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

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

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

This research paper focuses on addressing the challenge of infinite-time linear quadratic tracking control (LQT) for linear systems with parametric uncertainty. Traditional solutions to the LQT problem often involve using a discount factor to prevent the cost function from growing unbounded over time. However, this approach can introduce instability in the closed-loop system. To overcome this issue, this paper proposes an alternative approach using an undiscounted cost function that ensures the asymptotic stability of the uncertain closed-loop system. To design a control scheme without requiring precise knowledge of the system dynamics, reinforcement learning (RL) algorithms are employed. However, for systems with uncertain parameters that may lead to instability, the convergence of RL algorithms to a stabilising solution is not guaranteed. To address this limitation, a robust optimal control structure is developed using on-policy and off-policy reinforcement learning algorithms, resulting in a model-free controller. The effectiveness of the proposed robust optimal controller is validated through comparative simulations on an uncertain model of a DC–DC buck converter connected to a constant power load. These simulations demonstrate the advantages and benefits of the robust optimal controller in handling parametric uncertainty and ensuring stability in the control system. [ABSTRACT FROM AUTHOR]

This paper studies the tracking control problem of nonlinear high-order fully actuated system subject to state constraints. In order to guarantee the stability of the closed-loop system, a generalised nonlinear proportional differential controller is designed to configure the system into a desired linear constant system. Then, an explicit reference governor for high-order system is introduced to modify the reference signal such that the system state and the state derivatives of certain orders always remain within a prescribed constraint set. Furthermore, we prove that the modified reference will converge to the original reference as much as possible. Therefore, the system state can finally track to the original reference by tracking the modified reference. Two numerical examples demonstrate the validity of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the controllability of multi-agent systems over finite fields is studied based on the high-order fully actuated (HOFA) system approaches. The concept of HOFA systems over finite fields is introduced. Necessary and sufficient conditions for the controllability of linear systems over finite fields are established. It is proved that a leader–follower multi-agent system over finite fields is controllable if and only if it can be transformed into an HOFA system. Then the stabilisation control protocol is proposed based on the full-actuation property, and the upper bound of settling time for the system can be determined. The uncontrollable leader–follower multi-agent system over finite fields can be transformed into a controllable subsystem and an autonomous subsystem via structural decomposition, the conditions for the system to be stabilisable are provided. Finally, an illustrative example is shown to verify the validity of the main results of this paper. [ABSTRACT FROM AUTHOR]

This paper focuses on the adaptive prescribed performance control problem for high-order fully actuated (HOFA) system with state time-delay. Different from the existing results, an adaptive prescribed performance control method is proposed to ensure that the HOFA system with state time-delay can achieve semi-globally practical finite-time stability. Moreover, it is worth mentioning that the control method ensures both the steady-state performance of the system and the dynamic performance of the system within a finite time. In addition, the settling time of this paper is independent of design parameters and initial conditions. Hence, it can be set to any value. Finally, a practical example of a thermoacoustic unstable system in the Rijke tube is used to verify the effectiveness and feasibility of the scheme. [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]

In this paper, the energy-to-peak filtering problem for the uncertain discrete-time singular bilinear systems is studied. This paper considers the uncertainties that consist of real systems and the possible data packet dropouts when performance output signals and measurement output signals are transmitted over digital channels. In the course of the study, the phenomenon is elucidated utilising the Bernoulli random binomial distribution. The goal is to design the desired filter, so that the filtering error system is admissible, and meets the specified energy-to-peak performance index. The design conditions of energy-to-peak filters for uncertain discrete-time singular bilinear systems are given by introducing the Lyapunov function and using linear matrix inequalities (LMIs). The impact of uncertainty on singular bilinear systems is addressed using a two-step approach. At last, a numerical illustration is provided to demonstrate the validity of the design. [ABSTRACT FROM AUTHOR]

