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This paper proposes a switched high-gain observer with the desired convergence time for nonlinear systems. The proposed approach is based on a switching structure that plays an essential role, resulting in desired convergence and avoiding the observer's states' singularity. The main feature of this paper is that the observer's state converges to the actual state within the desired convergence time, where the convergence time is invariant with respect to the initial errors of the system. Moreover, the convergence time can be chosen at the will of the designer. The proposed method's results involve the high-gain principle and recent advancements in fixed-time observers. Further, the system's gain varies linearly (rather than square) with the order of the system. Using the Lyapunov theorem, the stability analysis of the proposed approach is investigated. Finally, two examples, (i) the Van der Pol oscillator circuit and (ii) Genesio–Tesi chaotic system demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we shall deal with stochastic singular difference equations (SSDEs) with delay. The aim of the paper is to study the solvability and stability of SSDEs with delay. It is difficult to investigate these properties for SSDEs with a delay because of the singularity of the leading coefficient matrix. An index-ν concept is derived for the solvability of these equations. Stability of SSDEs with delay is studied by using the method of Lyapunov functions and comparison principle. An example is given to illustrate the results. To the best of our knowledge, these results are novel for SSDEs with delay. [ABSTRACT FROM AUTHOR]

Most control systems require a fairly accurate model of dynamic system to design or implement a controller. When system dynamics change, the dynamic model must undergo online or offline re-estimation. The online model estimation algorithms in the time domain, especially for large models in presence of sensor noise, model uncertainty and external disturbance are almost inaccurate and unstable. In this paper, based on the dynamic model characteristics a novel online robust meta-heuristic adaptive Bi Conjugate Gradient Stabilized (Bi-CGSTAB) algorithm is proposed to estimate the model parameters and attitude simultaneously. First, the model is estimated iteratively using the output of attitude estimation from the Kalman filter algorithm, and the attitude is estimated by the output of estimated model from the least square method. The estimation method focuses on the solving algorithm of the matrix equations of the model estimation. The online robust meta-heuristic adaptive Bi-CGSTAB method uses the information of previous iteration in the current iteration to set the solving-steps toward the local optimums. This method leads to a broader and more intelligent search in the Krylov subspace of answers. The numerical results show a higher performance, robustness and more accurate model estimation than the other stated methods in the paper. 50 % [ABSTRACT FROM AUTHOR]

Interval Type-2 (IT2) Fuzzy-Model-Based (FMB) control design has emerged as a prominent research area within the fuzzy control field, showcasing its capability to directly address uncertainties present in nonlinear control systems. The objective of this special issue is to showcase recent research findings in the IT2 FMB systems and foster the advancement of research activities in the field of IT2 FMB control systems. Dr. H.K. Lam made a significant contribution to the field by successfully conducting stability analysis of FMB control systems under parameter uncertainty in 2008 (Lam & Seneviratne, [1]). [Extracted from the article]

This study investigates the application of the Bayesian filter method for identifying failure times in a 2D parabolic partial differential equation system. The identification of failure times in thermal systems, which are subject to partial differential equations, presents significant difficulties, especially due to their ill-posed nature, which makes them highly sensitive to measurement errors. A Bayesian inference framework was previously developed in a related study, aiming to solve inverse heat conduction problems by utilising temperature measurements from sensors to estimate failure times or potential restarts of fixed heat sources. This paper focuses on the case of mobile sources, where a set of fixed sensors is considered and the trajectories of the heating sources are known and follow a constant velocity. The main objective is to accurately identify the failing heat sources and determine the exact failure time, as well as the possibility of resuming normal operation. A Monte Carlo simulation is performed to assess the impact of sensor measurements. [ABSTRACT FROM AUTHOR]

This paper presents a formation control method for a swarm system, wherein each agent is capable of avoiding dynamic obstacles to reach a target area on the plane. Firstly, the collision hazard area, where an agent collides upon entry, is established by integrating a partial differential equation-based distribution model of dynamic obstacles and a curve model of the front agents' deployment. Secondly, the coverage-based controller is designed for each agent by optimising the objective function concerning its respective visibility-limited Voronoi set on the manoeuvrable domain outside the collision hazard area, ensuring that the agent's trajectories remain within the target area. Simulation results finally demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces two enhanced model order reduction techniques designed for scenarios involving frequency-weighted and frequency-limited-interval Gramians in the continuous-time domain. The primary objective is to address the instability issue identified in existing approaches in the continuous-time domain, as formulated by Enns for frequency-weighted scenarios and Gawronski & Juang for frequency-limited-interval scenarios. Despite numerous solutions proposed in the literature to mitigate this problem, a persistent challenge remains the high approximation error between the original and reduced-order systems. To overcome this limitation, the proposed improved techniques focus on ensuring stability in reduced-order models while simultaneously minimising the approximation error between the original and reduced systems. Furthermore, these enhanced techniques provide a computationally straightforward, a priori error bound formula. Numerical findings underscore the correctness and efficiency of the proposed techniques in reducing the approximation error while maintaining stability, thereby substantiating their efficacy. [ABSTRACT FROM AUTHOR]

