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This paper makes further generalisations based on the application of edge-based state decomposition methods on average consensus. The edge-based state decomposition method is a privacy-preserving technique that leverages the number of neighbours to decompose node information, effectively safeguarding the privacy of nodes in average consensus, even in the absence of reliable neighbours. Building upon this method, our research expands its applicability to bipartite consensus and scale consensus. In bipartite consensus, the method can also accurately achieve consensus while guaranteeing privacy. In contrast, scale consensus effectively protects the privacy of the final consistency result. In addition, this paper refines the privacy proofs for different enemies. Finally, the simulation results presented at the end of the paper validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time adaptive event-triggered control problem for a class of nonlinear multiagent systems with full-state constraints. The problem of full-state constraints is solved for multiagent systems by utilising a one-to-one nonlinear mapping. The design conditions of the controllers are more easily satisfied than the existing barrier Lyapunov functions strategy. A fixed-time adaptive control scheme is proposed for unconstrained systems that are converted from constrained systems. In contract with the existing finite-time control schemes that rely on the initial states, this restriction condition can be removed in this paper. An improved event-triggered mechanism with an adaptable switching threshold is designed to provide flexible switching between fixed threshold and variable threshold policies, resulting in reduced execution and sampling. It is proved via the Lyapunov stability method that all signals of the closed-loop systems are semi-global practical fixed-time stable. Finally, the effectiveness of the proposed control strategy is verified by some simulation results. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of robust output feedback control tracking for a class of nonlinear dual-rate sample-data systems subject to time-varying disturbances. These dual-rate systems, known as slow measurement fast control (SMFC) systems, are characterised by a slow sampling rate of the sensor and a fast updating rate of the controller. To facilitate high-frequency signal tracking, this paper introduces an alternating predictive generalised proportional integral observer (APGPIO). The proposed control method not only effectively restores the system state and captures disturbance changes between samples but also enhances the control bandwidth and anti-disturbance ability, enabling effective tracking of high-frequency reference outputs. By combining it with a fast updating sample-data feedback controller, APGPIO utilises predictive methods to increase the sampling rate of the system's sensors, matching the update frequency of the controller. Stability analysis demonstrates that the tracking error of this hybrid nonlinear system converges to a bounded region centred at the origin. Finally, the designed method's high control bandwidth and anti-disturbance ability are demonstrated through multiple sets of simulations using a real system. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive finite-time fault-tolerant control (FTC) problem is investigated for uncertain nonlinear strict-feedback systems. The strict-feedback nonlinear system considered in this paper contains unknown nonlinear dynamics and actuator faults (both lock-in-place and loss of effectiveness). In this control process, adaptive laws are used to approximate unknown nonlinear functions. By using the sliding mode control technique and combining it with the adaptive backstepping control design, an adaptive terminal sliding mode FTC scheme is proposed. It is proved that the closed-loop system is finite-time stable and that the system output can track a given reference signal. Simulation results further show the efficacy of the designed control approach. [ABSTRACT FROM AUTHOR]

The sparrow search algorithm (SSA) is an efficient swarm-intelligence-based algorithm that has made some significant advances since its introduction in 2020. A detailed overview of the basic SSA and several SSA-based variants is presented in this paper. To be specific, first, the principle of the basic SSA is introduced including its search mechanism and implementation process. Second, many improved SSAs are reviewed including the hybrid, chaotic, adaptive, binary and multi-objective SSAs. In addition, the applications of the SSAs are presented in some real scenarios such as the machine learning areas, energy systems, path planning and image processing. Finally, further research directions of the SSAs are discussed. This survey paper aims to provide a timely review on the latest developments of the SSAs. [ABSTRACT FROM AUTHOR]

