Showing papers in "Journal of The Franklin Institute-engineering and Applied Mathematics in 2016"

Admissible output consensualization control for singular multi-agent systems with time delays

Abstract: Admissible output consensus design problems for high-order linear time-invariant singular multi-agent systems with constant time delays are dealt with. Firstly, based on the observability decomposition, a dynamic output feedback consensus protocol is proposed, which makes singular multi-agent systems satisfy some separation principle and can simplify admissible output consensus design problems. Then, LMI criteria for admissible output consensualization are presented, which can guarantee the regular and impulse-free properties of singular multi-agent systems directly. Moreover, an approach to determine the output consensus function is presented on the basis of the first equivalent form and the impacts of initial states of consensus protocols and dynamic agents are determined, respectively. Finally, numerical examples are shown to demonstrate theoretical results.

Continuous finite- and fixed-time high-order regulators

Abstract: The contribution of this paper is twofold. First, an upper estimate of the convergence (settling) time is calculated for the finite-time convergent control algorithm that drives the state of a series of integrators to the origin. To the best of our knowledge, such an estimate is obtained for the first time. Second, a novel fixed-time continuous control law is proposed for a chain of integrators of an arbitrary dimension. Its fixed-time convergence is established and the uniform upper bound of the settling time is computed. The theoretical developments are applied to a case study of controlling a DC motor.

Recent advances on filtering and control for cyber-physical systems under security and resource constraints

Abstract: In this paper, some recent advances on the filtering and control problems are reviewed for cyber-physical systems under security and resource constraints. Three types of cyber-attacks, namely, denial-of-service (DoS) attacks, replay attacks and deception attacks, are discussed from the security viewpoint. The resource constraints under consideration mainly include energy constraints, timing constraints and communication constraints. Firstly, cyber-physical systems are introduced and their board range of applications is discussed. Then, with security and resource constraints, the developments on various filtering and control issues for cyber-physical systems are reviewed in great details. In addition, some latest results on cyber-physical systems are presented. Finally, conclusions are given and some possible future research directions are pointed out.

Delay-dependent stability and dissipativity analysis of generalized neural networks with Markovian jump parameters and two delay components

Abstract: This paper focuses on the problem of delay-dependent stability and dissipativity analysis of generalized neural networks (GNNs) with Markovian jump parameters and two delay components. By constructing novel augmented Lyapunov–Krasovskii functional (LKF), using free-matrix-based inequality to estimate the derivative of Lyapunov function and employing the reciprocally convex approach to consider the relationship between the time-varying delay and its interval, some improved delay-dependent stability criteria and dissipativity criteria are established in terms of linear matrix inequalities. Moreover, the obtained criteria is extended to analyze the stability analysis of GNNs with two delay components and the passivity analysis of GNNs with one delay. Numerical examples are given to show the effectiveness and the significant improvement of the proposed methods.

The recursive least squares identification algorithm for a class of Wiener nonlinear systems

Abstract: Many physical systems can be modeled by a Wiener nonlinear model, which consists of a linear dynamic system followed by a nonlinear static function. This work is concerned with the identification of Wiener systems whose output nonlinear function is assumed to be continuous and invertible. A recursive least squares algorithm is presented based on the auxiliary model identification idea. To solve the difficulty of the information vector including the unmeasurable variables, the unknown terms in the information vector are replaced with their estimates, which are computed through the preceding parameter estimates. Finally, an example is given to support the proposed method.

Finite-time H∞ control of switched systems with mode-dependent average dwell time

Abstract: In this paper, finite-time H ∞ control problem for a class of switched linear systems with mode-dependent average dwell time (MDADT) is investigated in discrete-time context. Since each mode has its own average dwell time (ADT), the switching law in this paper is more general than the average dwell time. Mode dependent controllers, which guarantee the finite-time boundedness with a prescribed H ∞ performance of the closed-loop systems, are designed for the switched system. Two numerical examples are given to demonstrate the validity of the proposed techniques.