In this paper, a forward-backward splitting based algorithmic framework incorporating the heavy ball strategy is proposed so as to efficiently solve the Wasserstein logistic regression problem. The proposed algorithmic framework consists two phases: the first phase involves a gradient descent step extension method, whilst the second phase involves a problem of instantaneous optimisation which balances the minimisation of a regularisation term while maintaining close proximity to the interim state given in the first phase. Then, it proves that the proposed algorithmic framework converges to the optimal solution of the Wasserstein logistic regression problem. Finally, numerical experiments are conducted, which illustrate the efficient implementation for high-dimensional sparsity data. The numerical results demonstrate that the proposed algorithmic framework outperforms not only the off-the-shelf solvers, but also some existing first-order algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the fuzzy adaptive finite-time formation tracking control problem for underactuated unmanned surface vehicles (USVs) subject to multiple constraints and unknown dead-zones. The model of USVs contains unknown nonlinear dynamics, so the fuzzy logic systems (FLSs) are employed to estimate them. The prescribed performance control (PPC) technique is used to construct the constraint requirements of collision avoidance and connectivity preservation in formation control. Then, based on the theory of finite-time stability and dynamic surface control (DSC) scheme, a fuzzy adaptive formation tracking control algorithm is presented, which can solve the problem of unknown dead zones for USVs and have the advantages of avoiding collision and preserving connectivity, and also ensures that all the signals are bounded in finite-time bounded. The simulation results can verify the feasibility of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed impulsive consensus problem for nonlinear multi-agent systems (MASs) subject to denial-of-service (DoS) attacks, where the information of agent states is not fully available in the impulsive control design. To reconstruct the states of MASs and guarantee secure average consensus, a novel observer-based impulsive secure control protocol is put forward, and some sufficient conditions are derived by means of linear matrix inequality (LMI) technique and Lyapunov method. It should be pointed out that both the proposed state observer and control protocol adjust the state value abruptly at some discrete instants in an impulsive manner. Moreover, the considered asynchronous DoS attack model can allow jam each channel independently, under which the decay rates of different attack modes are estimated. Finally, two simulation examples, including a practical control system, verify the effectiveness of the proposed consensus algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the linear quadratic Gaussian (LQG) control problem for wireless networked control systems (WNCSs) without packet acknowledgement. In WNCSs, packets may be randomly lost in both control and observation channels, and non-acknowledgement (non-ACK) mechanisms (e.g. UDP-like protocols) are generally adopted to achieve real-time control and reduce energy consumption. For general non-ACK systems, it is well known that the optimal controller is not yet available, and the performance of existing suboptimal controllers is poor because they either adopt a fixed control law or rely on a poorly performing filter. By using a moment-preserving Gaussian approximation approach, an efficient filter is proposed to approximately estimate system states. Based on the approximate state estimates of the proposed filter, an approximate optimal controller with a time-varying control gain for WNCSs is further designed by using a dynamic programming approach. The proposed controller has excellent performance and becomes the optimal controller under special system parameters. Numerical simulations show the correctness and effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, the problem of practical finite-time coordinated path following control of a multi-agent system in an uncertain Euler flow field is studied. The coordinated path following control system is decoupled into a path following sub-system and a coordinated formation sub-system. The uncertain Euler flow field is estimated by constructing an extended state observer. By embedding the extended state observer into the design of the finite-time controllers for both sub-systems, practical finite-time stability of the coordinated path following control system is proved. Simulation results are provided for validating the feasibility and effectiveness of the control method. [ABSTRACT FROM AUTHOR]