Fuzzy control has become one of the most effective tools for dealing with complex engineering processes. Over the years, research on fuzzy control systems has continuously evolved, witnessing numerous theoretical contributions and successful real-world achievements. The concept of model-free or data-driven fuzzy control was initially introduced with specific heuristics incorporated into the design. Due to the lack of a systematic framework for stability analysis in model-free fuzzy control, the significance of model-based fuzzy control has grown extensively. This approach ensures systematic design based on precise fuzzy models of the process. This survey focuses on the fundamental aspects of three prominent classes of fuzzy control. First, the paper commences with a review of Takagi–Sugeno fuzzy control systems. This includes discussions on stability analysis and controller design, exploring techniques to derive less conservative and/or complex results from a numerical burden perspective. Second, various aspects of data-driven fuzzy control are analysed in detail including a classification of the most popular data-driven control techniques and their combination with fuzzy control; a representative Iterative Feedback Tuning-based fuzzy controller is described. Third, this survey explores the fundamental aspects of evolving fuzzy control, with a particular emphasis on the significance of stability and control laws, which are not usually the primary focus of evolving intelligent systems research. For each discussed class of fuzzy control, the paper provides a selective list of mechatronics applications to illustrate their performance effectiveness, emphasising research papers published after 2011. Finally, drawing from recent advances in fuzzy control theory and mechatronics applications, future research directions and associated challenges are discussed. [ABSTRACT FROM AUTHOR]

The issue of prescribed time adaptive fuzzy output-feedback event-triggered control strategy subject to bounded disturbances is considered in this paper. For the unmeasured states, an adaptive fuzzy observer is designed to estimate them. Then, a novel event-triggering condition involving a decreasing function containing the error between the system states is designed by synthesising the backstepping design framework and command filtered technique. The developed prescribed finite time control approach ensures that all signals of the closed-loop system are bounded and the tracking error remains within the predefined bound in the prescribed time. Meanwhile, Zeno phenomenon can be avoided. Finally, a mass-spring-damper system is taken as an example for simulation. [ABSTRACT FROM AUTHOR]

In this paper, a class of second-order neutral impulsive stochastic evolution equations with infinite delay driven by fractional Brownian motion and Poisson jumps is considered. We utilise the Carathéodory approximation technique, the Burkhölder–Davies–Gundy inequality and the Gronwall inequality to present the existence and uniqueness of mild solutions for the addressed system under Carathéodory conditions. Furthermore, we derive an averaging principle for the proposed system which is approximated by the simplified equation in the sense of mean square. Finally, an application is used to illustrate the obtained theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the framework of dissipativity-based iterative learning control for singular systems (SSs) is established for the first time. Firstly, under the P-type learning algorithm, it has been demonstrated that the dissipative SSs can achieve the accurate tracking for the desired trajectory in a finite time interval. Secondly, a sufficient condition for the SSs to satisfy dissipativity is given by using the linear matrix inequality (LMI) method, and the solvability criterion of the LMI is further presented. Moreover, the relationship between dissipativity and positive realness is revealed. Finally, two examples are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method to the state bounding problem for a class of coupled differential-difference equations (CDDEs) with bounded disturbances and time-varying delays. Instead of using the state transformation to reformulate the considered problem into the one for non-perturbed CDDEs, our novel idea is to construct suitable comparison systems, from which the state vector can be estimated directly and the information on the initial value is exploited more effectively in order to derive a sharper state bound. For computing and minimising the state bound, a numerical linear programming-based algorithm is also presented. The effectiveness of the presented method is verified via two numerical examples. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced and robust tracking control method for the class of uncertain and disturbed affine nonlinear systems. The approach is based on predictive sliding mode control (PSMC) to combine the advantages of sliding mode control (SMC) with those of model predictive control (MPC). The resulting controller offers numerous benefits, including high robustness, quick transient response, finite-time convergence, and robustness against uncertainties and external disturbances. However, a significant limitation of the proposed PSMC is its reliance on prior knowledge of the disturbance and uncertainty bounds. To address this drawback, a sliding mode observer (SMO) is synthesised to identify uncertainties and disturbances. The proposed controller successfully eliminates the chattering effect without compromising robustness and precision. The Lyapunov theory is used to prove the stability of the closed-loop system. The effectiveness of the proposed approach is validated through simulations on a well-known benchmark. [ABSTRACT FROM AUTHOR]

