Search

Although flexible neural networks FNNs have been used more successfully than classical neural networks CNNs in many industrial applications, nothing is rigorously known about their properties. In fact they are not even well known to the systems and control community. In the first part of this paper, existing structures of and results on FNNs are surveyed. In the second part FNNs are examined in a theoretical framework. As a result, theoretical evidence is given for the superiority of FNNs over CNNs and further properties of the former are developed. More precisely, several fundamental properties of feedforward and recurrent FNNs are established. This includes the universal approximation capability, minimality, controllability, observability, and identifiability. In the broad sense, the results of this paper help that general use of FNNs in systems and control theory and applications be based on firm theoretical foundations. Theoretical analysis and synthesis of FNN-based systems thus become possible. The paper is concluded by a collection of topics for future work.

There are usually many sources for the supply of raw material to a pulp or paper mill in Sweden. Optimization of this supply is therefore a challenging task, and can only be managed properly if all aspects of risk are considered. In our study, these risks are related to when the weather reduces the load-bearing capacity of the ground or the roads. A stochastic and a deterministic model have been formulated, and they have been solved with mixed-integer quadratic programming and tested with data from a Swedish forest company. The results of this Study show that the option value is greater than zero and that both the optimal policy and the option value change whenever the storage cost is altered. This shows that the optimal planning policy obtained from the stochastic model differs from the solution of the deterministic model.

Non-impact printing techniques have in many fields of application replaced traditional printing methods. Apart from factors such as the type and conditions of ink/paper transfer and ink properties, the quality of the image produced with an office ink-jet printer depends decisively on paper characteristics. In this study, various medium- and photo-quality ink-jet paper sorts were examined for their general, optical, surface and printing properties. Based on these results, the ranking of tested paper properties according to their significance for ink-jet paper quality on one hand and the suitability of individual paper sorts for this printing technique on the other was determined using multivariate tool principal components analysis.

Stochastic models are developed to determine the production rate and efficiency of an integrated pulp and paper factory, which consists of a pulp mill, a storage tank and two parallel paper mills or pulp dryers. The effects of storage tank capacity and random stoppages in the mills on line efficiency and line output rate are investigated. The models are general and can be used for any continuous process system

A new technique for decoupling controller design is proposed. A reference model representing the desired closed-loop dynamic behaviour is chosen. A quadratic performance index is used to measure both coupling and tracking errors in the closed-loop system. The system state equation is organized in incremental canonical form. The optimal decoupling control law is obtained by minimizing the performance index. This algorithm has been applied to a paper machine headbox control system. The performance of the system under different conditions is satisfactory.

Control of batch digesters for magnesium bisulphite pulp cooking is considered. The aim of the study is to obtain a systematic, as well as a systemic problem definition for the subsequent design of higher-level control algorithms. The method of analysis was taken from general systems theory; the general scheme of the systems approach has been adapted to the specific control design problem. Following this approach, first, the behaviour of the basic control algorithms was explored by computer simulation. Based on these preliminary results, a systemic representation of the overall control problem was obtained by defining the pulp cooking process and the basic control algorithms together as a system with three inputs and three outputs. Further, the input-output relations of the system were examined by simulation. The final results are given in the form of three diagrams representing the static responses of the object system to the step changes of a single input. The significance of these characterist...

The paper deals with active fault diagnosis of stochastic large-scale systems consisting of several subsystems with separate inputs and observations, which are coupled through the system state. The subsystems are described by multiple models expressing their fault-free and faulty behaviour. The transition between the models is governed by a Markov chain. The paper proposes a distributed design of an active fault diagnosis algorithm, which takes into account the coupling among the subsystems in all stages of the algorithm. This results in a higher quality of the excitation signal and consequently in better decisions. The numerical example shows the improved performance of the proposed algorithm in comparison with the algorithms based on the decentralised design.

This paper investigates the containment control problem of multi-agent systems with semi-Markovian switching topologies. Different from the related works, this paper considers a more general case t...

This paper researches the stability and stabilisation problems of stochastic neural network with multiple time delays. As for time delay, neuron state delay is first introduced in this paper. The n...

This paper is motivated by a need to address the challenge of securing a safe landing after suffering from inflight impairment. In this paper, a new adaptive generalised model predictive static pro...