Reconfigurable spacecraft attitude takeover control in post-capture of target by space manipulators

Abstract: Most current research on reconfigurable control system puts emphasis on reconfiguration for adapting to actuator failures. However, the reconfigurable control system is necessitated for spacecraft attitude takeover control in the application of capturing target spacecraft whose fuel is exhausted to extend its operational lifetime by supplying them propulsion, navigation and guidance services. In this scenario, the capture of target spacecraft by space manipulators will cause a large shift in the dynamics of the service spacecraft. Not only do the mass properties change, but also does the thruster configuration. The changes in the mass, center of mass and inertia of the combined spacecraft will cause changes in the equivalent force exerted by each thruster. In this paper, considering the changes of thruster configuration and the control reallocation, a reconfigurable control system is designed for spacecraft attitude takeover control in post-capture of target by space manipulators in order to adapt to changes in the mass properties. The unknown inertia properties of target spacecraft in the system constitute a formidable technical challenge for controller design. Therefore, a modified adaptive dynamic inverse controller is proposed to provide global asymptotic stability in the presence of model uncertainties and nonlinearities. Moreover, by the null-space intersections control reallocation method, the thrust forces of service spacecraft can be redistributed and satisfy some constraints. Numerical simulations validate the feasibility of reconfigurable spacecraft attitude takeover control with large center of mass shifts and unknown inertia properties.

Robust state/fault estimation and fault tolerant control for T–S fuzzy systems with sensor and actuator faults

Abstract: This paper addresses the problems of state/fault estimation (FE) and fault tolerant control (FTC) for a class of Takagi–Sugeno (T–S) fuzzy systems subject to simultaneously external disturbances, sensor and actuator faults. A fuzzy-reduced-order robust state/fault estimation observer is proposed in this paper. The observer can not only estimate system state, sensor and actuator faults simultaneously, but also attenuate the influence of disturbances. Compared with the existing results, the observer has a wider application range and a lower dimension. Using the information of estimation, an observer-based fault tolerant controller is designed to compensate the fault effect and guarantee the stability of closed-loop system. In the paper, the observer and the controller are designed separately, which can avoid the coupling between them. As a result, the gain matrices of the observer and the controller can be calculated separately. At last, the simulations show the effectiveness of the proposed method.

Synchronization for fractional-order time-delayed memristor-based neural networks with parameter uncertainty

Abstract: In this paper, the global synchronization for fractional-order multiple time-delayed memristor-based neural networks with parameter uncertainty is investigated. A comparison theorem for a class of fractional-order multiple time-delayed systems is given. Under the framework of Filippov solution and differential inclusion theory, the synchronization conditions of fractional-order multiple time-delayed memristor-based neural networks with parameter uncertainty are derived, based on the given comparison theorem and Lyapunov method. Furthermore, the global asymptotical stability conditions of fractional-order multiple time-delayed memristor-based neural networks are obtained. Finally, two numerical examples are presented to show the effectiveness of our theoretical results.

Back-stepping robust trajectory linearization control for hypersonic reentry vehicle via novel tracking differentiator

Abstract: This paper proposes a back-stepping robust trajectory linearization control (TLC) design for hypersonic reentry vehicle (HRV) attitude tracking problem from a novel tracking differentiator perspective. First, the attitude kinematics and dynamics for HRV is formulated and rewritten in feedback form with mismatched and matched uncertainties introduced by variations of various aerodynamic coefficients. Second, a sigmoid function based novel tracking differentiator (STD) with global fast convergence property, simple structure and chattering-free in differential estimation is developed to handle the “explosion of term” problem in back-stepping TLC design. In addition, dynamical performance and noise-attenuation ability of STD are analyzed in frequency domain by describing function method. Third, how to convert between sigmoid function based disturbance observer (SDO) and STD is given, and based on the estimates of uncertainties provided by SDO in attitude and angular rate loop, the back-stepping robust TLC is synthesized to track the respective commands in dual-loop. Then, the stability of the composite SDO-enhanced back-stepping TLC approach is established. Finally, extensive simulation results are presented to demonstrate the effectiveness of the proposed control strategy in improving disturbance attenuation ability and performance robustness against multiple uncertainties.

