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This paper considers the classical problems of global asymptotic stability (GAS) of nonlinear autonomous (time-invariant) systems and global uniform asymptotic stability (GUAS) of nonlinear non-autonomous (time-varying) systems. By imposing new conditions on the Lyapunov function and its derivative, the state convergence to the origin from any initial condition with bounded norm in the classical setting is extended to the initial conditions with unbounded norms in the present work. The proposed new notions of stability are called extended global asymptotic stability (EGAS) and extended global uniform asymptotic stability (EGUAS) in this paper. The proposed Lyapunov-based criteria yield the global asymptotic stability of the origin and fixed-time/predefined-time attractiveness of any selected neighbourhood of the origin. The result is that the dynamical behaviour of the state trajectories from the convergence time point of view is improved considerably. The relationship between the smoothness and input-to-state stability (ISS) properties is also studied from a quantitative point of view. Under some new sufficient conditions, it is shown that dynamical systems with smaller Lipschitz constants represent smaller ultimate bounds, and as a result, are more robust. The desirable dynamical behaviour and robustness property of the proposed stability notion makes it comparable with the finite/fixed/predefined/prescribed (exact) stable systems that are naturally non-Lipschitz at the origin. Numerical results illustrate the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates a dual-channel event-triggered model-free adaptive iterative learning control problem for unknown nonlinear systems under periodic denial-of-service (DoS) attacks. Firstly, periodic DoS attacks on the measured output of the system are modelled using the Bernoulli distribution. Then, to minimise the utilisation of system bandwidth resources, a dual-channel event-triggered mechanism is designed for the sensor-to-controller and controller-to-actuator channels. Subsequently, a dual-channel event-triggered model-free adaptive iterative learning control algorithm is introduced. The convergence of the tracking error is proven in terms of mathematical expectation using Lyapunov stability theory. Finally, the effectiveness of the proposed algorithm is verified through two simulation cases. [ABSTRACT FROM AUTHOR]

The feedback linearisation has found its striking applications in many practical situations for control problems that exploit the ideas from the linear system theory. Towards linearisation techniques for controlled nonlinear systems, operator-theoretic frameworks from functional analysis have received attentions as well. The Koopman operator is a linear, but an infinite dimensional operator. Most notably, the novelties of the paper are the following. (i) Sketching a rigorous and comprehensive proof of the Koopmanization of the controlled nonlinear system using the notion of the operator-invariant subspace and unifying the minimiser inequality formula with the Koopmanized system. The proof unfolds that the Koopmanization meets the bilinearisation in control context with the augmented state space. (ii) A formal construction of an observer encompassing the generalised Riccati equation in the Koopman setting of the original controlled nonlinear system. The Carleman version of the generalised Riccati equation is rephrased as well. (iii) Unfolding the usefulness of the Koopman observer. To achieve extensive illustrations of the theory of the paper, we construct two Koopman observers for two practical control problems, i.e. a controlled Duffing system with a cubic nonlinearity and a controlled system with a square nonlinearity. Then, we compare the Koopman observers with the Carleman observers. [ABSTRACT FROM AUTHOR]

In this paper, a synchronisation problem is studied for a class of nonlinear leader-following systems. Based on adaptive control with some algebra lemmas, we propose a distributed control scheme. This scheme ensures each follower to asymptotically synchronise with the leader. Compared with the existing works where system input powers are assumed one, the input powers considered in this paper are unknown but larger than one. Based on Graph theory, Lyapunov theory and radial basis function (RBF) networks, we design a distributed adaptive supervisory control method. It is proven that the consensus among the leader and followers are achieved and all the signals including tracking errors asymptotically converge to a small neighbourhood of the origin. Finally, simulation results are displayed to demonstrate the effectiveness of control scheme. [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 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]

This paper presents a finite-time fault-tolerant fuzzy adaptive controller for uncertain interconnected nonlinear systems in strict-feedback form. The controller addresses input saturation, state-dependent actuator faults, external disturbances, and unavailable states for measurement. To avoid any unexpected alteration due to failures, the proposed control scheme design addresses all types of actuator faults that are bias, drift, and loss of accuracy as additive faults, as well as a multiplicative fault that results in loss of effectiveness with nonaffine state-dependency. Restrictions on control gains and unmatched interconnections are eliminated, and the explosion complexity caused by recursive back-stepping designs is avoided. Fuzzy Systems approximate the unknown ideal control laws along side the acutator faults and disturbances. The stability analysis guarantees that tracking errors converge to a small compact set around the origin in finite time. Simulation results on a quad-rotor UAV and a mathematical system confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A novel design framework of adaptive disturbance observer for a class of uncertain nonlinear systems based on class $ \mathcal {K} $ K functions is proposed in this paper. In the proposed observer, the upper bounds of the disturbance and its derivatives are not required a priori and the inherent adaptive gain can be arbitrarily selected as any class $ \mathcal {K} $ K functions. It is verified that the estimation error converges into a small neighbourhood around zero asymptotically, wherein the size of the above region can be exactly suppressed to be arbitrarily small by tuning parameters. Compared to conventional methods, the proposed observer is of easy implementations with simple structure and less parameters for tuning. Comparison simulation and an illustrating example are conducted to illustrate the superiorities and effectiveness of the proposed observer. [ABSTRACT FROM AUTHOR]