The problem of impulse analysis is considered for the T-S fuzzy singular system by using the Kronecker index and the generalised Kronecker index in this paper. The T-S singular system is divided into two cases: the uniformly regular T-S fuzzy singular system and the non-uniformly regular T-S fuzzy singular system. The contribution of this paper is twofold. First, a new definition of the generalised Kronecker form is introduced, and the algorithm of finding the generalised Kronecker form is given. Second, two theorems are proved, one is that the uniformly regular T-S fuzzy singular system is impulsive if and only if the Kronecker index is greater than one, the other one is the non-uniformly regular T-S fuzzy singular system is impulsive if and only if the generalised Kronecker index is greater than one. Finally, two numerical simulations and the population model with stage structure are carried out to show the consistency with theoretical analysis and illustrate the effectiveness of the algorithm.

This paper deals with the filter design problem of two-dimensional (2-D) discrete-time nonlinear systems described by Fornasini–Marchesini local state–space (FM LSS) model under Takagi–Sugeno (T–S) fuzzy rules. The frequency of disturbance input is assumed to be known and to reside in a finite frequency (FF) range. A novel so-called FF l2 gain is defined for 2-D discrete-time systems, which extends the standard l2 gain. The aim of this paper is to design filters such that the filtering error system is asymptotically stable and has the disturbance attenuation performance in sense of FF l2 gain. Sufficient conditions for the existence of a desired fuzzy filter are established in terms of linear matrix inequalities (LMIs). Simulation examples demonstrate the technique and its advantage.

This paper proposes a new fuzzy model predictive control approach for continuous-time nonlinear systems in terms of linear matrix inequalities (LMIs). The proposed approach is based on the Takagi–Sugeno fuzzy modeling, a quadratic Lyapunov function, and a sampled-data parallel distributed compensation controller with constant sampling time. The goal is designing the sampled-data controller such that at each sampling time, the stability of the closed-loop system is guaranteed and an infinite horizon cost function is minimised. The main advantage of the proposed approach is to eliminate the approximations induced from discretizing the original system and cost function upper bound minimisation. Consequently, a lower bound of the cost function is obtained and the performance of the proposed model predictive controller is improved compared to the recently published papers in the same field of interest. In addition, the Euclidean norm constraint of the control input vector is derived in terms of LMIs. To illustrate the merits of the proposed approach, the proposed technique is applied to a continuous stirred tank reactor system.

In this paper, a set of conditions for defining output functions is derived such that a system can become passive by static state feedback. A key feature of the proposed set of conditions is that several output functions that satisfy these conditions can be defined without any transformation into a normal form or any need of linearisation of the system. In essence, this paper introduces a new connection between the Lyapunov stability concept and minimum phase property which is indeed a necessary condition of feedback passivity.

This paper studies adaptive finite-time control problem of a class of nonlinear systems with dynamic and parametric uncertainties. The power of systems in this paper is any positive odd rational number but not necessary equal to or greater than one. To solve this problem, finite-time input-to-state stability (FTISS) is used to characterise the unmeasured dynamic uncertainty. By skillfully combining Lyapunov function, sign function, adding a power integrator, adaptive control and FTISS methods, an adaptive state feedback controller is designed to drive the states to the origin in finite time while maintaining all the closed-loop signals bounded.

This paper investigates the adaptive finite-time modified function projective lag synchronisation with definite attenuation rate for a class of chaotic systems with unknown parameters. Firstly, a novel integral type terminal sliding surface is proposed to ensure the finite-time stability of the sliding motion. Subsequently, an adaptive finite-time control law is designed to guarantee the occurrence of the sliding motion in a limited time and the accurate estimation of the unknown parameters. Moreover, the exponential attenuation rate designed in this paper can further cut down the synchronisation time. Numerical simulations are presented to verify the feasibility and effectiveness of the proposed method.

This paper proposes a new adaptive gain robust model-following/tracking controller for a class of uncertain linear systems. The proposed adaptive gain robust controller is composed of state feedback laws with fixed gains, feedfoward inputs for the reference model and nonlinear compensation inputs with adjustable time-varying parameters. Moreover, the proposed control strategy can achieve good transient performance and avoid the excessive control input by means of the design parameter. In this paper, linear matrix inequality-based sufficient conditions for the existence of the proposed adaptive gain controller are given, and two design strategies are presented. Finally, simple illustrative examples are included to show the effectiveness of the proposed adaptive gain robust controller.