This paper aims to realise the robust output bipartite consensus for unknown heterogeneous linear time-varying multiagent systems (MASs) subject to varying trial lengths, unknown measurement disturbances and data quantisation. To this end, inspired by the idea of quantised control, a quantised data-driven adaptive iterative learning bipartite consensus (AILBC) method is proposed. Specifically, to address the problem of varying trial lengths, a distributed auxiliary output prediction system is constructed based on the agents' input-output (I/O) dynamic relationship. An adaptive update protocol is developed to estimate the measurement disturbances and unknown parameters of I/O dynamic relationship. Subsequently, a quantised distributed data-driven iterative learning control (ILC) approach based on the quantised output information is proposed for MASs to achieve robust bipartite consensus tracking, with an attempt to relax the need of explicit model information. The bipartite consensus tracking errors are ultimately bounded through rigorous analysis, and this result is further extended to switching topologies. Finally, numerical simulations are conducted to verify the validity of the AILBC method. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive prescribed finite-time asymptotic tracking control problem is considered for the unknown nonlinear switched systems with unknown initial conditions and full-state constraints. A class of nonlinear mappings (NMs) and a new prescribed finite-time performance function (PFTPF) are introduced so that the control design is independent of initial conditions of the controlled states. Based on the neural network approximation approach, NMs, PFTPF and the Barbalat's lemma, an adaptive prescribed finite-time asymptotic tracking controller with full-state constraints is obtained. To avoid overlarge initial control input, the design method with zero initial control input is adopted, the definition of input tuning function (ITF) is expanded and its effectiveness is proved theoretically. As results, the full-state constraints and the boundedness of all the signals in the closed-loop system are guaranteed, and the tracking error of the system can converge to zero asymptotically. Finally, the effectiveness and superiority of the proposed scheme are verified by the simulation results. [ABSTRACT FROM AUTHOR]

In this paper, the problem of adaptive neural network (NN) reinforcement learning (RL) tracking control is investigated for the continuous time (CT) switched stochastic nonlinear systems with unknown control coefficients and full-state constraints. First, a set of reconstructed states are defined to handle the unknown control coefficients, and switched state observers are developed to estimate unmeasurable reconstructed states. Then, to improve the tracking performance, based on the minimal learning parameter (MLP) method and the RL control design technique, the adaptive RL controller is developed by the backstepping method. Finally, the boundedness of the tracking error and all signals is demonstrated via the average dwell time (ADT) method and tangent type time-varying barrier multiple Lyapunov functions. The effectiveness of the proposed scheme is verified by two examples. [ABSTRACT FROM AUTHOR]

The variable-order fractional (VOF) chaotic systems offer a promising solution for applications in secure communication due to their unique properties. This paper addresses the synchronisation problem in secure communication for these systems, which have uncertainties and external disturbances with unknown bounds. According to the variable-order fractional type Mittag-Leffler stability theorem, a fractional-order derivative is applied to a sliding surface, and suitable adaptive laws are devised to address uncertainties and disturbances. A variable-order fractional control strategy and a new criterion are developed to ensure the synchronisation error systems achieve asymptotic stability in finite time, for which the upper limit can be obtained. Simulation outcomes demonstrate the efficacy of the proposed synchronisation strategy in secure communication scenarios. [ABSTRACT FROM AUTHOR]

Firstly, with the rapid popularisation of the ESG concept and the deepening of the conception of green and low-carbon development, the performance of a firm in protecting and improving the ecological environment and the level of green technology investment has gradually become the reference criteria for judging whether the firm has social value and sustainability in development. Secondly, to pursue short-term survival and long-term development, the firm will also vigorously strive for short-term profit creation and long-term capital accumulation. Thirdly, due to the existence of information asymmetry, the principal-agent parties in a firm may generate agency conflicts in the pursuit of maximising their own interests, which may lead to losses or even bankruptcy. Therefore, making rational short-term, long-term, and green technology investment decisions that consider both characteristics of firm and agency conflicts is essential for the survival and sustainable development of the firm. From the perspective of contract theory, this paper considers the design of an optimal dynamic financial contract that considers the environment's improvement and the achievement of the firm's long- and short-term financial performance while satisfying incentive compatibility. On this basis, we explore how the optimal investment strategies vary with firm characteristics. The results of the study suggest that the decisions of green technology investment, short-term investment, and long-term capital investment should be appropriately adjusted according to the level of financial slack and the effect of different market shocks to facilitate the achievement of the dual objectives of long- and short-term profitability and environmental improvement. [ABSTRACT FROM AUTHOR]

The problem of $ L_1 $ L 1 output tracking control of positive linear systems in finite time is addressed in this paper. By constructing a linear copositive Lyapunov–Krasovskii functional, some sufficient conditions are achieved to ensure that the states are finite-time bounded and the measurement outputs have $ L_1- $ L 1 − gain performance for given systems, respectively. Next, a state feedback controller is designed to ensure that the output signals of the tracking system can effectively trace the specified signals of reference model with finite-time boundedness and $ L_1 $ L 1 -gain performance. Finally, an example is given to illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the $ H_{\infty } $ H ∞ resilient and event-triggered control of singular Markov jump systems against deception attacks. The deception attacks are modelled as a random bounded signal which is governed by a Bernoulli distributed random variable. The event-triggered scheme is adopted to achieve a trade-off between system performance and network resources. Based on the technique of stochastic Lyapunov–Krasovskii functionals and linear matrix inequalities, efficient criteria are developed such that the closed-loop system is stochastically admissible with a certain $ H_{\infty } $ H ∞ performance under deception attacks. Then, the co-design of resilient controller gains and event-triggered rules is provided in terms of a group of feasible LMIs. Finally, two examples are employed to verify the validity of our design. [ABSTRACT FROM AUTHOR]