Robust identification of OE model with constrained output using optimal input design

Abstract: This paper considers the robust algorithm for identification of OE (output error) model with constrained output in presence of non-Gaussian noises. In practical conditions, in measurements there are rare, inconsistent observations with the largest part of population of observations (outliers). The synthesis of robust algorithms is based on Huber׳s theory of robust statistics. Also, it is known fact that constraints play a very important role in many practical cases. If constraints are not taken into consideration, the control performance can corrupt and safety of a process may be at risk. The practical value of proposed robust algorithm for estimation of OE model parameters with constrained output variance is further increased by using an optimal input design. It is shown that the optimal input can be obtained by a minimum variance controller whose reference is a white noise sequence with known variance. A key problem is that the optimal input depends on system parameters to be identified. In order to be able to implement the proposed optimal input, the adaptive two-stage procedure for generating the input signal is proposed. Theoretical results are illustrated by simulations which show significant increasing of accuracy in parameter estimates of the OE model by using the robust identification procedure in relation to the linear identification algorithm for OE models. Also, it can be seen that the convergence rate of the robust algorithm is further increased by using the optimal input design, which increases the practical value of proposed robust procedure.

Quality-related fault detection using linear and nonlinear principal component regression

Abstract: The issue of quality-related fault detection is a hot research topic in the process monitoring community in the recent five years. Several modifications based on partial least squares (PLS) have been proposed to solve the relevant problems for linear systems. For the systems with nonlinear characteristics, some modified algorithms based on kernel partial least squares (KPLS) have also been designed very recently. However, most of the existing methods suffer from the defect that their performances are not stable when the fault intensity increases. More importantly, there is no way yet to solve the linear and nonlinear problems in a uniform algorithm structure, which is very important for simplifying the design steps of fault detection systems. This paper aims to propose such approaches based on principal component regression (PCR) and kernel principal component regression (KPCR). Such that, relevant problems in linear and nonlinear systems can be solved in the same way. Two literature examples are used to test the performance of the proposed approaches.

Distributed adaptive coordination control for uncertain nonlinear multi-agent systems with dead-zone input

Abstract: In this paper, a distributed design scheme is developed for consensus tracking control of multi-agent system with nonlinear input under a weighted directed graph topology. Each agent is modeled by a strict-feedback nonlinear system with unknown nonlinear dynamics and unknown external disturbances. The time-varying leader node only gives commands to a small portion of the followers. By using backstepping technique and neural networks method, adaptive distributed controllers for each follower node are constructed, which only require relative state information between themselves and their neighbors. The proposed controllers and adaptive laws guarantee that the tracking errors between all followers and the leader convergence to a small neighborhood of the origin. Moreover, by employing the maximum norm of the unknown neural network weight vectors as the estimated parameter, the algorithm proposed in this paper contains only N(N represents the number of the followers) adaptive parameters that need to be updated online. The number of online learning parameters is independent of the number of the neural networks׳ nodes, which reduces the computation burden significantly. Finally, a numerical example demonstrates the effectiveness of the proposed approach.

Robust speed control of hybrid electric vehicle using fractional order fuzzy PD and PI controllers in cascade control loop

Abstract: In this paper, a novel scheme is proposed for speed control of highly nonlinear hybrid electric vehicle (HEV) having an electronic throttle control system (ETCS) in the cascade control loop. Fractional order fuzzy PD (FOFPD) and fractional order fuzzy PI (FOFPI) nonlinear controllers are developed and used as primary and secondary controllers, respectively in the cascade control loop. These controllers are variable gain fuzzy controllers having an adaptive nature. Their corresponding integral counterpart, fuzzy PD and fuzzy PI controllers are realized by keeping integer order differentiator and integrator in FOFPD and FOFPI controllers. Further, fractional order PD (FOPD) and fractional order PI (FOPI) controllers are implemented by using non-integer order operators in conventional PD and PI controllers. Extensive simulations have been carried out using National Instruments software LabVIEW™ and its add-on tools such as control design and simulation toolkit to perform a comparative study for FOFPD, FPD, FOPD and PD as primary controller and FOFPI, FPI, FOPI and PI as secondary controller for setpoint tracking of speed of HEV. Multi objective genetic algorithm is used to optimize the gains of primary and secondary controllers for minimization of integral of absolute error (IAE), maximum overshoot and settling time. Performances of tuned controllers are further evaluated for tracking of speed profile, disturbance rejection and model uncertainty. It has been observed that combination of FOFPD and FOFPI controllers outperformed rest of the controllers in servo, regulatory and uncertain environment and demonstrate very robust behavior for speed control of HEV.