This paper presents a novel moving horizon estimation approach based on distributionally robust optimisation to tackle the state estimation problem of non-linear systems with missing noise distribution information. The proposed method adopts a fuzzy set to mitigate the impact of uncertainties on state estimation. Specifically, the method derives an empirical distribution within the prediction window using a priori data and constructs a fuzzy sphere set using the Wasserstein metric with the empirical distribution as the sphere centre. This enables the estimation of the state sequence under the worst probability distribution of the fuzzy set. To demonstrate the effectiveness of the proposed method, a simple simulation example is conducted to compare its performance with that of traditional moving horizon estimation. The results provide evidence of the feasibility and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper addresses the limited adaptability and the computational burden of energy management systems (EMSs) for hybrid electric vehicles (HEVs) implemented via dynamic programming (DP)-based approaches. First, a deterministic dynamic programming (DDP) framework is presented to solve HEV EMS problems subject to a specific driving cycle. To address this limitation, an improved DDP approach, integrating the actual travelled position of the vehicle into the control law, is proposed. This way, a given DDP-based EMS can be applied to all the driving cycles, yet still measured on the same road. Stochastic dynamic programming (SDP)-based EMSs are also developed and prove to be more adaptive to driving scenarios completely different from the ones used for their computation. Real-world driving cycles are employed in all the presented cases, while a reduced HEV powertrain model is used to alleviate the typical DP computational burden. [ABSTRACT FROM AUTHOR]

In this paper, the distributed online optimisation problem is considered in an unknown dynamic environment. Compared with the existing results, an unknown dynamic environment causes the problem to be more challenging. An online optimisation algorithm is designed based on distributed mirror descent and distributed average tracking technology. The analysis of dynamic regret is presented and a bounded regret is obtained. Some simulations are given to verify the validity of the designed algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with integral control of a class of switched affine systems characterised by time-varying differential equations that, along with the discontinuities caused by the switching, also depend periodically on an exogenous parameter, which is an affine function of time. The goal is to design a state-dependent switching function with integral action that ensures global asymptotic tracking of a trajectory of interest. The integral action is responsible for providing robustness to the system against uncertainties and load variations, whenever the discrepancy between the model and the real system is sufficiently bounded. Due to the integrator, all the convex combinations of the dynamic matrices are non-Hurwitz. In this case, the main difficulty is to guarantee that the time derivative of the Lyapunov function is strictly negative definite, which is done by means of LMIs and conditions on the affine terms. To the best of our knowledge, this is the first Lyapunov-based switching rule with integral action able to ensure global asymptotic tracking of a pre-specified profile. When applied to AC circuits the advantage is to design the switching rule based on the original system, without resorting to averaged models. The theory is illustrated by the control of a three-phase AC-DC converter. [ABSTRACT FROM AUTHOR]

In this paper, a predictive-based adaptive anti-saturation tracking control scheme is designed for uncertain multiple-input and multiple-output (MIMO) nonlinear systems subject to input constraints and external disturbance. Taking the safety of the MIMO nonlinear systems into account, a safety saturation threshold, which is less than the physical device saturation threshold, is given. In addition, since all signals of the auxiliary systems are not immediately employed to the systems when the input saturation happens, a new predictive-based auxiliary system is designed. Furthermore, to estimate the compound disturbance, with the help of the fuzzy control technology, the nonlinear disturbance observer (NDO) is used. Then, the stability of the closed-loop systems is proved under the designed controller. Finally, a simulation example is given to illustrate the feasibility of the designed anti-saturation controller. [ABSTRACT FROM AUTHOR]