This paper focuses on finite-time boundedness problem of switched linear systems under periodically intermittent control. Sufficient conditions are established to guarantee finite-time boundedness or finite-time stability of switched linear systems under periodically intermittent control. The proof procedures are implemented successfully resorting to piecewise Lyapunov functions. Based on the linear matrix inequality technique, the conditions are casted into a convex optimisation problem and therefore, they can be efficiently handled. The method proposed in this paper is a much potentially useful one in practice because of its economical and efficient nature that is easy to employ. Finally, two examples are given to express the validity and efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive consensus with limited information for a class of uncertain nonlinear multi-agent systems (MASs). The limited information, meaning the incomplete information transmitted between agents, stems from nonzero-kernel weight matrices of communication networks, which deserves concerns owing to its practical interest such as in privacy preserving and information compression. Apart from the limited/incomplete information, the paper considers nonlinear MASs with large uncertainties (without known upper bounds). This is essentially different from the existing results of linear systems and poses challenges for the design and analysis of adaptive consensus. As for fully distributed protocol design, dynamic high gains are introduced to compensate for the system uncertainties and for the unavailable global graph information. In closed-loop consensus analysis, a new Lyapunov function is constructed by utilising weight and incidence matrices instead of Laplacian matrix. It turns out that the designed protocol can guarantee the boundedness of closed-loop MASs signals and the convergent consensus. Two numerical examples are given to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

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 the paper entitled 'An Graph HT ht optimisation approach to distributed event triggered fault detection over wireless sensor networks' by Zhong et al., distributed fault detection has been realised by resorting to the Graph HT ht optimisation approach, where a product term between the preset positive scalar and measurements is involved in the event-triggering condition. The high integration of physical processes and cyber infrastructure greatly promotes the speedy growth of cyber-physical systems (CPSs), which has been turning into a critical essence of the so-called fourth industrial revolution. The surv ey paper ' I A survey on sliding mode control for networked control systems i ' by Hu et al. proposes a profound review on recent advances of sliding mode control schemes, according to different scheduling protocols, actuator/sensor faults, and cyberattacks. [Extracted from the article]

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]

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 paper mainly studies the convergence of time-varying discrete-time Altafini model. Firstly, we convert the bipartite consensus of the Altafini model with n nodes into consensus of the DeGroot model with $ 2n $ 2 n nodes by adopting lifting approach. Secondly, based on structural balance theory and node relabelling theory, the consensus of enlarged DeGroot model can be further equivalently converted into the consensus of one simple DeGroot model with n nodes. Thirdly, by using the equivalent transformation and the ergodic coefficient theory, we change the sufficient condition from 'repeatedly jointly strongly connected' graphs to 'repeatedly jointly scrambling' graphs, and verify that the new condition also can lead to the bipartite consensus of the Altafini model. This novel criterion allows for determining the bipartite consensus of the Altafini model without the condition of 'repeatedly jointly strongly connected' graphs. Next, we provide a concise proof for the case of opinions converging to 0 with the condition of 'repeatedly jointly strongly connected' by the ergodic coefficient theory. Finally, we develop a reasonable coevolution model to illustrate the effectiveness and strong applicability of our main theoretical results. [ABSTRACT FROM AUTHOR]