An intelligent adaptive control of DC–DC buck converters

Abstract: Buck DC–DC converter is used in many applications to supply a fixed amount of DC voltage. They are highly sensitive to the frequently changing loading conditions. Such a situation demands a robust control mechanism which can guarantee satisfactory performance of the buck converter over a widely changing load. This can be made possible by developing an adaptive control scheme which can estimate the true values of the uncertain load parameters in the least possible time. This paper proposes an adaptive Chebyshev neural network (CNN) based backstepping control technique for the output voltage regulation of a DC–DC buck converter. The proposed control strategy utilizes neural networks in approximating the unknown non-linear nature of load resistance by using orthogonal basis Chebyshev polynomials. CNN approximation tool in conjunction with the conventional backstepping procedure yields a robust control mechanism. The weights of neural network are tuned online using adaptive laws satisfying the overall closed loop stability criterion in the Lyapunov sense. The performance of the proposed control is demonstrated for wide range perturbations by subjecting the buck converter to changes in load resistance, input voltage and reference output voltage. Simulation studies are conducted to evaluate the performance of the proposed controller against radial basis function neural network based adaptive backstepping control and conventional adaptive backstepping. The results obtained are further verified from experimentation on a hardware setup using DSP based TM320F240 processor. Thus, the investigation confirms effectiveness of the proposed control scheme as the output voltage shows a fast and accurate response besides successfully rejecting the disturbances acting upon it.

On observer-based control of one-sided Lipschitz systems

Abstract: This paper describes the novel observer-based controller design for the one-sided Lipschitz nonlinear systems. An observer-based controller design condition is obtained by application of the Lyapunov theory, by involving the one-sided Lipschitz condition and quadratic inner-boundedness criterion, and by incorporating the matrix inequality procedures. Further, a decoupling condition, necessary and sufficient for the main design approach, is developed to determine the controller and observer gains. Furthermore, two design conditions, a computationally simple sufficient condition and a more generic necessary and sufficient condition, based on the existing and the novel treatments for the one-sided Lipchitz nonlinearity are evaluated to obtain the observer-based control solution. An algorithm for solving the proposed design constraints by combining a nested bilinear-terms-solver approach and a nonlinear-optimization-based cone complementary linearization method is provided. The effectiveness of the proposed observer-based controller design for the one-sided Lipschitz nonlinear systems is demonstrated using a numerical example.

Adaptive sliding fault tolerant control for nonlinear uncertain active suspension systems

Abstract: This paper is concerned with the fault tolerant control problem of nonlinear uncertain active suspension systems with constraint requirements. A novel adaptive sliding fault tolerant controller, which does depend on accurate models, is designed to stabilize the active suspension systems and thus to improve the ride comfort, without utilizing the bounds of actuator faults and parameter uncertainties. Furthermore, an H∞ optimization scheme based on differential evolution (DE) algorithm and linear matrix inequalities (LMIs) is introduced to design appropriate parameters of the sliding surface, which guarantees the constraint requirements of active suspension systems. Finally, simulation results are included to illustrate the effectiveness of the proposed strategy.

Synchronization of hybrid-coupled delayed dynamical networks via aperiodically intermittent pinning control

Abstract: In this paper, we concern the exponential synchronization problem for hybrid-coupled delayed dynamical networks via pinning aperiodically intermittent control. Different from previous works, the delayed coupling term considered here contains the transmission delay and self-feedback delay, and the intermittent control can be aperiodic. By establishing a new differential inequality and constructing Lyapunov function, several useful criteria are derived analytically to realise exponential synchronization for both free time delay (there is no restriction imposed on the delay and the control (and/or rest) width) and small time delay (the delay is smaller than the minimum of control width). Moreover, the detail of pinning and aperiodically intermittent control strategy are provided. Finally, a numerical example is given to demonstrate the validness of the proposed scheme.