This paper examines the boundary stabilisation results for time fractional-order reaction-diffusion systems involving with time-varying delays. The main goal is to design the boundary control for the system by proving the well-posedness of the kernel function using the backstepping method. An invertible Volterra integral transformation is used to convert the considered system into a stable target system. Different from the existing results, the stability results for fractional RDEs are discussed in the sense of the Lyapunov–Krasovskii theory and sufficient conditions are derived with the help of the linear matrix inequality (LMI) approach. Finally, to show the application of the results, the proposed conditions are numerically validated over a time fractional-order reaction-diffusion cellular neural network model. [ABSTRACT FROM AUTHOR]

This paper delves into the fuzzy adaptive event-triggered tracking control for a certain class of strictly uncertain nonlinear systems subject to partial state constraints as well as output dead-zones. First of all, fuzzy logic systems (FLSs) are utilised to approximate the unpredictable items, a fuzzy adaptive observer is intended to enable state estimation. Subsequently, a type of smooth approximate model is introduced to settle the nondifferentiable phenomenon of output dead-zones. To reduce the computational overhead of the communication operation effectively, an improved event-triggered control strategy including a decreasing hyperbolic tangent function related to dead-zone, is applied to determine the actual control input. In addition, the barrier Lyapunov function (BLF) is fabricated to address the issue of partial state constraint. By theoretical analysis, the reference signal can be precisely tracked by the system output and all closed-loop signals remain uniformly ultimately bounded (UUB). Finally, the constructed controller's effectiveness is validated with two illustrative examples. [ABSTRACT FROM AUTHOR]

The quotient space method provides a novel idea for modelling the transient dynamics of dimension-varying control systems. This paper investigates the realisation problem of the transient dynamics of dimension-varying control systems. By revealing the structure of the controllable subspace for the linear system on quotient space, we propose the condition for the realisation of the transient dynamics of dimension-varying control systems, based on which a new scheme for modelling the transient dynamics is given. Moreover, the proposed scheme justifies the existing result for the strategy for modelling the transient dynamics. A numeric example is given to illustrate our theoretical results. [ABSTRACT FROM AUTHOR]

