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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]

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]

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]

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]

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]

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]

Therefore, there is a clear need for innovative performance analysis and parameter design approaches to adequately realize the desired system performance and resource utilization efficiency of networked systems under coding-decoding communication mechanisms. Over the last decade, networked systems have been undergoing quick developments due mainly to their distinct merits in easy installation, maintenance convenience, and reliability assurance. [Extracted from the article]

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 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 order to reach an agreement, each agent in distributed systems needs to exchange its state information with neighbours. In this case, however, it is difficult to guarantee sensitive information will not be leaked. To tackle the problem, this paper proposes an encryption method, which does not need to compromise accuracy to privacy, nor does it need to encrypt and decrypt state information every time. Meanwhile, the method in this paper not only keeps the initial state secret, but also considers the real-time state privacy. Then, we further elaborate two aspects of the proposed method by simple proofs. Finally, simulations are presented to verify the effectiveness of our algorithm. [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]

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]

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]

In this era of information technology, information leakage and file corruption due to computer virus intrusion have been serious issues. How to detect and prevent the spread of the computer virus is the major challenge we are facing now. To target this problem, a class of virus propagation models with hybrid control scheme are formulated to investigate the dynamic evolution and prevention from a spatial-temporal perspective in this paper. Diffusion-induced Turing instability is detected in response to the computer virus propagation. The introduction of hybrid control scheme can effective suppress Turing instability and turn the propagation system back to a stable state. And then, the time delay is selected as the bifurcation parameter. If the time delay exceeds the bifurcation threshold, the propagation will be destabilised and a Hopf bifurcation will occur. The hybrid control tactic can not only regulate the occurrence of Hopf bifurcation well, but also optimise the properties of bifurcating period solutions. In the end, the correctness and validity of the theoretical results are verified via numerical simulations. [ABSTRACT FROM AUTHOR]

This paper considers the prescribed-time event-triggered consensus for a class of nonholonomic multi-agent systems (MASs) with external disturbances. Firstly, the nonholonomic chained MASs are divided into two subsystems, and a switching control scheme is introduced. Secondly, a distributed prescribed-time continuous protocol is proposed to ensure that all agents reach average consensus within a prescribed finite-time. Moreover, to reduce the communication costs, an event-triggered consensus protocol is further established and some related theorems are given. In addition, the Zeno behaviour can be excluded by choosing parameters appropriately. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

This paper investigates the almost sure and mean square convergence of a state feedback-based iterative learning control scheme for a class of discrete-time multi-input–multi-output linear systems with a direct feedback term in the presence of random data dropouts. Firstly, the realisability of the control system is considered. It is shown that the realisability of the control system is only determined by the direct feedback matrix, which has nothing to do with the input–output coupling matrix, and the direct feedback matrix to be full-row rank is enough to guarantee the system realisable. Secondly, the convergence of output tracking errors is analysed in both the almost sure and mean square senses, respectively. By the contraction mapping method combined with matrix decomposition and transformation techniques, it is strictly proved that the output sequence can converge to any given desired trajectory in the sense of almost sure and mean square, respectively, if and only if the general spectral radius condition is satisfied. Finally, an example is given to show the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, fast finite-time time-varying formation tracking control for second-order multi-agent systems with external disturbance and region constraints is investigated. The ubiquitous control input response delay is considered. A predictor-based state transformation is employed to obtain the ideal equivalent systems without control input response delay. Considering the difficulty of measuring external disturbance, a fast finite-time observer with the sliding mode technique is proposed to estimate the disturbance, which positively compensates the system. By adopting the sliding mode control strategy, a novel fast finite-time formation tracking controller is proposed. Sufficient conditions for the formation tracking control with region constraints are obtained. Numerical simulations are employed to validate the feasibility and effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with nonlinear disturbance observer (DO)-based geometric attitude fault-tolerant control (FTC) problem of quadrotor unmanned aerial vehicles (UAVs) subject to system uncertainties, exogenous disturbances, and actuator faults. A disturbance observer-based control (DOBC) framework is devised to tackle the difficulties caused by system uncertainties and external disturbances. The FTC is proposed to cope with the occurrence of actuator faults which can be modelled as a constant loss of effectiveness. The proposed design scheme can provide an effective way to analyse the stability of the closed-loop system, which can ensure the asymptotical convergence of the error. The other method considers actuator failures as part of the lumped disturbance, and the attitude tracking problem of quadrotors concerning the lumped disturbance is solved by the DO-based control method, which can ensure the exponential convergence of the error. The numerical simulations are performed to illustrate the effectiveness of the design techniques. [ABSTRACT FROM AUTHOR]