This paper studies the formation tracking problem of the so-called open multiagent systems. In these systems, agents can freely join or leave at any time, i.e. the system sizes are changeable. To achieve formation tracking control in these systems, we propose a general theoretical analysis framework that consists of a formation adjustment strategy and a formation tracking protocol. Therein, the former is used to adjust the predefined task-oriented formation shape to the varying size of the system. This strategy is built on the existing scalable self-healing idea and then modified by a size estimation method. The latter is used to drive the present followers to form the adjusted formation and track a leader. To accommodate undirected and directed graphs, two distributed formation tracking protocols are designed respectively. Due to possible agent migration, the designed tracking protocols are endowed with self-adaptive ability, i.e. without depending on global topology information. Then, their convergence properties are analysed using the Lyapunov theory. Finally, two simulations are presented to verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Minimum circle circumnavigation is proposed in this paper, which is of special value in target monitoring, capturing and/or attacking. With this new pattern, our goal is to steer one agent to enclose multiple targets along a safe and minimum circular orbit with the desired tangential speed. This is achieved by a control protocol with dynamic compensators, which is also capable of avoiding collision between the agent and the multiple targets during the whole operational process. The control protocol is further extended to the scenarios of multiple agents circulating a group of targets by adding a coordination mechanism into the tangential velocity term, which allows the agents to distribute evenly on each expected circular orbit with the same or different radius. Simulations results illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper addresses the co-design of event-triggered dynamic output-feedback (DOF) control for discrete-time linear parameter-varying (LPV) systems. Two independent event-triggering schemes are introduced to determine when to transmit data from the sensor to the controller and from the controller to the actuator channels. Enhanced conditions employing linear matrix inequalities (LMIs) are derived to establish constructive criteria for simultaneously designing the gain-scheduled DOF controller and the triggering mechanisms, ensuring the asymptotic stability of the closed-loop LPV system's origin. Additionally, a convex optimisation problem is proposed to enlarge the inter-event sampling on the independent channels of sensor-to-controller and controller-to-actuator. Numerical examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the exponential quasi-synchronisation of conformable fractional-order complex dynamical networks with mixed delays under periodically intermittent pinning control. First, by the definition of the conformable fractional-order derivative and fractional-order comparison principle, two generalised conformable fractional-order Halanay inequalities are established. Then, by these two inequalities, Lyapunov method and periodically intermittent controller, criteria for realising exponential quasi-synchronisation for the researched network are obtained. Finally, numerical examples are given to illustrate the feasibility of the derived results. [ABSTRACT FROM AUTHOR]

This paper explores event-triggered model predictive control (MPC) for constrained switched systems, incorporating a buffer-based anti-attack (BBAA) approach to ensure network security. Since the system state is unknown and disturbed, an observer-based $ H_{\infty } $ H ∞ MPC approach is used to estimate the system state and suppress disturbance. Furthermore, the estimated state and control input can be optimised by minimising the performance function. Considering that the system state is subject to denial of service (DoS) attacks in the feedback channel, the BBAA approach is used to reconstruct the estimated state, thereby reducing the impact of attacks on system performance. To improve the utilisation of network resources, a set of event-triggering mechanisms (ETMs) is used. In addition, a set of matrix inequality conditions is given to constrain the gains of controllers and observers. Then, the system $ H_{\infty } $ H ∞ performance is demonstrated by applying the average dwell time (ADT) and the Lyapunov function method, and the complex coupling between model switching, DoS attacks and event-triggering is analysed. Simulation results validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces a new analytical technique using a direct synthesis strategy to develop centralised proportional–integral (PI) controllers for multivariable processes. The current method design controller is focused on attaining the desired closed-loop response for multi input multi output (MIMO) processes that involve multiple time delays. The conventional multivariable PI controller is obtained by approximating the ideal multivariable controller by the Maclaurin series expansion. This is accomplished by choosing the desired closed-loop response order as the system order plus two. Subsequent analysis investigates the proposed method's efficacy in designing multivariable PI controllers. Servo and regulatory problems studies were conducted to find the effectiveness of the proposed method and compared it with other methods recently reported in the literature, as well as assessment of performance indices like integral absolute error (IAE) and integral square error (ISE). The proposed controller's robustness is assessed by plotting the inverse maximum singular value against frequency, both input and output multiplicative uncertainties. [ABSTRACT FROM AUTHOR]

The overarching contribution of this paper is a novel event-triggered distributed control architecture for multiagent systems that are composed of a team of nonholonomic mobile robots. Specifically, the robot dynamics are first feedback linearised to equivalently represent each robot with double integrator dynamics and the event-triggering law is then presented. This law is characterised by being decentralised (depending only on own signals of a robot), norm-free (independent of distances for reducing robot-to-robot data transmissions), and adaptive (estimating signals without using global information). Moreover, its system-theo- retical analysis is provided, which holds for both the sampled data exchange method and the solution-predictor curve-based data exchange method. In contrast to the well-studied sampled data exchange, the solution-predictor curve method further reduces robot-to-robot data transmissions by making each agent to store a generic form of this curve and exchange its parameters when an event occurs for approximating the solution trajectory of each robot. Finally, an illustrative numerical example is included to demonstrate the overall architecture. [ABSTRACT FROM AUTHOR]