Control and synchronisation of chaotic systems have received much attention due to their various potential applications. In this paper, the predefined-time master–slave synchronisation of two chaotic systems is investigated. With the help of two different predefined-time stability theorems, two controllers are designed to achieve the predefined-time synchronisation of two nonidentical chaotic systems. Then based on inequality techniques and a Lyapunov function, some new sufficient criteria are obtained to ensure that two nonidentical chaotic systems achieve predefined-time synchronisation. Finally, numerical examples are given to show the effectiveness of our new results. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of octonion-valued stochastic recurrent neural networks with time-varying delays whose state variables, self-feedback coefficients, connection weights and external inputs of the networks are all octonion-valued functions. Based on Banach fixed point theorem and inequality technique, we obtain the existence, uniqueness and global exponential stability of almost automorphic solutions in distribution of this kind of neural networks. Our results are new. Finally, an example is given to illustrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This paper studies model predictive control (MPC) with control barrier function to solve the stability and safety problems of switched systems subject to input and state constraints. Considering that the system state is unmeasurable and disturbed, an observer-based MPC $ H_\infty $ H ∞ method is adopted to estimate the system state and suppress disturbances. A cost function is designed to optimise the state and control input of switched systems by minimising its upper bound, and the optimisation problem is further transformed into a feasibility problem. Moreover, a control barrier function is introduced to constrain the controller gains, which ensures that the initial state of switched systems converges to the steady state without entering the specified unsafe region. Furthermore, a set of matrix inequality conditions is given to express the input and state constraints. Then, the Lyapunov function and average dwell time (ADT) approach are used to obtain sufficient conditions for the asymptotic stability of switched systems. Finally, two simulation examples are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses on finite-time robust adaptive simultaneous stabilisation for nonlinear time-delay systems, taking into account actuator saturation and unknown parameters. Firstly, by employing adaptive control strategies and finite-time stability theory, a finite-time adaptive simultaneous stabilisation scheme for the nonlinear time-delay systems is developed. Secondly, utilising the orthogonal decomposition method, a finite-time robust adaptive simultaneous stabilisation scheme for nonlinear time-delay systems with external disturbances is proposed. Finally, simulations demonstrate the effectiveness of the provided control scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered $ \mathcal {L}_2-\mathcal {L}_\infty $ L 2 − L ∞ control problem for discrete-time Markov jump systems with DoS attacks and exogenous disturbance. Instead of one common event-triggering scheme, multiple triggering schemes are introduced to sample system signals and the event-triggered Markov closed-loop jump systems are further obtained under the full state feedback controller. Generally, mode information of subsystems is not completely available for the modules of trigger and controller such that asynchronous jump behaviour may occur among the modes of subsystems, triggering scheme and controller. Thus, a hidden Markov model (HMM) is developed to describe such asynchronous behaviour. Meanwhile, security issues of networks are widely studied in recent many literature. For discrete-time Markov jump systems, few works consider cyber attacks, especially periodic Denial-of-Service (DoS) attacks. Based on the HMM, the DoS attacks are taken into account and a set of sufficient conditions are further presented to guarantee the $ \mathcal {L}_2-\mathcal {L}_\infty $ L 2 − L ∞ control performance of the resulting Markov closed-loop jump systems. Subsequently, the design of controller and event-triggering parameters are provided by using matrix inequality method. Finally, an example is given to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper deals with the leader-following consensus problem of Lur'e multi-agent systems (MASs) under undirected topologies, in which the leader has an unknown input. First, based only on the relative outputs among neighbouring agents, a distributed unknown input observer (UIO) is introduced to estimate the relative state of each follower, and the observation errors can exponentially converge to zero. Then, we propose an observer-based discontinuous controller with adaptive gains and demonstrate that the tracking errors can converge to zero. Subsequently, we further propose an observer-based continuous controller with adaptive gains under which chattering phenomenon can be avoided. Unlike the related works, both controllers can achieve leader-following consensus in a fully distributed manner, meaning that they do not depend on the global communication topology structure and the upper bound of the leader's input. Furthermore, two tractable algorithms are devised for computing the gain matrices. Finally, some simulation examples are presented on Chua's circuit system to validate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of H ∞ filter and event-triggered scheme for a class of continuous non-linear networked control systems (NCSs) based on fuzzy system appeared with time delays. First, we consider the discrete event-triggered (ET) scheme to make efficient utilisation of bandwidth. Under this ET-scheme, sensor releases the data only when our sampled-data plant violates the specific event-triggered condition. Second, the T-S fuzzy system is used to model the non-linear NCSs. Another purpose of this paper is to design filters involving delays. Such filters have a more general form than the delay-free filters that have been mostly considered in the traditional studies. By using the time-delay system, co-design of event-triggered scheme and H ∞ filter for the delayed NCSs is presented in a unified frame work. To choose the latest data packet and discard the dis-ordered packet logic, zero-order hold is inserted between the fuzzy filter and event generator. Then, by using the novel fuzzy Lyapunov-Krasovskii function approach with free-weighting matrix technique, H ∞ filter design of event-triggered delay NCSs is proposed. Finally, to show the effective result of our co-design method, a tunnel-diode example is given. [ABSTRACT FROM AUTHOR]

From the perspective of large system, a directed complex dynamic network (DCDN) is regarded as being made up of the nodes subsystem (NS) and the links subsystem (LS), which are coupled to each other. Different from previous studies, which propose the dynamic model of LS with the matrix differential equations, this paper describes the dynamic behaviour of LS with the outgoing links vector at every node, by which the dynamic model of LS can be represented as the vector differential equation to form the outgoing links subsystem (OLS). Due to the fact that vectors have more flexible mathematical properties than matrices, this paper proposes the more convenient mathematic method to investigate the double tracking control problem of NS and OLS. Under the condition that the states of NS are available and the states of OLS are unavailable, the asymptotical state observer of OLS is designed, by which the tracking controllers of NS and OLS are synthesised to ensure achieving the double tracking goals. Finally, an example simulation for supporting the theoretical results is also provided. [ABSTRACT FROM AUTHOR]

This paper presents a Hamiltonian approach to study the event-triggered stabilisation problem of switched affine nonlinear systems with actuator saturation. The key idea is to obtain an event-triggered control strategy by developing a constant Hamiltonian realisation method, an average dwell time (ADT) scheme and a nonlinear sector method. The proposed strategy can ensure the asymptotic stability of the plant without violating actuator saturation, even if there exists asynchronous phenomenon between the subsystems and their candidate controllers. The results presented in this paper remove the common assumption that the system's mode and controller's mode have to be synchronised and that the triggering condition has to be monitored continuously. This extends available results for switched nonlinear systems and reduces energy consumptions. Finally, an example is provided to verify the validity of the proposed results. [ABSTRACT FROM AUTHOR]