This paper studies the non-singular practical fixed-time neural adaptive control issues for multi-input and multi-output (MIMO) nonlinear systems with non-strict feedback form. Neural networks (NN) are used to estimate the unknown nonlinearities and deal with the problem of an algebraic loop. Under the framework of the backstepping control design, a practical fixed-time adaptive NN control method is developed by using the adding power integration technology. According to the Lyapunov function theory, it is proved that the closed-loop system is practical fixed-time stable, and the system can track the desired reference signal within a fixed time. Finally, the proposed practical fixed-time control method is applied to a multi-motor control platform, which proves the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

Teleoperation of manipulators has allowed extending the human skills to several areas, where the commands generated by a human operator on the leader robot are followed by a remote robot. For this purpose, position-position tracking requires a mapping given the mismatch between robot workspaces. This paper compares a pair of functions for mapping the haptic device commands to handle a remote robot, considering non-identical workspaces and a classic P+d control scheme. As part of the theoretical analysis, a Lyapunov-Krasovskii-based stability analysis is developed in steps, allowing us for determining the boundedness of velocities and coordination errors. Subsequently, the performance of the teleoperation system is compared using two mapping functions. To achieve a fair comparison of results, a test protocol is developed, where different levels of haptic device damping, time delays, and workspace scales are used to evaluate the time to complete the task and the average coordination errors. Based on the results, the mapping function influences the calculation of force feedback, stability of the system, and performance metrics. During a pick-and-place task, a reduction in coordination error is observed when the linear function is chosen, while the time to complete the task is lower when a non-linear function is considered. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive cooperative formation controller is developed to force a swarm of nonholonomic mobile robots with limited sensing ranges to move along the desired reference trajectory in a predetermined formation, while maintaining communication connectivity among the initial user-specified connected robots and guaranteeing no collision between the robots. Both kinematic models and dynamic systems with parametric uncertainties are discussed. The presence of parametric uncertainties is handled by adaptive control technique. A novel artificial potential function, which is based on a 4 times differential pseudo bump function, is constructed to achieve collision avoidance and connectivity maintenance. The control design is based on a fusion of potential function, adaptive backstepping technique, and Lyapunov synthesis. The cooperative formation controller is proven to be stable and can guarantee that there is no collision between any robots and the initial connectivity of the communication network can be always maintained. The proposed formation control approach is decentralized in the sense that the control action on each robot depends only on information from its neighbours and the desired trajectory of the group. Comparative simulation and experiment studies are performed to show the effectiveness of the proposed formation controller. [ABSTRACT FROM AUTHOR]