This paper is concerned with the distributed optimisation problem over a multi-agent network, where the objective function is described by a sum of all the local objectives of agents. The target of agents is to collectively reach an optimal solution while minimising the global objective function. Under the assumption that the information exchange among agents is depicted by a sequence of time-varying undirected graphs, a distributed optimisation algorithm with uncoordinated time-varying step-sizes is presented, which signifies that the step-sizes of agents are not always uniform per iteration. In light of some reasonable assumptions, this paper fully conducts an explicit analysis for the convergence rate of the optimisation method. A striking feature is that the algorithm has a geometric convergence rate even if the step-sizes are time-varying and uncoordinated. Simulation results on two numerical experiments in power systems show effectiveness and performance of the proposed algorithm.

Leaderless consensus for the fractional-order nonlinear multi-agent systems is investigated in this paper. At the first part, a control protocol is proposed to achieve leaderless consensus for the nonlinear single-integrator multi-agent systems. At the second part, based on sliding mode estimator, a control protocol is given to solve leaderless consensus for the the nonlinear single-integrator multi-agent systems. It shows that the control protocol can improve the systems’ convergence speed. At the third part, a control protocol is designed to accomplish leaderless consensus for the nonlinear double-integrator multi-agent systems. To judge the systems’ stability in this paper, two classic continuous Lyapunov candidate functions are chosen. Finally, several worked out examples under directed interaction topology are given to prove above results.

This paper investigates distributed connectivity-preserving consensus problems of networked multi-agent systems with limited communication ranges. Compared with existing literature, a main contribution of this paper is to present a new nonlinear transformation approach of consensus errors for preserving the initial interaction patterns of multi-agent systems. Both the consensus and the connectivity preservation can be achieved by using one transformed error function. Based on the proposed nonlinear error transformation, we derive distributed connectivity-preserving consensus algorithms for single-integrator dynamics, double-integrator dynamics and second-order nonlinear systems. The asymptotic stability of consensus errors and the connectivity preservation among agents are established through Lyapunov stability analysis.

This paper concentrates on the dynamical behaviours of memristor-based complex-valued delayed neural networks. By constructing the appropriate Lyapunov functional and utilising some inequality techniques, sufficient conditions are proposed to guarantee the existence and global exponential stability of the periodic solution of the considered system. The proposed results not only generalise some previously related literatures, but also are easy to be checked with the parameters of system itself. In addition, the theoretical results of this paper may be helpful in qualitative analysis for complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of the proposed results.

International audience; This paper proposes a methodology to design a Takagi–Sugeno (TS) descriptor observer for a class of TS descriptor systems. Unlike the popular approach that considers measurable premise variables, this paper considers the premise variables depending on unmeasurable vectors, e.g. the system states. This consideration covers a large class of nonlinear systems and represents a real challenge for the observer synthesis. Sufficient conditions to guarantee robustness against the unmeasurable premise variables and asymptotic convergence of the TS descriptor observer are obtained based on the H∞ approach together with the Lyapunov method. As a result, the designing conditions are given in terms of linear matrix inequalities (LMIs). In addition, sensor fault detection and isolation are performed by means of a generalised observer bank. Two numerical experiments, an electrical circuit and a rolling disc system, are presented in order to illustrate the effectiveness of the proposed method.

In this paper, the fault detection problem is investigated for a class of discrete-time piecewise linear systems with external disturbances and infinite distributed time-delays. As a modelling framework, piecewise linear system often arise when piecewise linear components are encountered, such as dead-zone, saturation, relays and hysteresis. The time-delays are assumed to be infinitely distributed in the discrete-time domain. The aim of this paper is to detect the possible faults and to estimate the system state. For this purpose, firstly, stability analysis is given based on a piecewise smooth Lyapunov function. Afterward, an appropriate approach of fault detection and filter design problem is provided to achieve a satisfactory balance between the disturbances attenuation level γ and the sensitivity to the fault for piecewise linear systems. As a consequence, a sufficient condition is obtained in terms of the linear matrix inequalities such that, for all admissible infinite distributed time-delay...

This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov–Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.

This paper studies the problem of robust stability and stabilisation for uncertain large-scale interconnected nonlinear descriptor systems via proportional plus derivative state feedback or proportional plus derivative output feedback. The basic idea of this work is to use the well-known differential mean value theorem to deal with the nonlinear model such that the considered nonlinear descriptor systems can be transformed into linear parameter varying systems. By using a parameter-dependent Lyapunov function, a decentralised proportional plus derivative state feedback controller and decentralised proportional plus derivative output feedback controller are designed, respectively such that the closed-loop system is quadratically normal and quadratically stable. Finally, a hypersonic vehicle practical simulation example and numerical example are given to illustrate the effectiveness of the results obtained in this paper.