This article investigates the trajectory tracking control problem for wheeled mobile robot (WMR) subject to both velocity and torque constraints. Firstly, a more general model is established by considering the displacement between the centre of mass and the midpoint of the driving wheels of the WMR, based on which, a new bounded kinematic controller is proposed by combining the first-order filtering approach to keep the trajectory tracking error asymptotically stable. Then the dynamic controller is designed and the stability of the overall closed-loop system is proved by the Lyapunov stability theory and Barbalat's lemma. Some conditions are given to ensure that both the velocity constraints and torque constraints are satisfied. Finally, some numerical examples are given to show the effectiveness and advantages of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

With the rapid development and wide application of sensing devices and communication networks, the traditional power system has transformed into a cyber physical power system (CPPS) gradually. The constant interaction of information flow and power flow exposes the grid to potential cyber-attack risks. In this paper, the defense strategy selection approaches are proposed based on incomplete information game against false data injection attack (FDIA) in different scenarios. Firstly, the attack-defense tree model containing economic indicators and a measure of attack is established, and the expected revenues of both attacker and defender are calculated in detail. Secondly, the static Bayesian attack-defense game model in which the defender grasps incomplete information is constructed, considering two cases of low-tech attacker and high-tech attacker. Then, the zero-sum attack-defense game model in which the attacker masters incomplete information is structured, considering the changing attack space. Finally, the simulations are carried out on the IEEE 30-bus system, and the optimal defense strategies in the case of two incomplete information are obtained by solving the equilibrium point. The simulation results demonstrate that the proposed method is effective in cyber security defense system, and can provide guiding ideology and theoretical basis for operators to deploy defense measures. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic positioning control scheme for offshore drilling platforms using the equivalent-input-disturbance (EID) method with separated observers. The scheme incorporates observer-based disturbance compensation, wave-filtering state estimation, input transformation, and state feedback control to achieve robust control performance. The main contributions include the design of an EID scheme with separated observers, enabling enhanced disturbance-rejection performance, incorporating a notch filter in the EID estimator to reduce energy loss, and giving an observer tuning scheme to achieve a balanced performance between disturbance rejection and wave filtering. Simulation results demonstrate the effectiveness of the proposed scheme. This work provides valuable insights into designing stable and efficient dynamic positioning systems for offshore drilling platforms. [ABSTRACT FROM AUTHOR]

One of the main difficulties for application of sliding mode control is the presence of chattering. It is a well known fact that the amplitude of chattering is proportional to the magnitude of discontinuous control. The problem can be alleviated by making the magnitude as small as possible while still ensuring the existence of the sliding mode. In this paper, a novel predefined-time single layer adaptive scheme based on equivalent concept with a relaxed assumption about unknown dynamics is proposed for dynamically tuning the gain associated with the control magnitude, and applying this scheme in sliding mode control algorithms enables decreasing the control action magnitude to the minimum possible value remaining the characteristic of finite-time convergence. Simulation results are shown to verify the effectiveness of the designed scheme. [ABSTRACT FROM AUTHOR]

Designing a full-state observer for nonlinear systems has always been accompanied by challenges and restrictive constraints. Mainly, applying a state observer in nonlinear systems with non-minimum phase characteristics is more challenging when the limiting constraints are not satisfied due to diverging internal dynamics. In this paper, a robust sliding-mode observer approach has been successfully employed to estimate the states of nonlinear systems with unbounded and diverging dynamics. The design principles of this observer are based on applying a classifying algorithm in single-input single-output and multiple-input multiple-output nonlinear systems. It is noteworthy that this observer is highly robust against disturbance, uncertainty and measurement noise, and its conditions are less conservative compared to previous nonlinear sliding-mode observers. One novel feature of the proposed observer is that while the system's state gets unbounded and diverged in fault-occurring scenarios or critical circumstances, this observer retains accuracy. The efficiency of the proposed observer is verified in the simulation results for two nonlinear industrial systems, including a hydro-turbine power generation plant and a continuous stirred tank reactor. [ABSTRACT FROM AUTHOR]