In this paper, demand response for isolated industrial micro-grid is investigated, in which the typical manufacturing assembly process, battery, conventional generator and PV generation are contained. The typical manufacturing assembly process is modelled by fully considering the coupling relationships between tasks and buffers. On the other hand, the uncertainty for PV generation is taken into account, where the data-driven distributionally robust chance constraints are designed and the error between day-ahead forecast and real-time operation is dealt with by the reserve capacity of conventional generator. In addition, the distributionally robust chance constraints and the expectation operator are reformulated as linear constraints, which can be solved by commercial solver directly. Finally, a case study using lithium-ion battery factory is presented to prove the superiority and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Nonlinear equation systems (NESs) are ubiquitous in scientific and engineering domains, necessitating efficient methods for discovering multiple roots simultaneously. Employing evolutionary algorithms to solve NESs has received significant attention. To simultaneously locate multiple roots in a single run, this paper proposes a differential evolution algorithm with adaptive niching and reinitialisation (DE-ANR). The proposed differential evolution algorithm integrates four strategies to address the challenges posed by NESs. Firstly, DE-ANR employs a dynamic niche size control mechanism to adjust the niche size during evolution, striking a balance between exploration and exploitation. Secondly, a lifetime mechanism is introduced to identify stagnating individuals for reinitialisation, ensuring diversity of the population and preventing premature convergence. Thirdly, an improved archive technique is employed to conserve computational resources by eliminating redundant solutions, directing the algorithm's efforts toward unexplored regions of the solution space. Finally, an adaptive strategy is introduced to dynamically adjust the scaling factor F based on the feedback of evolving population, simplifying parameter tuning. Extensive evaluations on 30 diverse NES problems and the comparison results with 12 state-of-the-art algorithms clearly show the superiority of proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the linear delayed forward–backward stochastic differential equations (D-FBSDEs). The existence of time delay leads to the infinite-dimensional problem which makes the explicit solvability more challenging. The main contribution is to propose a discretisation approach which gives the explicit solution of the D-FBSDEs and consists of the following three steps: first, we transform the continuous-time D-FBSDEs into the discrete-time form with the aid of interval partition. Second, we derive the solution of the discrete-time D-FBSDEs by applying backward iterative induction. Finally the explicit solution of the continuous-time D-FBSDEs is obtained by taking the limit to the solution of discrete-time form which is also strictly proved under continuous-time framework. The proposed approach is applicable for more general FBSDEs with delay, which would provide a complete solution to the stochastic LQ control with time delay. [ABSTRACT FROM AUTHOR]

This paper employs two scalar parameters to extend auxiliary function-based integral inequality into a new formulations, say a parametrised auxiliary function-based integral inequality. Using these formulations, the constituent signals are utilised more efficiently by exploiting the interaction between them to reduce conservatism. Numerical example for stability analysis of linear systems with time-varying delay shows the improved performance of the proposed new formulations in terms of maximum delay bounds and decision variables. [ABSTRACT FROM AUTHOR]

This paper studies the finite-time synchronisation (FTS) issue for Markovian master-slave neural networks (NNs). A weighted try-once-discard (WTOD) protocol is introduced to overcome communication constraints, and an intermittent control strategy is employed to surmount energy constraints. A WTOD and parameter dependent static feedback controller is designed for the slave neural networks. Then, sufficient conditions are developed to achieve FTS for the considered master-slave NNs, and the controller design method is obtained. Finally, a numerical example is provided to demonstrate and verify the derived results. [ABSTRACT FROM AUTHOR]

This paper emphasises the study of switched nonlinear cyber-physical systems (CPSs) subjected to input delay, actuator faults and deception attacks by a $ H_\infty $ H ∞ -based truncated predictive control design. Particularly, the predictive controller uses delayed state information in addition to the traditional feedback control to estimate the present state for the feedback. Furthermore, the proposed control design is modelled with a truncated predictive approach which is also used to compensate the effects of actuator faults and attacks that also satisfy a predefined $ H_\infty $ H ∞ index simultaneously. With the assistance of the Lyapunov stability theory, a novel set of adequate criteria are acquired in terms of LMIs which assure the asymptotical stability of the switched nonlinear CPS under prescribed predefined $ H_\infty $ H ∞ performance levels. Eventually, the proposed theoretical results are validated through two numerical examples. [ABSTRACT FROM AUTHOR]

Pole placement with specified damping and decay rate is one of the primary criteria for ensuring good transient response. This control design problem has known solutions for continuous-time systems in terms of the linear matrix inequality (LMI) region. However, a systematic solution for a discrete-time system is an open problem due to the non-convexity of the constant damping locus. This paper addresses the problem of placing the closed-loop poles of linear discrete-time uncertain polytopic systems in a constant damping region. The non-convex damping region is approximated to an elliptical segment that represents an LMI region. Criteria for designing state and output feedback (static as well as dynamic) controllers are then derived. The effectiveness of the proposed approach is demonstrated with examples including a boost converter to improve its transient behaviour in the presence of input voltage and load variations. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed event-triggered consensus tracking control problem for nonlinear multi-agent systems in the presence of intermittent attacks on both intra- and inter-agent communication channels. Intermittent attacks are characterised by their ability to manipulate the channel transmission factor to disrupt the reliability of communication. First, to address the problem that the agent model knowledge is unknown, a dynamic linearisation method is introduced to transform the nonlinear agent models into equivalent linear models that depend only on the agents' input and output data. Then, a dynamic event-triggered mechanism is developed to reduce communication transmission, which is based on individual agent information rather than consensus errors to avoid the impact of attacks occurring in the communication between agents. Building upon the attack model and the equivalent linear data model, a distributed model-free adaptive control scheme with dynamic event-triggering is devised to ensure that the consensus errors of all agents are ultimately bounded, even when intermittent attacks occur. The proposed scheme's effectiveness is demonstrated through numerical simulation. [ABSTRACT FROM AUTHOR]