This paper is concerned with the optimal portfolio problem for a company that can invest in two risky assets, where a novel Lévy-process-driven model is constructed to describe the dynamics of the wealth process by using a constant elasticity of variance model and a jump-diffusion process. A delicately designed value function is proposed under the mean–variance criterion to reflect the optimal portfolio for the stochastic volatility model. By using the verification theorem, the desired optimal portfolio strategy is proposed by the solution to certain Hamilton–Jacobi–Bellman equations. Furthermore, the corresponding expressions are achieved by using the stochastic analysis theory. Finally, a numerical simulation example is provided to verify the effectiveness of the proposed optimal portfolio strategy. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of developing a unified method for quantifying and improving the cyber resilience of large-scale cyber-physical systems (CPSs) against cyber-attacks targeting structural controllability. To this end, we provide necessary and sufficient conditions to extend the concept of structural controllability of single-layer systems to two-layer CPSs comprising of power network as the physical layer and communication network as the cyber layer. Next, in order to assess cyber resilience, we introduce the notion of $ R $ R -resilient CPS as the maximum number of cyber nodes that can be attacked by an adversary while preserving the structural controllability of the entire CPS. Furthermore, we provide an algorithm to find the optimal network reconfiguration matrix to increase the resilience level of CPS from $ R $ R -resilient to $ ({R + 1}) $ (R + 1) -resilient by adding a minimum number of transmission lines to the physical layer or communication links to the cyber layer, or both. Additionally, the proposed methodology is extended to achieve $ ({R + \alpha }) $ (R + α) -resilient CPS, where α>1. The effectiveness of the proposed method is verified through simulations of IEEE 14, IEEE 39, and IEEE 118 power networks with different cyber topologies, considering four scenarios of denial-of-service (DoS) attacks, including targeted and random node attacks. [ABSTRACT FROM AUTHOR]

This paper discusses the fuzzy adaptive consensus secure control problem for a class of stochastic nonlinear multi-agent systems (MASs) with unknown false data injection attacks. Fuzzy logic systems (FLSs) are used to model unknown nonlinear functions. A distributed filter is proposed to obtain the information of the leader since leaders information is only available to partial agents. To deal with the unknown virtual time-varying gains caused by false data injection attacks, an adaptive compensation method is developed via re-codesigning the virtual controllers. Based on backstepping control methodology and bounded estimation algorithms, an adaptive fuzzy consensus secure controller is proposed. It is proved that all the signals in the closed-loop systems are bounded in probability, and all the output of followers can track the leader under false data injection attacks. Finally, simulation results and comparative results show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

Flexible-joint manipulators (FJMs) have been attracting much attention in recent decades. However, position control and vibration suppression of single-input (SI) multi-degrees-of-freedom (multi( $ {\geqslant }3 $ ⩾ 3)-DOF) FJM is still a high mountain to climb due to the characteristic that some states of such FJMs cannot be directly controlled by the SI. This paper aims to carry out this challenging research. We present a double-loop frame-based adaptive neural sliding-mode control method for an uncertain horizontal SI 3-DOF FJM, i.e. a two-link FJM with only one actuator at the first joint. Since only the actuator angle can be directly controlled, in the inner loop, we design a torque controller so that the actuator angle tracks a time-varying reference actuator angle, designed in real-time by the outer loop. In the outer loop, we consider the reference actuator angle as a virtual control signal and design it based on the dynamic coupling relationships, aiming to indirectly control the first link to its target position and suppress the vibration of both links. Furthermore, to enhance the transient system performance, we plan the input signal of the outer loop (i.e. the target angle) as a smooth reference-trajectory signal. The validity and superiority of this method are demonstrated in simulations. [ABSTRACT FROM AUTHOR]

In the paper, the exponential synchronisation for delayed Clifford-valued coupled switched neural networks via static event-triggering rule is studied. Firstly, the drive-response systems for delayed Clifford-valued coupled neural network models are established. So as to avoid the non-commutativity issue of Clifford number multiplication, the original n-dimensional Clifford-valued models are decomposed into $ 2 ^mn $ 2 m n -dimensional real-valued models. On this basis, the error dynamics system is constructed, and then some new sufficient conditions are presented of the exponential synchronisation for the considered neural network models by using Lyapunov–Krasovskii (L-K) functional approach and the technique of linear matrix inequality. Finally, the effectiveness of the results are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of attitude trajectory tracking of rigid bodies subject to external disturbances. The attitude control problem has been studied in the last decades and novel solutions have been proposed. Nevertheless, most of the proposed controllers only achieve local or almost global asymptotic stability without considering external disturbances. Contrary to other works, we focus on the problem of achieving almost global exponential stability of the attitude tracking errors in the presence of external disturbances using continuous control laws. We propose an attitude trajectory tracking controller based on the exponential coordinates of rotation in combination with a disturbance observer that estimates the exogenous signals. To solve this problem, we adopt a hierarchical approach, where the proposed control law is divided into a kinematic controller (outer control loop) and a velocity tracking controller (inner control loop). To design the disturbance observer, it is assumed that exogenous signals can be generated by a linear exosystem, i.e. the magnitude and phase of the disturbance are unknown. The almost global exponential stability of the closed-loop dynamics' equilibrium point was proved by a strict Lyapunov function. The performance of the proposed approach is assessed by numerical simulations and experimental tests on a low-cost quadrotor. [ABSTRACT FROM AUTHOR]