For open-loop stable processes, the internal model control (IMC) provides a simple and effective parameterisation of stabilising controllers. However, for unstable processes, it cannot be directly used for control system implementation. In this paper, a new unified approach for the IMC design is proposed for stable, unstable and integrating processes, using the modified IMC (m-IMC) structure in which an additional controller K (s) is introduced along with the basic IMC. Also, the partial IMC (PIMC) concept is utilised, wherein we write the plant as the summation of stable and unstable parts and consider only the stable part as the internal model. The combined approach is thereby referred to as modified partial IMC (m-PIMC). This paper presents a graphical method to obtain controller parameters for the proposed m-PIMC to satisfy the given gain margin ( A m ) and phase margin ( ϕ m ) specifications. Further, stability boundary and robust boundary for the controller parameters are obtained which respectively give a set of all controller parameters stabilising the close loop and a set of all controller parameters ensuring robust stability. Seven different well-known standard stable, unstable and integrating process models are considered for the proposed m-PIMC design, and simulation examples are given for four selected plant models. [ABSTRACT FROM AUTHOR]

This paper extends the finite-time input-to-state stability (FTISS) issue to the discrete-time dynamical systems. Based on the Lyapunov method, sufficient conditions are derived to check the FTISS property of the discrete-time nonlinear systems with inputs. In addition, the generalised class- K L functions and the settling-time function capturing the finite settling-time behaviour of the dynamical system are constructed explicitly and well studied. In the end of the paper, a numerical example is provided to illustrate the feasibility/effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper develops a real-time identification algorithm for structures of network systems. We have already proposed a structure identification method for nonlinear network systems with nonlinear couplings by combining Koopman operator theory and a sparse identification method. However, the proposed method is only applicable for offline use under the assumption that the state of an isolated system is available. To relax this assumption, this paper shows that the dynamical model of isolated systems can be extracted from measured data of interconnected systems under some weak restrictions on network structures. We also modify the identification algorithm to obtain real-time identification results with fewer calculation resources and analyse the convergence of the proposed methods. Numerical examples demonstrate that the proposed method can identify network structures in real-time and detect changes in networks correctly. [ABSTRACT FROM AUTHOR]

This paper presents the design of a robust Restricted-Model-Based control providing tools for both stability and performance analysis of a class of Single-Input-Single-Output (SISO) Takagi–Sugeno (T–S) systems. The proposed method is evaluated in terms of induced L 2 -gain (or so-called H ∞ norm) to be robust to disturbances, sensor noises and uncertainties on the premise variables. Unlike the common approaches reported in the literature that consider exact premise variables, this work deals with the problem of unmeasured premises. The main results of this paper illustrate that stability and performance conditions can be evaluated by examining the feasibility of parametrised sets of linear matrix inequalities (LMIs). The proof of stability is based on the non-linear sector approach of the closed loop under Lyapunov conditions, L 2 norm and system transformations. The result is a control structure with only one parameter tuned via simple conditions. The performance and applicability of the proposed approach are illustrated through numerical simulations of an academic example. [ABSTRACT FROM AUTHOR]

This paper will investigate the prescribed-time stabilisation problem for a class of nonlinear systems with state constraints. A recursive design algorithm is proposed to solve the prescribed-time stabilisation problem. Firstly, a barrier Lyapunov function (BLF) is employed and a time-varying coordinate transformation is utilised. Then, a modified method of L g V -backstepping is proposed, and a novel stabilising function by adding a fractional term proposed in this paper which is capable of decreasing the BLF to the origin within any desired settling time and then achieving the prescribed-time stabilisation. Such a fractional term plays an important role in achieving prescribed-time stabilisation. Next, the analysis of the prescribed-time stabilisation with state constraints is presented. Finally, an example shows the effectiveness of the proposed method. The feature of this paper is that the settling time is not only independent of the design parameters, nor does it depend on the initial conditions, and can be set according to per our will. Other is that the constraints of all the states are not violated. [ABSTRACT FROM AUTHOR]

This paper addresses the H ∞ filtering for discrete-time Markovian jump singular systems (MJSSs). While the existing researches have only given the sufficient conditions, in this paper a sufficient and necessary condition is established to guarantee that the filtering error system is regular, causal, stochastically stable and H ∞ performance in light of supermartingale-liked approach and property of conditional expectation as well as – bounded real lemma. For obtaining the filter, an equivalent matrix inequality to the obtained one in nonlinear form is provided in terms of congruence transformation. Thus the desired filter can be developed by choosing matrix variables with certain structures. Finally, two simulations are provided to show the effectiveness of the proposed techniques in this paper. [ABSTRACT FROM AUTHOR]