Vehicle state estimation based on Minimum Model Error criterion combining with Extended Kalman Filter

Abstract: This paper researched an estimation method based on the Minimum Model Error (MME) criterion combing with the Extended Kalman Filter (EKF) for 4WD vehicle states. A general 5-input–3-output and 3 states estimation system was established, considering both the arbitrary nonlinear model error and the white Gauss measurement noise. Aiming at eliminating the estimation error caused by the arbitrary nonlinear model error, the prediction algorithm for the dynamic tire force error was deduced based on the MME criterion, based on which the system model can be effectively updated for higher estimation accuracy. The estimation algorithm was applied to a two-motor-driven vehicle during a double-lane-change process with varying speed under simulative experimental condition. The results showed that the dynamic tire force error could be effectively found for updating the system model, and higher estimation accuracy of the vehicle states were achieved, when compared with the traditional EKF estimator.

Ensemble extreme learning machine and sparse representation classification

Abstract: Extreme learning machine (ELM) combining with sparse representation classification (ELM-SRC) has been developed for image classification recently. However, employing a single ELM network with random hidden parameters may lead to unstable generalization and data partition performance in ELM-SRC. To alleviate this deficiency, we propose an enhanced ensemble based ELM and SRC algorithm (En-SRC) in this paper. Rather than using the output of a single ELM to decide the threshold for data partition, En-SRC incorporates multiple ensembles to enhance the reliability of the classifier. Different from ELM-SRC, a theoretical analysis on the data partition threshold selection of En-SRC is given. Extension to the ensemble based regularized ELM with SRC (EnR-SRC) is also presented in the paper. Experiments on a number of benchmark classification databases show that the proposed methods win a better classification performance with a lower computational complexity than the ELM-SRC approach.

An auxiliary model based least squares algorithm for a dual-rate state space system with time-delay using the data filtering☆

Abstract: For dual-rate state space systems with time-delay, this paper combines the auxiliary model identification idea with the filtering technique, transforms the state space model into the identification model with different input and output sampling rates, and presents a filtering and auxiliary model based recursive least squares identification algorithm with finite measurement input–output data. Compared with the auxiliary model based recursive least squares algorithm, the proposed algorithm can generate more accurate parameter estimates and has a higher computational efficiency because the dimensions of its covariance matrices become small.

On switching manifold design for terminal sliding mode control

Abstract: In this paper, a general design scheme is proposed for finite-time switching mode manifolds and corresponding nonsingular controllers. The reduced system is obtained from terminal sliding mode control (TSMC) and its homogeneity is investigated, which also illustrates that TSMC is finite-time control by the homogeneous theory. Based on the homogeneity analysis, a design proposal is provided for general finite-time switching manifolds and corresponding controllers, which guarantee that all states of a controlled system are finite-time convergent to the origin. In simulations, three finite-time switching manifolds are presented and the corresponding controllers are validated to perform the finite-time control for the double integral system.

Observer-based consensus tracking of multi-agent systems with one-sided Lipschitz nonlinearity

Abstract: This paper investigates the observer-based consensus tracking problem of multi-agent systems with one-sided Lipschitz nonlinearity. The agent dynamics considered here covers a broad family of nonlinear systems, and includes the well-known Lipschitz system as a special case. To achieve consensus tracking for such multi-agent systems, two types of observer-based protocols named the continuous protocol and the intermittent protocol are proposed. Furthermore, several multi-step design algorithms are presented to select the observer gains and the controller parameters of the proposed protocols. It is shown that the established sufficient criteria can not only ensure the observer error to approach to zero, but also realize the consensus tracking of multi-agent systems. The obtained results are illustrated by two simulation examples.

Event triggered control for Markovian jump systems with partially unknown transition probabilities and actuator saturation

Abstract: In this paper, the problem of controller design using event triggered strategy for Markovian jump systems subject to actuator saturation is investigated. Such a problem is motivated by the following aspects. The transition probabilities of Markovian jump systems can usually not be completely accessible. Actuator saturation exists virtually in all practical applications and event triggered scheme reduces communication burden and saves computation energy. A stochastic stabilization condition is derived as well as the lower bound of inter-event time between two consecutive events to avoid Zeno behavior. The corresponding optimization algorithm for obtaining the biggest domain of attraction in mean square sense is then formulated. Finally, some examples are given to evaluate the effectiveness of our results.

