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This paper focuses on the consensus tracking control problem of multi-agent systems (MASs) with arbitrary adjacency weights instead of traditional nonnegative weights in a sampling setting. First, unlike Lemma 4 in Hu and Hong [2007. Leader-following coordination of MASs with coupling time delays. Physica A: Statistical Mechanics and its Applications, 374(2), 853-863], for MASs with arbitrary weights, the global reachability of the leader node is just a necessary but not a necessary and sufficient condition to guarantee the positive stability of matrix H. Hence, it's urgent for us to establish some positive stability criteria of matrix H first, which is a necessary condition for MASs to achieve consensus tracking. Simultaneously, we also solve the following problems successfully: which nodes should have direct connection with the leader? What's the range size of the leader adjacency coefficients? Then, some sufficient consensus tracking control conditions are obtained for MASs without time-delays and with time-delays by using matrix analysis method and perturbation theory, respectively. Finally, numerical examples are given to illustrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

Conventional yaw stability strategy of distributed drive electric vehicle (DDEV) is usually realised by torque distribution strategy. However, the instantaneous variations of four independent tyres slip ratio and the effect of disturbance have not been considered sufficiently. Therefore, it is difficult to realise the robustness of yaw stability for DDEV under various operating conditions. To solve this problem, a novel model predictive controller-based compensation control system (MPC-CCS) is proposed in this paper. The proposed MPC-CCS consists of two parts, an MPC based-feedback controller and a Kalman filter based-feedforward controller. In the feedback controller, a dual torque distribution scheme is adopted to obtain optimal torque values derived from the real-time signals of four independent tyres slip ratio, an MPC is designed to realise optimal torque values for vehicle yaw motion. In the feedforward controller, a Kalman filter is employed to attenuate the effect of the disturbance on yaw performance. In this way, the robustness of yaw stability for DDEV can be guaranteed by the proposed MPC-CCS. The proposed MPC-CCS is evaluated on eight degrees of freedom simulation platform and simulation results of different conditions show the effectiveness of the MPC-CCS. [ABSTRACT FROM AUTHOR]

This paper studies the problem of dynamic output feedback H ∞ control for continuous-time Takagi–Sugeno fuzzy systems. Based on the fuzzy Lyapunov functions without the special structure, a controller design method is developed and the relaxed stabilisation conditions are proposed. First, some shortcomings of the existing results are improved and a new approach is proposed for bounding the time derivatives of the membership functions. Then, less conservative conditions are obtained by introducing regulable parameters and applying the matrix decoupling techniques, which guarantee the prescribed H ∞ performance. Moreover, the relaxation technique proposed in this paper is also suitable for the fuzzy system with immeasurable premise variables. Finally, some examples are provided to demonstrate the validity of the approach. [ABSTRACT FROM AUTHOR]

This paper studies the robust static output feedback control of discrete-time Markov jump systems considering that the mode of operation cannot be directly measured. It is assumed that the only available information concerning the main jump process comes from a detector, so that the jump and detector processes can be modelled as a hidden Markov model. Initially, it is considered that the system's dynamic matrix is subject to parametric uncertainties. By assuming a full row rank condition for the output matrix and employing a clusterisation technique for the state space of the hidden Markov model, it is derived a set of linear matrix inequalities (LMIs) that guarantees robust stability in the mean-square sense of the closed-loop system, as well as a bound on its H 2 norm. It is also shown that this design technique is linked to the bounded real lemma, so that a new set of LMIs conditions can be obtained for the 'pure' H ∞ control problem, which can be extended to cover the robust polytopic case. The paper is concluded with an illustrative example. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of distributed fusion over sensor networks with probabilistic constraints and stochastic perturbations. In order to save the bandwidth resources, a new event-triggering mechanism (ETM), called torus-event-triggering mechanism (TETM), is utilised for data transmission. Compared with the traditional ETMs, the TETM has two thresholds, which will not only discard the sampling data smaller than the lower threshold but also hold back the packet larger than the upper threshold. The main purpose of this paper is to design a time-varying distributed fusion filter such that: (1) the probability of the filtering error falling in a given ellipsoid domain is greater than a specified value and (2) the ellipsoidal set is minimised in the sense of matrix norm at each time point. To achieve the above-mentioned purpose, sufficient conditions are given to obtain the global fusion with the help of the recursive linear matrix inequality technique. The desired local filter parameters are then computed by solving an optimisation problem with some inequality constraints. Finally, a numerical simulation is given to illustrate the effectiveness and applicability of the proposed distributed fusion strategy. [ABSTRACT FROM AUTHOR]