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.

In this paper, an exponential synchronisation problem for neutral-type multi-slave hybrid systems with stochastic perturbation is discussed, where the adaptive synchronisation model involves a master system and multiple slave systems. By the use of generalised Ito^'s formula and M-matrix method, a sufficient condition is obtained to guarantee the stability of the error system, and the update law of the feedback controller is determined to deduce the synchronisation between the master system and the sum system of all slave systems. Finally, a numerical example is given to illustrate the effectiveness of the results obtained in this paper.

This paper is concerned with the fault detection problem for two-dimensional 2-D discrete-time systems described by the Fornasini–Marchesini local state-space model. The goal of the paper is to design a fault detection filter to detect the occurrence of faults in finite-frequency domain. To this end, a finite-frequency H− index is used to describe fault sensitivity performance, and a finite-frequency H∞ index is used to describe disturbance attenuation performance. In light of the generalised Kalman–Yakubovich–Popov lemma for 2-D systems and matrix inequality techniques, convex conditions are derived for this fault detection problem. Based on these conditions, a numerical algorithm is put forward to construct a desired fault detection filter. Finally, a numerical example and an industrial example are given to illustrate the effectiveness of the proposed algorithm.

This paper investigates the leader-follower formation problem for a group of quadrotors. Finite-time control scheme and prescribed performance control method, which are regarded as two highlights, are introduced in this paper. First, a control scheme with prescribed performance is used to control the translational movements to ensure the quadrotors obtain a relative gentle transient process and an adjustable steady-state error bound. Then, the desired orientations for the rotation subsystem provided by translational movements part are stabilised by a fixed-time control law. Finally, by designing a finite-time formation controller, followers can track the desired position and heading angle in finite time, which is important for the practical application. Several simulation results are given to show the effectiveness of the designed control strategy.

This paper addresses a distributed consensus optimisation problem over networks with time-varying topologies based on Zero-Gradient-Sum ZGS algorithm. First, the exponential convergence of the algorithm is guaranteed under a new condition on network topologies, called cooperatively connected. This condition does not require the topology constantly connected or jointly connected but only requires the integral of the Laplacian matrix of the network topology over a period of time is connected. Hence, it is suitable for more general time-varying topologies. Second, by establishing a key mathematical lemma, we develop a convergence analysis technique which is based on the difference of the Lyapunov function rather than its differentiation. Finally, a simulation example is also provided to verify the results obtained in this paper.

This paper investigates the extended dissipative filter design problems for continuous-time fuzzy systems with time-varying delays under imperfect premise variables based on a unified performance index−extended dissipative. Attention is focused on solving the H∞, L2 − L∞, passive and dissipative filtering problems for fuzzy systems with time-varying delays under this unified framework. Based on the unified performance index, a new delay-dependent filter design approach in terms of linear matrix inequalities is obtained by employing Lyapunov–Krasovskii functional method together with a novel efficient integral inequality. The designed filter can guarantee the filtering error system satisfy the prescribed H∞, L2 − L∞, passive and dissipative performance by tuning the weighting matrices in the conditions. Moreover, in this paper, the fuzzy filter does not need to share the same membership function with fuzzy model, which can enhance design flexibility and robust property of the fuzzy filter system. Finally, two examples are provided to illustrate the effectiveness and significant improvement of the method proposed in this paper.

This paper is concerned with the finite-time mixed H∞ and passivity performance analysis and filter design for a class of uncertain nonlinear discrete-time Markovian jump systems MJSs described by Takagi–Sugeno fuzzy model with nonhomogeneous jump processes. In this paper, the proposed MJSs fuzzy model is formulated with norm-bounded parameter uncertainties and time-varying jump transition probability matrices. In particular, the time-varying transition probability matrices are expressed in respect of a polytope. By constructing a suitable Lyapunov functional, a new set of sufficient conditions is derived in the form of linear matrix inequalities LMIs to ensure that the filtering error system is robustly stochastically finite-time bounded and a prescribed mixed H∞ and passive performance index is achieved. Moreover, the robust mixed H∞ and passivity filter design gain matrices can be computed from the obtained LMIs. Furthermore, the developed results unify H∞ and passive filtering problems in a single framework. Finally, two numerical examples including an application-oriented example are provided to demonstrate the effectiveness of the proposed filter design technique.

