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In this paper, the open-loop and closed-loop local and remote stochastic nonzero-sum game (LRSNG) problem is investigated. Different from previous works, the stochastic nonzero-sum game problem under consideration is essentially a special class of two-person nonzero-sum game problem, in which the information sets accessed by the two players are inconsistent. More specifically, both the local player and the remote player are involved in the system dynamics, and the information sets obtained by the two players are different, and each player is designed to minimise its own cost function. For the considered LRSNG problem, both the open-loop and closed-loop Nash equilibrium are derived. The contributions of this paper are given as follows. Firstly, the open-loop optimal Nash equilibrium is derived, which is determined in terms of the solution to the forward and backward stochastic difference equations (FBSDEs). Furthermore, by using the orthogonal decomposition method and the completing square method, the feedback representation of the optimal Nash equilibrium is derived for the first time. Finally, the effectiveness of our results is verified by a numerical example. [ABSTRACT FROM AUTHOR]

This paper investigates the impulsive stability analysis issues of discrete-time positive singular systems with time delay. First, the paper addresses the positivity problem of the system by providing sufficient conditions. Next, a new method based on state transformations is presented to derive a new delay-dependent criterion for the exponential stability of impulsive positive singular systems. Finally, the effectiveness of the proposed conditions is validated through three numerical examples. [ABSTRACT FROM AUTHOR]

A novel strategy based on the robust distributed model predictive control (DMPC) is proposed in this paper to address the consensus of multi-agent systems subjected to unknown-but-bounded disturbances with limited communication range. The proposed strategy utilises the formulation of the tube-based predictive control and has the ability to handle the explicit input constraints and achieve the bounded consensus with initial communication topology preserved under the disturbances. In this proposed strategy, a novel constraint is designed to guarantee the connectivity of the system under the disturbances, and the synthesis methods are proposed for the controller. Meanwhile, it is proved that the proposed algorithm in this paper is recursively feasible, the connectivity can always be preserved and the system can finally reach bounded consensus under the disturbances. A simulation example is provided to demonstrate the proposed robust DMPC-based strategy. [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]

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]

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]

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]

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]

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 piecewise Chebyshev cardinal functions (PCCFs) are handled to design a highly accurate direct method to solve a category of fractional optimal control problems (FOCPs) involving the Caputo–Katugampola fractional derivative (as a generalisation of the Caputo derivative) in a large domain. These cardinal functions have many useful features, such as cardinality, exponential accuracy and orthogonality. Notice that the exponential accuracy makes it possible to obtain an approximate solution with very high accuracy for the problem under consideration by applying only a small number of basis functions. In this regard, first, the ordinary and fractional integral matrices of the PCCFs are obtained to construct a direct method using them. Then, the constructed approach turns to solve the FOCP under consideration into deriving the solution of an algebraic system (by the aid of approximating the state and control variables based on the PCCFs). To show the validity of the established technique some illustrative examples are provided. [ABSTRACT FROM AUTHOR]