This paper introduces a novel event-triggered consensus control strategy for a multi-agent system composed of underactuated flexible manipulators. Most existing research on consensus tracking control assumes that the model targeted is fully actuated, the control signal is continuous, and that both time-varying actuator failures and input constraints are managed separately. These challenges were tackled in this study within the context of multi-agent systems, addressing input constraints and nonlinear time-varying actuator failures simultaneously. Moreover, an event-triggered mechanism is proposed to reduce signal communication. A double closed-loop consensus tracking method is designed based on the multi-agent topology, enabling all flexible manipulators to track the angle and angular velocity of the leader and an adaptive control law is designed to solve actuator faults. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

A central processing unit receives data from all agents and transmits control commands in a Networked Control System (NCS) which is centralised. Centralised NCSs have numerous applications in industrial settings due to their efficiency, simplicity and cost-effective design. However, centralised NCSs are vulnerable to false data injection (FDI) attacks. Despite the fact that researchers have developed detection and mitigation defense mechanisms during past several years, most of these methods have focused on systems with linear dynamics. Furthermore, the existing literature only assumes the injection of FDI attacks on measurement signals. In this paper, we assume that an adversary has injected the FDI attack into both state measurements and control signals with nonlinear dynamics while considering communication noises and disturbances. We propose a secure nonlinear control design that mitigates FDI attacks in real time by combining learning and model-based approaches. We used Lyapunov stability analysis to design the controller, estimator and updating laws of the neural network (NN). In addition, we selected a network of two robots with Euler–Lagrange dynamics to illustrate the robustness of the proposed controller and estimator. [ABSTRACT FROM AUTHOR]

In this paper, based on event-triggered control, the stabilisation problem of time-varying delay switched linear system with all subsystems unstable is studied. Firstly, the idea of event-triggered mechanism is introduced, and the state feedback controller is designed. Combining the discretised linear Lyapunov function method with the average dwell time technique, the stabilisation schemes for continuous switched system with unstable modes are obtained. Secondly, compared with existing results, the event-triggered mechanism scheme adopted under the current event-triggered control strategy reduces the number of system triggers, effectively saves computational resources, avoids Zeno's behaviour, and has better performance in terms of sampling frequency. Finally, the idea of event-triggered mechanism is first extended to discrete time-varying delay switched systems where all subsystems are unstable. By solving linear matrix inequalities, sufficient conditions for global exponential stability of the system are obtained. The effectiveness of this method was verified through a simulation example of a turbofan engine model. [ABSTRACT FROM AUTHOR]

This paper studies the expected-time tracking control problem for multiagent systems with disturbances and time delays. The expected-time control scheme can ensure that control objectives can be completed within the desired time. In the other words, the output signal can track the reference signal in an expected time. Based on a special error and the adaptive technology, the expected-time tracking consensus control scheme is achieved. Moreover, by using the neural networks technique, the unknown nonlinear functions which often exist in the dynamic of the system are handled. According to the Lyapunov stability theorem and the Lyapunov–Krasovskii function, it is proved that the consensus errors are semi-globally uniformly ultimately bounded. At last, the feasibility of the control method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

The problem of robust tracking and model following is reconsidered for a class of uncertain nonlinear systems with some uncertainties and dead-zone input constraints. In this paper, the system uncertainties are regarded as some nonlinear functions which are not required to must satisfy the matching conditions. That is, being different from traditional robust control theory, the matched structures of uncertainties are sufficient, but not necessary, for being able always to design some types of robust tracking control schemes. In addition, the external disturbances of dynamical systems are assumed to be any bounded functions which is not also required to satisfy the matching condition. By making use of the integral inequality, instead of Lyapunov function, a class of robust tracking control schemes is constructed to guarantee the uniform exponential boundedness of the model tracking errors. Moreover, the upper bounds of nonlinear uncertainties are not required to be known, and it is also unnecessary to know or estimate the characteristic parameters of dead-zone functions. Therefore, the resulting robust tracking control schemes have a simple structure. Actually, the resulting robust tracking control scheme is a linear feedback control one with a self-tuning control gain. Finally, an illustrative numerical example is provided to demonstrate the validity of the presented theoretical results. [ABSTRACT FROM AUTHOR]