This paper deals with simultaneous fault estimation and control for a class of nonlinear systems with parameter uncertainty, which is described by Takagi–Sugeno T–S fuzzy model with parameter uncertainties and unknown disturbance. In this paper, a fuzzy reference model is used to generate error dynamic for tracking control. By considering actuator fault as an auxiliary state vector, we construct an augmented error system and propose a fault estimator/controller to achieve simultaneous fault estimation and fault-tolerant tracking control. H∞ approach is used in the design of estimator/controller to attenuate the effect of the unknown disturbance and parameter uncertainties. The design conditions are formulated into a set of linear matrix inequalities LMIs, which can be efficiently solved. Finally, a pitch-axis nonlinear missile model is used to illustrate the effectiveness of the proposed method.

This paper investigates a predefined performance control problem for adaptive tracking of uncertain nonlinear time-delay systems in nonstrict-feedback form. Nonstrict-feedback nonlinearities, time-varying delays and external disturbances are assumed to be unknown. Based on the exponential decaying design functions denoting the preassigned bounds of transient and steady-state tracking errors, some variable separation lemmas are derived to design an approximation-based robust adaptive control scheme in the presence of nonstrict-feedback time-delayed nonlinearities. The proposed control system guarantees that a tracking error remains within a predesigned bound for all t ≥ 0 and converges to a preselected neighbourhood of the origin. Compared with the existing results in the literature, the main contribution of this paper is to provide a solution on the guaranteed performance control in the presence of unknown nonstrict-feedback nonlinearities related to all delayed state variables. Simulation results illustrate the effectiveness of the proposed methodology.

This paper develops a correlation-based method into the Youla parameterisation structure for a fault-tolerant controller design strategy. By tuning the Youla parameters with the proposed correlation-based algorithm, a number of conditional faults described by the dual Youla parameters are attenuated. The traditional fault-tolerant control FTC schemes under the Youla parameterisation often require the gradient information of the defined cost function for minimisation, which is either tedious or even unfeasible with unknown fault model. However, the proposed correlation-based FTC algorithm in this paper can compensate the faults via system data without the explicit fault model or the cost function gradient information. It is also proved that the algorithm convergence can be achieved without identifying the unknown fault model. For illustration, a simulation example with corresponding comparisons are presented to show the effectiveness of the proposed method in the end.

To save resource of communication, this paper investigates the model-based event-triggered control systems. Two main problems are considered in this paper. One is, for given plant and model, to design event conditions to guarantee the stability of the systems. The other is to consider the effect of the model matrices on the stability. The results show that the closed-loop systems can be asymptotically stabilised with any model matrices in compact sets if the parameters in the event conditions are within the designed ranges. Then, a separation property of model-based event-triggered control is proposed. Namely, the design of the controller gain and the event condition can be separated from the selection of the model matrices. Based on this property, an adaption mechanism is introduced to the model-based event-triggered control systems, which can further improve the sampling performance. Finally, a numerical example is given to show the efficiency and feasibility of the developed results.

This paper investigates robust sliding mode control SMC for discrete singular systems which include time-varying delays, parameter uncertainties and nonlinear perturbations. An appropriate discrete sliding surface function is constructed such that the corresponding sliding mode dynamics are gained. By using some free-weighting matrices, a linear matrix inequality constraint is established to make sure that the closed-loop system is regular, causal and stable. Furthermore, in consideration of the improved discrete reaching condition, an SMC law is synthesised for reaching motion and the chattering can be weakened, while few existing papers focus on how to employ it to study the SMC problem for the discrete singular systems with time-varying delays. At last, the designed law is tested through an example.

This paper deals with the problems of robust stability analysis and robust stabilisation control design for Boolean networks with disturbance inputs. The main tool used in this paper is the semi-tensor product of matrices. First, a necessary and sufficient condition is presented for the robust stability of Boolean networks based on the algebraic representation of Boolean networks with disturbance inputs. Second, the concept of one-step robust reachability is defined for Boolean control networks BCNs with disturbance inputs, based on which a constructive procedure is proposed for the robust stabilisation control design of BCNs. The study of an illustrative example demonstrates the effectiveness of the obtained new results.