In this paper, a novel distributed event-triggered mechanism (ETM) is used to solve the problem of the leader-following mean square consensus of stochastic switched multi-agent systems (SSMASs) with switching dynamics of agents, disturbed by the multiplicative noise. Moreover, the distributed ETM utilises only the state information at the triggering instants of neighbouring agents, which eliminates continuous communication among agents while reducing the frequency of controller updates. A general agent-dependent switching law is developed to achieve leader-following mean square consensus of SSMASs. Subsequently, it is proved that under the distributed ETM, linear SSMASs can achieve leader-following mean square consensus and no Zeno behaviour occurs with the help of stochastic analysis and the average dwell time approach. Finally, the effectiveness of the study is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of estimating a stochastic signal from nonlinear uncertain observations with time-correlated additive noise described by a first-order Markov process. Random deception attacks are assumed to be launched by an adversary, and both this phenomenon and the uncertainty in the observations are modelled by two sets of Bernoulli random variables. Under the assumption that the evolution model generating the signal to be estimated is unknown and only the mean and covariance functions of the processes involved in the observation equation are available, recursive algorithms based on linear approximations of the real observations are proposed for the least-squares filtering and fixed-point smoothing problems. Finally, the feasibility and effectiveness of the developed estimation algorithms are verified by a numerical simulation example, where the impact of uncertain observation and deception attack probabilities on estimation accuracy is evaluated. [ABSTRACT FROM AUTHOR]

This paper studies the periodic event-triggered bipartite containment control problem of multi-agent systems with unmeasurable states and input delays. To deal with the system input delay problem, a Pade approximation technique is introduced. By utilising the sampled output of the system, a fuzzy state observer is constructed to estimate the unknown state. A periodic event-triggered mechanism is designed, which can effectively reduce the usage of communication resources. Compared with the traditional event-triggered control manner, a periodic event-triggered control manner only monitors the preset threshold condition at the sampling instant to determine whether to update the current control signal, which can automatically guarantee the minimum lower bound of the execution interval, avoiding the occurrence of Zeno behaviour. Stability analysis verifies that all signals in the closed-loop system are bounded, and the bipartite tracking error can converge to a small neighbourhood of the origin. Finally, a practical example further verifies the effectiveness of the designed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the $ H_{\infty } $ H ∞ consensus problem of stochastic nonlinear multi-agent systems with time-delay, Markov jump and $ (x, u, v) $ (x , u , v) -dependent noises is studied. Firstly, the $ H_{\infty } $ H ∞ consensus problem is transformed into a standard $ H_{\infty } $ H ∞ control problem by model transformation. Then, a dynamic output feedback control protocol is constructed by solving a set of linear matrix inequalities to ensure that the closed-loop system achieves the mean square consensus and meets the specified $ H_{\infty } $ H ∞ performance level. After that, both delay-independent and delay-dependent stochastic bounded real lemmas are established by taking advantage of the Lyapunov–Krasovskii function method and the generalised It $ \hat {\rm {o}} $ o ^ formula. Finally, we illustrate the validity of the developed method with numerical simulations. [ABSTRACT FROM AUTHOR]