The finite frequency domain H ∞ consensus control problem of neutral multi-agent systems with the input delay is investigated in this paper for the first time. According to the linear transformation, the problem of H ∞ consensus is converted into a set of H ∞ stability problems. Through the Lyapunov theory, combined with the time-delay interval decomposition method, a less conservative controller design method for the state feedback controller is presented. And the obtained controller can ensure closed-loop multi-agent systems to fulfil the consensus. Utilizing the generalised Kalman-Yakubovich-Popov (GKYP) lemma, the sufficient condition is derived for multi-agent systems achieving the H ∞ performance in the finite frequency domain (FFD). Then, via the orthogonal spatial information of the input matrix, the conservativeness of obtained design approach is further declined. Moreover, the design method with less conservativeness is proposed for the dynamic output feedback controller. Finally, a numerical case and a multi-pendulum system controlled by the multi-motor system are presented in this paper to demonstrate the availability and applicability of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper studies the input-to-state stability (ISS) and the finite-time input-to-state stability issues for the discrete-time stochastic switched nonlinear systems with time-varying delay. Based on the improved vector-version comparison principle and by employing the stochastic analysis method, sufficient conditions are first derived to guarantee the discrete-time stochastic switched system to be finite-time input-to-state stable in mean and finite-time stochastic input-to-state stable. Then, the comparison principle is further utilised to establish the p-th moment ISS result. As an application, the mean-square ISS is analysed for the linear discrete-time stochastic switched system. At the end of the paper, two numerical examples are provided to illustrate the feasibility/effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive tracking control problem of uncertain large-scale nonlinear time-delay systems with input saturation. Unknown constants and continuous functions with respect to output or delayed output are allowed in the nonlinear interconnected functions. The dynamic gain control technique is used to study the tracking control problem of considered system, which simplifies design process and form of controller. By introducing a full-order state observer and three dynamic equations, output feedback tracking control scheme is constructed to ensure that all signals of the closed-loop system are globally uniformly ultimately bounded (GUUB). In addition, the tracking error can converge to a small neighbourhood around the origin by selecting appropriate parameters. Finally, a practical example is presented to illustrate the feasibility of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the problem of designing an incentive strategy for a leader–follower dynamic game affected by some sort of uncertainties. As is traditionally understood in the standard theory of incentives, the leader has complete knowledge of the game parameters, including the follower's performance index. So then he can compute the strategy that will lead the game to the global optimum that is favourable for him. Most of the current work is devoted to this situation. Nevertheless, such an assumption is unrealistic. This paper proposes an incentive scheme in which the game's dynamic depends on an unknown value that belongs to a finite set. The solution of the incentive strategy is computed in terms of the worst-case scenario, of the team's optimal solution. Based on the Robust Maximum Principle, the new incentive is presented in the form of a mini-max feedback control. Two numerical examples illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider the specified-time platoon control for the networked vehicles modelled by the third-order dynamics with uncertain external bounded disturbance. The vehicles are subjected to disturbance and parameter uncertainties. To ensure the platoon can converge within a predefined time, a novel disturbance observer with a time-varying dynamic gain is proposed. By the time transformation approach, the disturbance observer is proved to be uniformly ultimate bounded (UUB) stable in a specified time. The time-varying dynamic gain is also applied to design the novel specified-time controller for the vehicle platoon and rigorous proof is presented based on Lyapunov stability. Under the proposed disturbance observer and controller, the vehicle platoon can be stabilised within the predefined time without depending on any system parameters and initial conditions. Finally, some comparison simulation results are given to verify the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

Nowadays, scheduling problem in the field of production planning has been considered by many researchers and this issue is used in various industries. Using the same parallel machines reduces the queue of WIP behind the machine and also increases production. Since scheduling problems in large scale are NP-hard, to solve them, metaheuristic algorithms are used. In this paper, Ant colony optimisation and Harmony Search algorithm are used to solve the problem as well as comparison with two other algorithms and finally, their performance is analysed and the efficient algorithm is investigated. [ABSTRACT FROM AUTHOR]

This paper deals with the robust observer-based synchronisation problem for a class of complex dynamic networks subject to multi-weights, time-varying coupling delay and external disturbance. Specifically, the gain fluctuation appears in the proposed controller and is represented in terms of a random variable obeying the Bernoulli distribution. The main objective of this paper is to design a non-fragile memory state feedback controller such that the resulting error system is stochastically synchronised under a prescribed performance level . First, a Luenberger-type state observer is constructed to estimate the state variables of the addressed system. Second, by constructing an appropriate Lyapunov–Krasovskii functional and using Wirtinger-based integral inequality techniques, the required set of sufficient conditions for the synchronisation of the proposed system is established in the form of linear matrix inequalities. In the end, two numerical examples are provided to demonstrate the validity and feasibility of the developed theoretical results. [ABSTRACT FROM AUTHOR]