This paper presents a secure containment control problem of nonlinear Multi-Agent Systems (MASs) under aperiodic Denial of Service (DoS) attacks and external disturbances simultaneously. A novel adaptive neural network (NN)-based event-triggered control is considered that uses the nonlinear estimator to predict the state of other agents. Since data access is denied during DoS attacks, the overall system switches between two modes of stable and unstable containment behaviours. Therefore, the maximum of attack duration and frequency is determined such that the overall system evolution leads to containment convergence in the presence of DoS attacks. We proposed an adaptive NN-based distributed disturbance observer to estimate external disturbances in a nonlinear system's dynamics. The state estimator predicts neighbouring agents' states, and each agent's input and event times are determined without monitoring other agents. The directed graph topology is used to determine data exchange among agents instead of an undirected graph that reduces implementation conditions. Zeno-free behaviour is also proved by analysis of the system. Eventually, the numerical simulation of the proposed approach is shown. Abbreviations: DoS attacks, Containment control of multi-agent system [ABSTRACT FROM AUTHOR]

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

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

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

This paper deals with the identification of Hammerstein nonlinear models. The system nonlinearity is not necessarily static and can be of hysteresis type. The latter nonlinearity can be backlash, switch or any other memory operator having several transient cycles, but one permanent (major) cycle. The LTI (Linear Time-Invariant) block is nonparametric and is described by its impulse response. The considered system's nonlinearity is any memory operator having one major cycle (periodic closed loop), which is bordered by any two arbitrary functions. The nonlinearity borders are not assumed to be invertible. The proposed identification algorithm can be used in static nonlinearity, e.g. polynomial nonlinearity, dead zone function or any piecewise affine functions. Unlike several previous studies, the proposed identification method is carried out using only one stage. In this approach, an input signal of piecewise constant (staircase) is used. It is shown that the estimated parameters converge to their true values. [ABSTRACT FROM AUTHOR]

This paper integrates event-triggered mechanisms and fixed-time control to address the cluster consensus problem of second-order nonlinear multi-agent systems (MASs) under the undirected communication topology. Firstly, a fractional-order-based nonlinear algorithm is proposed to obtain cluster consensus for second order MASs within a bounded settling time for any initial conditions. Secondly, to further save communication resources, double dynamic event-triggered mechanisms (DETMs), considering the dynamic parameters in the triggering conditions, are introduced, which control the communication among agents and the update of controllers asynchronously. In addition, Zeno behaviour is excluded by calculating the positive lower bound of the inter-event intervals. Finally, a simulation of Chua's circuit is given to verify the effectiveness and superiority of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the consensus of nonlinear multi-agent system based on distributed estimation and adaptive sliding mode control schemes. A strategy for distributed estimation is proposed to ensure that every follower is able to estimate the state of the leader within a finite time. Then a structurally simpler integral sliding manifold is designed to eliminate the reaching phase. Furthermore, two control protocols are formulated to ensure the reachability of sliding surface according to the estimated state of the leader. The first protocol is a discontinuous one that ensures exponential convergence of the consensus tracking error to zero, but its switching gain is difficult to be adjusted. Then an adaptive protocol is developed to adjust the switching gain automatically, which is the second control protocol. This protocol not only avoids chattering but also relaxes the dependence on certain boundaries. Finally, simulations are conducted to validate the efficacy of the employed strategy. [ABSTRACT FROM AUTHOR]

In this paper, a novel model-free adaptive bipartite consensus control scheme is proposed for unknown heterogeneous nonlinear multi-agent systems (MASs) with different input delays. Firstly, an equivalent data model is established for each agent by using the dynamic linearisation method. Secondly, based on an established equivalent data model and constructed predictive compensation algorithm, the distributed model-free adaptive bipartite consensus control scheme (DMFABCC) is designed by only utilising the measured input/output data rather than the state-space model of MASs. Next, by selecting an appropriate Lyapunov function, the convergence of bipartite tracking error is rigorously proved. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed DMFABCC scheme for achieving the bipartite consensus target. [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]

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]

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]

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]

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, 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]

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]

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 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]

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]

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]

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]

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]

This paper presents a novel application of the metaheuristic Slime Mould Algorithm (SMA) to the optimal tuning of interval type-2 fuzzy controllers. Inserting the information feedback model F1 in SMA leads to a new version of the metaheuristic algorithm, further referred to as SMAF1. The paper discusses implementation details specific to interval type-2 fuzzy controllers for the position control of processes modelled by nonlinear servo systems with an integral component and dead zone plus saturation nonlinearity. The linear PI controllers are tuned on the basis of the Extended Symmetrical Optimum method using only one tuning parameter and next fuzzified to result in interval type-2 fuzzy controllers. The optimisation requires the minimisation of a discrete-time objective function expressed as the sum of time multiplied by squared control errors, and the vector variable is the parameter vector of the Mamdani PI fuzzy controller. Experimental results conclusively illustrate the superiority of SMAF1 and SMA in comparison with other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking problem for large-scale nonlinear systems in lower triangular form by employing the technology of static gain. For one thing, in the interest of improving the fault tolerance of the concerned system, a novel adaptive compensation method is considered in the tracking strategy for dealing with actuator failures. For another, in order to alleviate the communication burden and maintain satisfactory systems performance, the control method for handling event-triggered input is also added in the tracking strategy. Besides, this paper also proves that the designed strategy can achieve the control goal for large-scale nonlinear systems with bounded disturbance. The simulation example of the machine feed system is given to verify the validity of the proposed strategy. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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