Robust adaptive sliding mode control for uncertain nonlinear MIMO system with guaranteed steady state tracking error bounds

Abstract: In this paper, a robust adaptive sliding mode controller is proposed for a class of uncertain nonlinear multi-input multi-output (MIMO) systems. The upper bounds of the uncertainties are not needed in the procedure of the controller design, and the controller is continuous, which guarantees that the tracking error can converge to a small residual set. Furthermore, explicit formulas are given that allow for calculating the size of the residual set, and the bounds of the tracking errors at steady state can be specified a priori and guaranteed by choosing certain design parameters. Finally, a simulation study based on a two-link rigid robotic manipulator model is used to illustrate the effectiveness of the proposed controller.

Distributed containment control of fractional-order uncertain multi-agent systems☆

Abstract: This paper investigates the containment control problem of uncertain linear multi-agent systems, where the dynamics of each agent is described by a fractional-order differential equation. Based on the stability theory of fractional-order systems and matrix theory, some sufficient conditions are presented to ensure that the states of the followers can asymptotically converge to the convex hull formed by those of the leaders, and the feedback matrix of the proposed protocol is also determined according to linear matrix inequalities. Two simulation examples are provided to demonstrate the effectiveness of the theoretical results.

Unified filters design for singular Markovian jump systems with time-varying delays

Abstract: This paper considers the problem of unified mode-dependent and mode-independent filters design for continuous-time singular systems with Markovian jumping parameters and time-varying delays. The main objective of this paper is to design the L 2 − L ∞ , H ∞ , passivity, strict (Q, S, R)-dissipativity, very-strict passivity filters in a unified framework so as to ensure that the filter error system is stochastically admissible and extended dissipative. By tuning the weighting matrices, the extended dissipativity performance can reduce to the L 2 − L ∞ performance, H ∞ performance, passivity, strict (Q, S, R)-dissipativity and very-strict passivity, respectively. A mode-dependent and delay-dependent stochastic Lyapunov–Krasovskii functional is proposed to reflect the information of Markovian jump modes and the time-varying delays, and a set of linear matrix inequalities (LMIs) are utilized to derive sufficient conditions which guarantee that the desired unified filters can be constructed. Three numerical examples including an oil catalytic cracking process (OCCP) are employed to verify the usefulness and effectiveness of the main results obtained.

Exponential lag synchronization for memristor-based neural networks with mixed time delays via hybrid switching control

Abstract: In this paper, the exponential lag synchronization for a class of memristor-based neural networks with mixed time-delays is investigated via hybrid switching control method. Based on the theory of differential equations with discontinuous right-hand side, several new sufficient conditions ensuring the exponential lag synchronization of memristor-based chaotic neural networks are obtained by designing two diffident hybrid switching controllers and constructing novel Lyapunov functionals. Finally, a numerical example with simulation is given to show the effectiveness and feasibility of the obtained results.

Synchronization of complex-valued neural network with sliding mode control

Abstract: In this paper, the synchronization and module-phase synchronization of complex-valued neural network are studied with sliding mode controllers. Both inner synchronization in the same complex-valued neural network and outer synchronization of two complex-valued networks are considered. By separating the real part and imaginary part, a new complex-valued neural network model is provided and module-phase synchronization is defined. With an equivalent linear controller, sliding mode controllers are designed to get the synchronization and module-phase synchronization theorems. Finally, some numerical simulations are presented to show the robustness and effectiveness of our study.

Iterative algorithms for X+ATX−1A=I by using the hierarchical identification principle☆

Abstract: By using the hierarchical identification principle and introducing the convergence factor and the iterative matrix, a family of inversion-free iterative algorithms is proposed for solving nonlinear matrix equations X + A T X − 1 A = I . The convergence is proved and the convergence speed is analyzed. The suggested iterative algorithm includes some previous algorithms as its special cases. Two numerical examples are given to illustrate the proposed algorithms. Theoretical analysis and numerical examples show that for appropriate convergence factors, the proposed algorithms are more efficient than the existing inversion-free iterative algorithms.