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts’ estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts’ estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

The traditional use of global and centralised control methods fails for large, complex, noisy and highly connected systems, which typify many real-world industrial and commercial systems. This paper provides an efficient bottom-up design of distributed control in which many simple components communicate and cooperate to achieve a joint system goal. Each component acts individually so as to maximise personal utility whilst obtaining probabilistic information on the global system merely through local message-passing. This leads to an implied scalable and collective control strategy for complex dynamical systems, without the problems of global centralised control. Robustness is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, can be implemented adaptively and opens a systematic rich way to information sharing. This paper opens the foreseen direction and inspects the proposed design on a linearised version of coupled map lattice with spatio-temporal chaos. A version close to linear quadratic design gives an initial insight into possible behaviours of such networks.

This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series FS to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.

In this paper, the stability analysis of linear systems with an interval time-varying delay is investigated. First, augmented Lyapunov–Krasovskii functionals are constructed, which include more information of the delay's range and the delay's derivative. Second, two improved integral inequalities, which are less conservative than Jensen's integral inequalities, and delay-range-partition approach are utilised to estimate the upper bounds of the derivatives of the augmented Lyapunov–Krasovskii functionals. Then, less conservative stability criteria are proposed no matter whether the lower bound of delay is zero or not. Finally, to illustrate the effectiveness of the stability criteria proposed in this paper, two numerical examples are given and their results are compared with the existing results.

In this paper, a robust unknown input observer (UIO) for the joint state and fault estimation in discrete-time Takagi-Sugeno (TS) systems is presented. The proposed robust UIO, by applying the framework, leads to a less restrictive design procedure with respect to recent results found in the literature. The resulting design procedure aims at achieving a prescribed attenuation level with respect to the exogenous disturbances, while obtaining at the same time the convergence of the observer with a desired bound on the decay rate. An extension to the case of unmeasurable premise variables is also provided. Since the design conditions reduce to a set of linear matrix inequalities that can be solved efficiently using the available software, an evident advantage of the proposed approach is its simplicity. The final part of the paper presents an academic example and a real application to a multi-tank system, which exhibit clearly the performance and effectiveness of the proposed strategy., This work has been funded by the National Science Centre in Poland under the grant 2013/11/B/ST7/01110, by the Spanish Government (MINECO) through the project CICYT ECOCIS (ref. DPI2013-48243-C2-1-R), by MINECO and FEDER through the project CICYT HARCRICS (ref. DPI2014-58104-R), by AGAUR through the contracts FI-DGR 2014 (ref. 2014FI_B1 00172) and FIDGR 2015 (ref. 2015FI_B2 00171), and by the DGR of Generalitat de Catalunya (SAC group Ref. 2014/SGR/374).

In this paper, the division-by-zero DBO problem in the field of nonlinear control, which is traditionally termed the control singularity problem or specifically, controller singularity problem, is investigated by the Zhang dynamics ZD method and the Zhang-gradient ZG method. According to the impact of the DBO problem on the state variables of the controlled nonlinear system, the concepts of the pseudo-DBO problem and the true-DBO problem are proposed in this paper, which provide a new perspective for the researchers on the DBO problems as well as nonlinear control systems. Besides, the two classes of DBO problems are solved under the framework of the ZG method. Specific examples are shown and investigated in this paper to illustrate the two proposed concepts and the efficacy of the ZG method in conquering pseudo-DBO and true-DBO problems. The application of the ZG method to the tracking control of a two-wheeled mobile robot further substantiates the effectiveness of the ZG method. In addition, the ZG method is successfully applied to the tracking control of a pure-feedback nonlinear system.

This paper concerns the problem of H∞ filtering for piecewise homogeneous Markovian jump nonlinear systems. Different from the existing studies in the literatures, the existence of variations in transition rates for Markovian jump nonlinear systems is considered. The purpose of the paper is to design mode-dependent and mode-independent filters, such that the dynamics of the filtering errors are stochastic integral input-to-state stable with H∞ performance index. Using the linear matrix inequality method and the Lyapunov functional method, sufficient conditions for the solution to the H∞ filtering problem are derived. Finally, three examples are proposed to illustrate the effectiveness of the given theoretical results.