This paper explores the application of a fully actuated system (FAS) approach to a spacecraft rendezvous system with actuator saturation. A FAS model for the spacecraft rendezvous system is developed, followed by the design of an extended state observer (ESO) to estimate unmeasurable states and the system's nonlinear terms. The saturation issue is addressed using the convex hull method. By the designed ESO, a state feedback controller for the system is designed using the FAS approach, resulting in a constant linear closed-loop system whose eigenvalues can be arbitrarily assigned. This method simplifies the controller design process and provides a more accurate system model without the linearisation of nonlinear terms, thereby improving control accuracy. Simulation results show the effectiveness and usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the μ-state estimation issue for Markovian switching complex-valued neural networks with mixed delays has been addressed. To conserve communication resources, an effective event-triggered mechanism is proposed, which relays on a Markovian switching positive threshold. By resorting to Lyapunov stability theory, stochastic analysis method and matrix inequalities technique, some sufficient criteria are established to guarantee the estimation error system is stochastic μ-stable in the mean sense. Moreover, the expected estimator gain matrices can be accurately achieved by solving some obtained matrix inequalities. Besides, it is should be pointed that the μ-stability involves exponential, power, and logarithmic stability, which expands a mass of existed relevant results. In the end, for power stability, one numerical example is provided to substantiate the effectiveness and credibility of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Aiming to the sudden change of the system actual output constraint, a novel safe tracking control strategy is investigated for a class of non-strict feedback nonlinear systems with unknown initial tracking condition in this paper. A secure boundary protection method (SBPM) based on prescribed performance and a new prescribed finite-time performance function (PFTPF) are proposed. The SBPM can automatically construct two adjustable secure boundaries, so that the system output meets both the control performance prescribed by the PFTPF and the secure boundary constraint. In order to solve the problem of the excessive control input jitter caused by the sudden change of output constraint, a bidirectional filtering smoothing mechanism (BFSM) is proposed. The proposed SBPM can effectively ensure the safe tracking control of the system with unknown initial tracking condition when the actual output constraint changes suddenly, and ensure that the system has the prescribed finite-time control performance given by the PFTPF. Differently from the existing works, the system output will be strictly constrained into the actual output constraint without the prediction for the system output value. Finally, the effectiveness of this proposed method is verified by simulations. [ABSTRACT FROM AUTHOR]

Disturbance rejection has always been a major phenomenon in control theory. Disturbances that arise in control of unstable or integrating unstable processes with time delay present considerable difficulties for classical PID controllers. This paper supplies analytical tuning rules, derived from optimum disturbance rejection responses to minimise the error signal according to several integral performance criteria to identify the tuning parameters of the I-PD controller. The provided analytical rules offer the advantage of calculating controller parameters without the necessity of employing an optimisation algorithm. This simplifies the tuning process and allows for a straightforward determination of the controller's parameters, making it more convenient and efficient for practical implementation in control systems. Comprehensive simulations were performed to validate the effectiveness of the proposed I-PD controller in terms of disturbance rejection responses, control signals, perturbation in process parameters, measurement noises, TV values, Ms values, and integral performance indicators. Overall, the outcomes demonstrate that the introduced method for the tuning of I-PD controllers offers notable advantages when compared to other tuning methods found in the literature. [ABSTRACT FROM AUTHOR]

This paper studies the global regulation problem by a non-predictor event-triggered controller for high-order feedforward nonlinear systems with unknown time-varying delays in the states and input. We propose a non-predictor-based event-triggered controller with a dynamic gain-scaling factor, in which any information on the delays is not required in controller design. The dynamic gain-scaling factor is utilised to deal with high-order terms so that the considered class of systems are generalised over the previous results. The simulation results effectively demonstrate the validity of our proposed control method. [ABSTRACT FROM AUTHOR]

In this paper, we develop an improved data-based integral policy iteration method to address the robust control issue for nonlinear systems. Combining multi-step neural networks with pre-training, the condition of selecting the initial admissible control policy is relaxed even though the information of system dynamics is unknown. Based on adaptive critic learning, the established algorithm is conducted to attain the optimal controller. Then, the robust control strategy is derived by adding the feedback gain. Furthermore, the computing error is considered during the process of implementing matrix inverse operation. Finally, two examples are presented to verify the effectiveness of the constructed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive optimal control strategy of finite-time control for high-order uncertain strict-feedback nonlinear systems. Firstly, a reinforcement learning (RL) based an optimal control scheme is employed to design a optimal controller, to achieve global optimisation. Additionally, considering the unmeasurable states, we construct a fuzzy observer and utilise fuzzy logic systems to approximate the unknown functions. Meanwhile, the inclusion of command filtering and time-based control simplifies the controller design and enhances the system's response rapidity. Finally, the effectiveness and feasibility of the proposed approach are validated through a numerical simulation and a single link-robot system simulation. [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 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]

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 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 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]

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]

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]

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 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]

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 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]

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]

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]

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]

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 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 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]

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]

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]

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]

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 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]