In this paper, a typical motion-based modelling and tracking issue is investigated for vehicle targets in linear road segment by a variable structure multiple model (VSMM) method. Vehicle target motions in a road with multiple lanes are described by typical lane keeping and lane changing manoeuvres. To describe trajectories of typical manoeuvres, an Ornstein–Uhlenbeck process and a sine half-cycle are used to model lane keeping and lane changing motions in lateral direction, respectively. Note that starting point and length of lane changing motion are unknown in target tracking. Time-varying model sets are designed based on the current motion model with different motion parameters. A VSMM tracking frame is constructed to obtain target state estimates with time-varying model set. The effectiveness of the proposed typical motion-based tracking scheme is displayed by simulation results on road vehicle tracking. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive control problem is investigated for a class of interconnected nonlinear systems (INS) with unknown interconnection delays and input saturations. For state unmeasurable problem, a full dimension state observer (SO) is designed to estimate the inaccessible state variables. Broad learning system (BLS), a novel nonlinear approximation technique, is introduced in this study to identify the unknown dynamics and could achieve better approximation performance. An adaptive distributed control (DC) scheme is proposed for the uncertain time-delay INS without input saturation, which guarantees that the stability of the closed loop INS. On this basis, the input saturation problem is further considered, an online approximation smooth function is added into the distributed adaptive tracking controller, such that the closed-loop INS have the anti-input saturation capability. In terms of Lyapunov theory, all the signals in the closed-loop uncertain time-delay INS with input saturation are proved to be uniformly ultimately bounded (UUB) and the tracking errors could converge to a small neighbourhood of zero. Finally, one simulation example performed on two parallel inverted pendulum cars demonstrates the superiority of the developed distributed control scheme. [ABSTRACT FROM AUTHOR]

Distributed switched large-scale systems are composed by dynamically coupled subsystems, in which interactions among subsystems vary over time according a switching signal. This paper presents a distributed robust switched model-based predictive control (DSwMPC) to control such systems. The proposed method guarantees stabilising the origin of the whole closed-loop system and ensures the constraints satisfaction in the presence of an unknown switching signal. In the distributed model-based predictive control (DMPC) used in this work, by considering the interactions among subsystems as an additive disturbance, the effect of the switch is reflected on the dynamic equation, local, and consistency constraint sets of the nominal subsystems. To compensate the effect of switching signal which creates a time-varying network topology, a robust tube-based switched model-based predictive control (RSwMPC) with switch–robust control invariant set as the target set robust to unknown mode switching is used as local controller. The scheme performance is assessed using three typical examples. The simulation results show that the input and state constraints are satisfied by the proposed DSwMPC at all times. They also validate that the closed-loop system converges to the origin. Also, a comparison of the DSwMPC with a centralised SwMPC (CSwMPC) and a decentralised SwMPC (DeSwMPC) are performed. [ABSTRACT FROM AUTHOR]

In this paper, the security control problem for T-S fuzzy systems is investigated through the sliding mode control (SMC) strategy. Consider deception attacks occurring randomly, a multi-modes round-robin protocol is proposed on the sensor-to-controller channel. Under this protocol, the number of transmitted nodes can be dynamically adjusted according to attack probability and system state so as to balance the relationship between system performance and communication transmission. The fuzzy sliding mode controller depending on the scheduling signal is constructed, whose membership functions (MFs) are different from the ones of the controlled system. In addition, by using the membership-function-dependent (MFD) stability analysis method, appropriate sufficient conditions are derived to ensure both the stochastic stability and the reachability. Finally, simulation results are presented to prove the proposed SMC strategy. [ABSTRACT FROM AUTHOR]