For switched under-actuated systems subject to bouc-wen hysteresis, an adaptive neural predefined-time control scheme is presented first time in this paper. To improve the robustness and response rate of the system under consideration, a hierarchical sliding mode control technique is introduced. Based on a predefined-time stability criterion, the stabilization time of the system can be directly preset under the designed controller. Besides, the convergence time depends only on the preset value and is independent of all other parameters. The projection algorithm is introduced to avoid the singularity problem in the controller design process. Meanwhile, it is proved that all signals of the closed-loop system are bounded after a preset time through the Lyapunov stability theory. Finally, a simulation example and a comparison example are given to further illustrate the effectiveness and superiority of developed control approach. [ABSTRACT FROM AUTHOR]

In this paper, the issue of projective synchronisation of bipartite networks (BNs) with multiple time-varying delays and nonlinear couplings has been studied. To reduce wasted communication resources, the novel quantised event-triggered controllers and the measurement error functions are introduced. In particular, the event-triggered schemes without Zeno behaviour are designed, which can determine whether controllers need to be updated or not. For the purpose of investigating the synchronisation with isolated nodes, comparing to the usual 1-norm and 2-norm, generalised $ \{\xi,1\} $ { ξ , 1 } -norm has been utilised to derive sufficient conditions. Furthermore, in the proof of the theorem, constant time-delays and time-varying time delays are dealt with by utilising novel Lyapunov functions, and then some valid criteria are established to achieve different types of projective synchronisation. Lastly, numerical simulations are well provided to confirm the given theoretical results. [ABSTRACT FROM AUTHOR]

Network inference has been extensively studied in several fields, such as systems biology and social sciences. Learning network topology and internal dynamics is essential to understand mechanisms of complex systems. In particular, sparse topologies and stable dynamics are fundamental features of many real-world continuous-time (CT) networks. Given that usually only a partial set of nodes are able to observe, we consider linear CT systems to depict networks since they can model unmeasured nodes via transfer functions. Additionally, measurements tend to be noisy and with low and varying sampling frequencies. This paper applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured nodes. A numerical sampling method, preconditioned Crank–Nicolson (pCN), is used to refine coarse-grained trajectories to improve inference accuracy. The proposed method can handle sparsely sampled data and unmeasurable nodes. Monte Carlo simulations indicate that the proposed method outperforms state-of-the-art methods with various network topologies. The developed method can be applied under a wide range of contexts, such as gene regulatory networks, social networks and communication systems. [ABSTRACT FROM AUTHOR]

In this paper, an output feedback controller using iterative learning algorithm is proposed for stabilising a class of uncertain nonlinear single-input single-output (SISO) systems with unknown bounded disturbances. By dividing the time interval into equal iteration periods, iterative learning algorithm is integrated into output feedback control and is carried out under alignment condition. Using the output signal, control input and estimates of uncertain parameters, adaptive state observers are established to generate state estimates which will be employed to design the control law. Based on the backstepping technique, the output feedback controller is developed consisting of the ILC part and robust control part. Different from the traditional ILC update laws of uncertain parameters with constant gains, a novel type of parameter update law is developed where the ILC gain is variable and determined by the real-time state estimates such that system stability can be guaranteed. By virtue of the composite energy function, it is proved that all the signals of the closed-loop system are bounded and the output will uniformly converge to zero along the iteration axis, which indicates the stabilisation of the output signal over the time domain. Simulation verification is conducted to apply the proposed controller to a one-degree-of-freedom suspension system to validate the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