To guarantee the stability and performance of an overall heterogeneous multi-agent system, feedback control structures must be capable of addressing the presence of heterogeneous agent-based nonlinear uncertainties, unknown actuation performance, and coupling effects. Additionally, some tasks require agents to track the leader's state and reach different states in a distributed way. Therefore, this paper proposes a distributed adaptive controller that drives a set of agents to user-assigned positions in the presence of the mentioned heterogeneous system anomalies. To address actuator dynamics, a hedging-based reference model is used to decouple adaptive updating from the actuator dynamics. To deal with the coupled dynamics, an observer-based estimation algorithm is proposed. Finally, the low-frequency learning method is used to deal with the high-frequency control response and, therefore, to obtain a better transient performance. The overall closed-loop system's stability is investigated using the Lyapunov Stability Theory, and the Linear Matrix Inequalities (LMI) technique is employed to identify stability limits regarding the actuator bandwidths of each agent. Illustrative numerical examples are presented to demonstrate the designed controller's performance in a heterogeneous uncertain multi-agent system's dynamics with unknown actuation capabilities and coupled dynamics. [ABSTRACT FROM AUTHOR]

This paper deals with the reliable finite-frequency $ H_{\infty } $ H ∞ control problem for switched systems with actuator faults and mode-dependent average dwell time (MDADT). The objective of designing reliable controllers is to guarantee the stability and robustness of the closed-loop system in the case of actuator faults. The actuator fault is solved using the convex combination method. A finite-frequency $ H_{\infty } $ H ∞ performance index is first defined in the frequency domain, and then a finite-frequency performance condition is derived by the generalised Kalman-Yakubovi $ \breve{c} $ c ˘ -Popov (GKYP) lemma. Combined with the MDADT approach and linear matrix inequality technology, a sufficient condition for the existence of a set of reliable finite-frequency $ H_{\infty } $ H ∞ controllers is derived. As a comparison, a sufficient condition is also given for the existence of a set of standard finite-frequency $ H_{\infty } $ H ∞ controllers. Finally, the effectiveness and feasibility of the proposed method are verified using a tilt-rotor aircraft. In addition, simulations and comparisons show that the designed reliable controller can stabilise the system even if actuator faults occur. [ABSTRACT FROM AUTHOR]

This paper studies the input-to-state stability (ISS) and uniform stability for impulsive nonlinear systems with delayed impulses, where delays are flexible between two consecutive impulsive instants. Illustrating effects caused by delays, concepts of generalised average impulsive delay and generalised reverse average impulsive delay are introduced, and more applicable in practice. Moreover, sufficient conditions for stability properties are formulated with destabilising and stabilising impulses through a general candidate ISS-Lyapunov function and the generalised conditions. It is shown that the double effects of delays in impulses (i.e. stabilising an initial unstable system, or contrarily, destabilising an initial stable system) coincide with the existing results using the exponential candidate ISS-Lyapunov function. Finally, two examples are provided to demonstrate our developed techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven predictive iterative learning control (DDPILC) for nonlinear nonaffine systems. First, we develop an iterative predictive model (IPM) where an iterative dynamic linearisation technique is introduced for addressing the strong nonlinearities and nonaffine structure. Then, an auto-regressive model is employed to estimate the unavailable parameter of IPM along with the iteration direction. Next, the outputs in the future iterations are predicted pointwisely over the finite operation interval that are further incorporated into the optimal objective function to obtain the optimal control input sequence. In addition, a constrained-DDPILC is extended for systems with I/O constraints which are reformulated as a linear matrix inequality (LMI). The two proposed methods do not have model requirement except for I/O data. Simulation study verifies the results. [ABSTRACT FROM AUTHOR]

For nonlinear cyber-physical systems (CPSs) with input saturation and denial-of-service (DoS) attacks, an adaptive secure control strategy is investigated in this paper. To estimate the unmeasurable states, a novel switched neural network (NN) observer is designed, where two sub-observers can be freely switched from each other. Considering unnecessary packet transmissions, an improved event-triggered mechanism (ETM) is introduced to reduce the communication burden in the controller-to-actuator channel. In addition, in order to simultaneously solve the input saturation and unknown control direction problems, a Nussbaum-type function is introduced to the control design process. Finally, it is proven that all closed-loop signals are the semiglobal uniformly ultimately bounded, and simulation results demonstrate the effectiveness of the proposed secure control scheme. [ABSTRACT FROM AUTHOR]