This paper addresses the $ H_{\infty } $ H ∞ static output feedback control of discrete-time semi-Markov jump systems (S-MJSs) with mismatched modes in finite-time horizon. Different from the existing relevant work, transient dynamics in short-time is focused instead of the Lyapunov stability over infinite-time horizon. Considering the immeasurable system states, output control strategy is developed subject to mismatched modes. Firstly, S-MJSs in closed-loop form are established by designing a hidden mode-dependent output feedback controller. Based on the closed-loop system (CLS), new Lyapunov function relies on both system mode and elapsed-time are created, according to which fresh analysis criteria ensuring FTB and $ H_{\infty } $ H ∞ FTB are derived. To circumvent the non-convex difficulty arising in controller design, a separation technique is introduced such that the design prerequisites are obtained from the above performance analysis conditions. Finally, the boost converter circuit and a numerical example are borrowed to test the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper investigates the linear quadratic regulator (LQR) problem for continuous-time linear systems with redundant control inputs. The LQR control problem has been widely studied, but due to technical difficulties, there is little advance in the study of adding input redundancies. To address this problem, a convergent matrix series, the limit of a monotonically decreasing sequence, is first presented as an upper bound of the symmetric positive definite solution of a class of continuous algebraic Riccati equations (CAREs). Compared with the existing studies on this topic, the obtained bounds are more precise. Moreover, by utilising these upper bounds in the LQR problem of adding input redundancies, a class of sufficient conditions is presented to decrease the quadratic performance index. Finally, the corresponding numerical examples are given to illustrate the effectiveness of our results and compare them with the existing results. Meanwhile, some simulation experiments show that systems with redundant control inputs have good control performance. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative adaptive nonsingular Second-Order Terminal Sliding Mode (SOTSM) control strategy specifically designed to stabilise disturbed nonlinear Cyber-Physical Systems (CPSs) within a finite timeframe. Unlike the conventional methods, our approach incorporates a novel nonlinear sliding surface that eliminates the need for a reaching step, significantly enhancing the system's robustness. Novel real-time adaptive control laws are developed to cope with external perturbations, time-varying delays, and cyber-attack on actuators without requiring the identification of their upper bounds. The proposed method ensures robust performance for time-varying delayed and disturbed nonlinear CPSs, even in the face of strong cyber-attacks targeting the actuators. Moreover, the proposed method has distinct advantages: it delivers rapid response times, enhanced flexibility, exceptional accuracy, smooth and robust control devoid of transient fluctuations or chattering, and ensures finite-time convergence. Simulation results comprehensively demonstrate the superior efficiency and success of our proposed technique compared to traditional methods such as integral Sliding Mode Control (SMC) and State-Feedback Control (SFC) schemes. [ABSTRACT FROM AUTHOR]

This paper aims to establish a new event-triggered model predictive control (MPC) strategy for perturbed nonlinear systems working in a networked environment, which can effectively save the computation and communication resources while ensuring control performance. The core of the framework is a new event-triggered mechanism, which is built by combining the differential and integral (D-I) information of the error between the optimal state and the actual state in a pre-specified time horizon. Based on such a triggering mechanism, a D-I based event-triggered robust MPC algorithm is proposed to stabilise the system and reduce the computation/communication resource consumption. In addition, sufficient conditions to ensure the feasibility and stability of the proposed MPC algorithm and avoid Zeno behaviour are obtained through rigorous theoretical analysis. Finally, the proposed control framework is implemented in the nonlinear cart-damper-spring and autonomous underwater vehicle systems to verify its effectiveness. [ABSTRACT FROM AUTHOR]