In this paper, four general types of time-varying underactuated systems (UASs) are proposed and investigated with the fully-actuated system (FAS) approach. The proposed UASs consist of both the first-order and the second-order ones, and cover a very wide range of UASs. FAS models for all these types of UASs are obtained under very mild assumptions, and corresponding homeomorphism or diffeomorphism transformations and sets of feasibility are also established. Governed by the FAS theory, once a FAS model of a system is obtained, a stabilising controller for the system can then be easily designed. Particularly, in the case that the set of feasibility coincides with the whole initial value space, a global FAS model is produced for which (and also the original system) global exponential stabilisation is immediately achieved. Otherwise, a sub-FAS with a feasible set is produced for which a sub-stabilising controller can be immediately designed. Meanwhile, a subset in the system initial value space, which is called the region of exponential attraction (REA), is characterised, such that all the solutions of converted sub-FAS starting from this domain are driven exponentially to the origin within the feasible set. Furthermore, in the case that the set of feasibility, or the REA, contains the origin as only a boundary point, the system possesses a 'nonholonomic' feature. Several examples are proposed to demonstrate the proposed approach and the power of the FAS approach. [ABSTRACT FROM AUTHOR]

The problem of controllability and control of linear discrete-periodic systems is investigated in this paper. The high-order fully-actuated models for linear discrete periodic time-varying systems are constructed, and a controllability criterion based on fully-actuated system models is proposed. On this basis, stabilization as the fundamental issue is studied and periodic state-feedback control laws are designed via fully-actuated systems approach and parametric design, which converted the original problem into the pole assignment problem for linear constant systems. Finally, a numerical example is given to verify the validity and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a novel fault tolerance strategy for a class of uncertain high-order fully actuated systems (FASs) with actuator faults. Recently, the FAS approaches have been rapidly developed in different fields. As a control-oriented approach, a great advantage of FAS approaches is the convenient design of controllers for the deterministic nonlinear systems which meet the full-actuation condition. However, the presence of uncertainties and faults brings more challenges. To cope with the fault tolerance issue in FAS framework, a class of uncertain FASs with multiplicative actuator faults is introduced. Based on the Lyapunov stability theory, a fault-tolerant controller is presented to robustly stabilise the considered system. Moreover, for the systems that do not meet the full-actuation condition, previous research has indicated that they may be decomposed into a fully actuated subsystem and an extra autonomous subsystem. Leveraging the theory of input-to-state stability, the proposed fault-tolerant controller is further extended to cope with the stabilisation for such compound systems with nonlinear uncertainties and actuator faults. Both provided controllers can ensure the globally uniformly asymptotically stability of the considered systems and only rely on a few general assumptions. A numerical experiment illustrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of prescribed-time fault-tolerant control for the formation of quadrotors by utilising the fully-actuated system approach. Since not all quadrotors can directly access the virtual leader's state, a distributed prescribed-time observer is designed to estimate the state of a virtual leader. Leveraging this observer, a horizontal position controller that combines the fully-actuated system approach with the prescribed-time method, as well as altitude and attitude fault-tolerant controller, are proposed. In the design of control law, two distinct sets of controllers are designed: asymptotically stable controller and prescribed-time controller, each tailored to specific control objectives. The proposed distributed prescribed-time observer can ensure that the estimation error of the leader's state reaches zero within any specified time interval. Finally, the numerical simulation is conducted to demonstrate the effectiveness of the proposed fault-tolerant formation control algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a frequency-domain method for transforming a class of high-order linear systems with high-order derivatives of input into the corresponding full-actuated system (FAS) models. Based on the coprime factorisation theory, it is proved that under the condition that the factorisation polynomial matrix is column reduced, a direct method can be given to transform the original system into an FAS model. Regard the non-square problem faced by the direct method, two different methods are given to present the explicit expression of the transformed state. The first one is given for the case the order of system is higher than that of input, in which the system is transformed into the corresponding observability canonical form. The other one considers the opposite case, in which the model reduction method is adopted. Three numerical examples are given to illustrate the proposed theories. [ABSTRACT FROM AUTHOR]