode control method is proposed. This method can eliminate the reaching phase of the traditional sliding mode control by designing a novel sliding surface. Moreover, by the parallel-distributed compensation scheme, a sufficient condition is established to guarantee the global robust stability of the sliding mode dynamics in the specified surface in ...

Guaranteed cost control for discrete-time Markovian jump linear system with time delay

Abstract: The scaled small gain theorem is used to investigate the problem of guaranteed cost control for discrete-time Markovian jump systems with mode-dependent and time-varying delay in this article. For obtaining the results in this article, first a stochastic scaled small gain condition for discrete-time Markovian jump systems is introduced. Then, the original system is transformed into an input–output form. The robust stability criterion of the original system is proposed through the Lyapunov–Krasovskii functional approach combined with the utilisation of the stochastic small gain condition. The merit of the proposed criterion lies in its reduced conservatism, which is made possible by a precise approximation of the mode-dependent and time-varying delay. Furthermore, the guaranteed cost controller existence criterion is proposed by the robust stability criterion, which guarantees the robust stochastic stability of the closed-loop system and the existence of the upper bound for the cost function. Finally, two illustrative examples are provided to demonstrate the advantage and the effectiveness of the obtained results.

H∞ control of switched delayed systems with average dwell time

Abstract: This paper considers the problems of stability analysis and H∞ controller design of time-delay switched systems with average dwell time. In order to obtain less conservative results than what is seen in the literature, a tighter bound for the state delay term is estimated. Based on the scaled small gain theorem and the model transformation method, an improved exponential stability criterion for time-delay switched systems with average dwell time is formulated in the form of convex matrix inequalities. The aim of the proposed approach is to reduce the minimal average dwell time of the systems, which is made possible by a new Lyapunov–Krasovskii functional combined with the scaled small gain theorem. It is shown that this approach is able to tolerate a smaller dwell time or a larger admissible delay bound for the given conditions than most of the approaches seen in the literature. Moreover, the exponential H∞ controller can be constructed by solving a set of conditions, which is developed on the basis of th...

Autonomous impulsive rendezvous for spacecraft under orbital uncertainty and thruster faults

Abstract: This paper investigates the reliable impulsive control problem for autonomous spacecraft rendezvous under the orbital uncertainty and possible thruster faults. The orbital uncertainty is described as the model uncertainty, and the possible thruster faults are modelled by scaling factors. By introducing a state-feedback controller, the autonomous rendezvous problem is regarded as an asymptotic stabilization problem of a switching system composed of impulse action phase and free motion phase. Based on Lyapunov theory and genetic algorithms (GA), a reliable impulsive controller design approach is proposed. With the obtained controller, the autonomous spacecraft rendezvous is accomplished by a series of proper impulse thrust in spite of the orbital uncertainty and the possible thruster faults. The effectiveness of the proposed approach is illustrated by simulation examples.