This paper deals with the problem of the fault detection FD for continuous-time singular switched linear systems with multiple time-varying delay. In this paper, the actuator fault is considered. Besides, the systems faults and unknown disturbances are assumed in known frequency domains. Some finite frequency performance indices are initially introduced to design the switched FD filters which ensure that the filtering augmented systems under switching signal with average dwell time are exponentially admissible and guarantee the fault input sensitivity and disturbance robustness. By developing generalised Kalman–Yakubovic–Popov lemma and using Parseval's theorem and Fourier transform, finite frequency delay-dependent sufficient conditions for the existence of such a filter which can guarantee the finite-frequency H− and H∞ performance are derived and formulated in terms of linear matrix inequalities. Four examples are provided to illustrate the effectiveness of the proposed finite frequency method.

In this paper, the consensus problem for leader-following linear multi-agent systems with external disturbances is investigated. Brownian motions are used to describe exogenous disturbances. A distributed tracking controller based on Riccati inequalities with an adaptive law for adjusting coupling weights between neighbouring agents is designed for leader-following multi-agent systems under fixed and switching topologies. In traditional distributed static controllers, the coupling weights depend on the communication graph. However, coupling weights associated with the feedback gain matrix in our method are updated by state errors between neighbouring agents. We further present the stability analysis of leader-following multi-agent systems with stochastic disturbances under switching topology. Most traditional literature requires the graph to be connected all the time, while the communication graph is only assumed to be jointly connected in this paper. The design technique is based on Riccati inequalities and algebraic graph theory. Finally, simulations are given to show the validity of our method.

A new nonlinear optimal and explicit guidance law is presented in this paper for launch vehicles propelled by solid motors. It can ensure very high terminal precision despite not having the exact knowledge of the thrust-time curve apriori. This was motivated from using it for a carrier launch vehicle in a hypersonic mission, which demands an extremely narrow terminal accuracy window for the launch vehicle for successful initiation of operation of the hypersonic vehicle. The proposed explicit guidance scheme, which computes the optimal guidance command online, ensures the required stringent final conditions with high precision at the injection point. A key feature of the proposed guidance law is an innovative extension of the recently developed model predictive static programming guidance with flexible final time. A penalty function approach is also followed to meet the input and output inequality constraints throughout the vehicle trajectory. In this paper, the guidance law has been successfully validated from nonlinear six degree-of-freedom simulation studies by designing an inner-loop autopilot as well, which enhances confidence of its usefulness significantly. In addition to excellent nominal results, the proposed guidance has been found to have good robustness for perturbed cases as well.

This paper addresses the robust reliable stabilisation problem for a class of uncertain switched systems with random delays and norm bounded uncertainties. The main aim of this paper is to obtain the reliable robust sampled-data control design which involves random time delay with an appropriate gain control matrix for achieving the robust exponential stabilisation for uncertain switched system against actuator failures. In particular, the involved delays are assumed to be randomly time-varying which obeys certain mutually uncorrelated Bernoulli distributed white noise sequences. By constructing an appropriate Lyapunov–Krasovskii functional LKF and employing an average-dwell time approach, a new set of criteria is derived for ensuring the robust exponential stability of the closed-loop switched system. More precisely, the Schur complement and Jensen's integral inequality are used in derivation of stabilisation criteria. By considering the relationship among the random time-varying delay and its lower and upper bounds, a new set of sufficient condition is established for the existence of reliable robust sampled-data control in terms of solution to linear matrix inequalities LMIs. Finally, an illustrative example based on the F-18 aircraft model is provided to show the effectiveness of the proposed design procedures.

Importance analysis is aimed at finding the contributions by the inputs to the uncertainty in a model output. For structural systems involving inputs with distribution parameter uncertainty, the contributions by the inputs to the output uncertainty are governed by both the variability and parameter uncertainty in their probability distributions. A natural and consistent way to arrive at importance analysis results in such cases would be a three-loop nested Monte Carlo MC sampling strategy, in which the parameters are sampled in the outer loop and the inputs are sampled in the inner nested double-loop. However, the computational effort of this procedure is often prohibitive for engineering problem. This paper, therefore, proposes a newly efficient algorithm for importance analysis of the inputs in the presence of parameter uncertainty. By introducing a ‘surrogate sampling probability density function SS-PDF’ and incorporating the single-loop MC theory into the computation, the proposed algorithm can reduce the original three-loop nested MC computation into a single-loop one in terms of model evaluation, which requires substantially less computational effort. Methods for choosing proper SS-PDF are also discussed in the paper. The efficiency and robustness of the proposed algorithm have been demonstrated by results of several examples.