This paper investigates banks' credit strategies concerning micro, small and medium enterprises (MSMEs) by taking into consideration the intrinsic characteristics of individual enterprises and external correlations among them when assessing credit risk. We analysed transaction data between enterprises and utilised the Bean Search algorithm and Markov random field to construct enterprise network topology diagrams, quantifying inter-enterprise correlations. We also applied the combined weight method to balance their creditworthiness and strength indices and developed a comprehensive and scientifically grounded credit strategy model for MSMEs by employing genetic algorithms to solve nonlinear programming problems. Overall, our proposed model aims to maximise loan revenues while simultaneously minimising the risk of customer default. [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]

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 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]

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 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]

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]

Simultaneous localisation and mapping (SLAM) systems have been widely studied over the past 30 years and extensively applied in various fields such as mobile robotics, augmented reality, and virtual reality. The goal of the SLAM technique is to simultaneously map the surrounding environment and obtain the ego-motion of the sensing platform. As the number of application scenarios of SLAM systems increases and related tasks become more complex, SLAM systems based on a single sensor are no longer sufficient to meet the demands, thus the trend for SLAM systems based on multi-sensor fusion has emerged. In this paper, we review the SLAM systems from the perspective of various configurations of heterogeneous sensors. These configurations include visual-inertial, lidar-inertial, lidar-visual, lidar-visual-inertial, and other multi-sensor fusion systems. In addition, the advantages and disadvantages of each configuration are also given. Based on the review, several open issues for further research are discussed at the end of this paper. [ABSTRACT FROM AUTHOR]

The robust stabilisation for a general class of disturbed nonlinear systems subject to state-dependent uncertainties is addressed in this paper. Different from most existing results, the lumped uncertainty, including external disturbances and model uncertainties, is relaxed to be bounded by some nonlinear functions rather than constants in this paper. Based upon the classical design procedure of disturbance observer-based control (DOBC), a new continuous saturation-based dynamic controller is proposed to estimate the lumped uncertainty. By the delicate design of several compact sets, the qualitative robust stability of the closed-loop system is guaranteed in the sense that all the signals of the closed-loop system can converge to an arbitrarily small compact set in a finite time. Furthermore, when the external disturbances are constants, the semi-global asymptotic stability can also be guaranteed by the proposed controller. Finally, the relationship between system uncertainties and observer gain is shown by a numerical simulation example. [ABSTRACT FROM AUTHOR]

This research investigates the double tracking control problem for the complex dynamic network (CDN) with an unavailable link state. Firstly, from the angle of a large-scale system, the dynamical model of CDN is described by the vector differential equations, which consists of node dynamic subsystem (NDS) and link dynamic subsystem (LDS), in which the weighted-values of links are regarded as the state variables of LDS. Secondly, to realise the double tracking control (DT-Control) of CDN, the presented DT-Control scheme in this paper includes the synthesis of controller for NDS and the coupling term in LDS, which can ensure that the two subsystems track the given reference targets. The tracking of NDS contains the synchronisation of nodes as the special case, and the tracking of LDS shows that the eventual topologic structure of CDN will be determined only by the given link reference signal. Due to the economic and technological limitations of sensors in the practice applications, this paper assumes that the state variables of LDS are unavailable in the DT-Control scheme. Finally, the engineering simulation example is given to verify the validity of DT-Control scheme proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive tracking control strategy based on neural networks (NNs) is proposed for the stochastic nonlinear strict-feedback system with a time-varying input delay and unmeasurable states. A state observer is constructed to solve the problem of unmeasurable state and the effect of the time-varying input delay is compensated by using an auxiliary signal. All the virtual controllers and the actual controller are designed as the optimised solution of the corresponding subsystems to ensure the entire obtained controllers are optimised. To ensure that the states of the system are constrained within some given compact sets, the barrier Lyapunov function (BLF) is used to design the controller and perform stability analysis. Meanwhile, this paper adds a self-triggered mechanism to reduce the communication burden of the data transfer. Thus, the proposed control strategy can achieve optimised control and all system states can be constrained within the given ranges. On the other hand, all closed-loop signals of system are bounded in probability. Finally, it is demonstrated that the proposed control strategy can achieve the expected system performance by a simulation experiment. [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]

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]

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]

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]

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]