This paper deals with the state feedback control for linear systems in the presence of state delay using a direct numerical design methodology. The design algorithm iteratively searches the solution space of controller parameters to find a proper controller gain that guarantees the delay-independent stability of the closed-loop system. The algorithm divides the solution space to some simplexes, selects one simplex and evaluates two checking methods on this simplex. The first method checks the stabilisability of the corner points (to establish the feasible point) and the other detects the total infeasibility (total de-stabilisability) of the simplex (to omit the undesired parts). One interesting property of the method is its capability in detecting total de-stabilisability of a simplex through its corner points. If none of these methods is successful, the simplex is divided into two smaller ones which will be checked in the next algorithm's iterations. According to the direct search nature, the algorithm is non-conservative and assuredly reaches a stabilizable point in the feasible space of the design space. The proposed algorithm and previous methods are evaluated on a set of random generated systems to compare the feasibility of the methods. Simulation results reveal the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper discusses the output-feedback-based model-free optimal output tracking control problem of discrete-time systems with completely unknown system models under mild assumptions. The only information that allows utilisation is the system output and the reference output. To overcome these challenges, the paper aims to solve a model-free optimal output regulation problem for achieving optimal output tracking control; solving an optimal output regulation problem is equivalent to solving a linear quadratic regulation (LQR) problem and a constrained static optimisation problem. The state reconstruction is first given to represent the system state in terms of the input and output sequences. A data-driven value iteration (VI) algorithm is then proposed to iteratively approximate the solution to the discrete-time algebraic Riccati equation (ARE) of the corresponding LQR problem on the basis of input and output data. Next, based on the iterative solutions with respect to the ARE, a model-free solution is provided for the corresponding constrained static optimisation problem. Finally, an alternative reference system equivalent to the original reference system is established to avoid the requirement of having the knowledge of the reference system dynamics and the reference state. A numerical example is employed to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The indefinite linear quadratic (ILQ) optimal control problem has many important applications in financial, economic systems, etc., which has been widely researched in stochastic systems and descriptor systems, etc. However, for ILQ problem of rectangular descriptor Markov jump systems (DMJSs), because there are impulses simultaneously in descriptor subsystems and at the switching time, it is theoretically difficult to research and there is no result yet. This paper discusses the ILQ optimal control problem for continuous-time linear rectangular DMJSs. Firstly, under some rank conditions and inequality conditions, the ILQ problem for rectangular DMJSs can be equivalently transformed into standard LQ problem for Markov jump systems (MJSs) by using elementary linear algebra method. Then based on the LQ theory of MJSs, the solvable sufficient condition of the ILQ problem for rectangular DMJSs and the non-negative optimal cost value are obtained. The optimal control can be synthesised as state feedback, and the resulting optimal closed-loop system has the stochastically stable solution. In addition, with some rank inequality assumptions, the differential subsystem of the resulting optimal closed-loop system can be ensured to have a unique solution. Finally, two numerical examples are provided to illustrate the effectiveness of the methods proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the problem of circle formation of multi-agent system in a plane is investigated. Inspired by the idea of decouple design, we decompose the overall control objective into two sub-goals, the first one is circle forming that agents need to move to a circle with a desired radius and centred on a predefined target, and the second one is space adjustment that each agent form and maintain a desired relative position with its neighbours. Since the completion of the second objective requires information from neighbouring agents, a distributed event-triggered controller is proposed to save communication resources and reduce the number of controller updates, which consists of two parts corresponding to two sub-goals. Furthermore, it is proved that there exists no Zeno behaviour under the proposed control laws. Finally, a numerical simulation verifies the effectiveness of the distributed event-triggered circle formation controller proposed in this paper. [ABSTRACT FROM AUTHOR]

A new methodology is proposed in this paper to solve multiobjective optimisation problems (MOPs) by invoking genetic algorithms and solutions concepts of cooperative games. The weighting problems that are formulated by assigning some suitable weights to the multiple objective functions are usually solved to obtain the Pareto optimal solutions of MOPs. In this paper, the suitable weights will be taken from the core values of a cooperative game that is formulated from the original MOP by regarding the objective functions as players. Under these settings, we shall obtain a large set of all so-called Core-Pareto optimal solutions. In order to choose the best Core-Pareto optimal solution from this set, we shall invoke the genetic algorithms by setting a reasonable fitness function. [ABSTRACT FROM AUTHOR]