This paper considers the problem of the semi-global asymptotic stabilisation of fractional-order (FO) linear systems subject to actuator saturation by output feedback. To solve the problem, a family of observer-based linear output feedback laws, parameterised in a positive scalar, is proposed by means of the low gain feedback design technique. The design applies to FO linear systems that are stabilisable and detectable, but not exponentially unstable. For such an FO system under the proposed observer-based linear low gain output feedback, the peak value of the control input for a given initial condition can be made arbitrarily small to avoid actuator saturation by decreasing the value of the parameter towards zero and thus semi-global asymptotic stabilisation is achieved. To obtain these results, we establish the properties of low gain feedback, derive asymptotic expansions and the bounds of high-order derivatives of the Mittag-Leffler (ML) functions to estimate the state responses of FO linear systems, and explicitly construct a Hermitian matrix to satisfy a linear matrix inequality (LMI) stability condition for FO linear systems. The results in this paper extend the corresponding results for integer-order (IO) linear systems. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of multiple agents circumnavigating group targets within finite time. Unlike most existing works in which agents can circumnavigate with a regular-shaped trajectory in the mission area, the problem of agents with an irregular-shaped trajectory in the area with obstacles is addressed in the paper. The agents can only obtain the bearing information of the targets. First, a scheme for establishing the group targets as an extended convex hull is presented. And the boundary of the extended convex hull is taken as the scheduled irregular-shaped trajectory. Then, a finite-time estimator is presented to localise the positions of the targets and their geometric centre. Afterward, a finite-time control protocol is proposed to drive agents along the irregular-shaped trajectory within finite time. Subsequently, an obstacle avoidance method is provided for agents to avoid obstacles on the path to the expected trajectory. Finally, simulation results validate the effectiveness of the theoretical results in this paper. [ABSTRACT FROM AUTHOR]

Wiener model structures have attracted considerable attentions due to their powerful modelling abilities to solve industrial problems. This paper considers a new class of Wiener model structure, which consists of a linear transfer function and a radial basis function (RBF) network. Based on this structure, the unstable zero-dynamics, the added disturbances and the approximation errors problems can be dealt with. Since the model parameters are assumed unknown, we develop a new indirect adaptive control scheme. It is a consensus that the certainty equivalence principle is the key that connects the adaptation algorithm with the control law. This paper explains that, for indirect adaptive control of Wiener model, this principle may not be applied directly. The main contribution of this paper is to address this issue by presenting a rigorous theoretical analysis. Representative examples including a simulated pH process control problem are studied to test the control performance. Comparison results indicate that the proposed controller has much wider applicability than some alternative methods, especially for its improved performance and smoother adaptation. [ABSTRACT FROM AUTHOR]

In this paper, a novel state-dependent switching controller with delayed positive partial state feedback is proposed to stabilise a controlled system with insufficient state information. At first, the controlled system is converted to a single-degree-of-freedom vibrational system by using an invertible transform and a second-order Taylor approximation for the time delay term. Then, appropriate control gain parameters and a state-dependent switching rule are designed via the physical meaning of the resulting vibrational system. By combining the advantages of delayed positive position feedback with the state-dependent switching mechanism, we find that the time delay can considerably improve the asymptotic stability of the controlled system. Finally, three numerical examples show that the proposed controller is effective and that time delay indeed plays a prominent positive role in switched control systems. [ABSTRACT FROM AUTHOR]

This paper investigates the bipartite output containment control problem by utilising distributed control protocols for heterogeneous multi-agent systems over directed signed graphs. A fully distributed dynamic compensator is proposed to estimate the convex hull and symmetric convex hull spanned by states of leaders. Then, a distributed observer based on neighbour information is presented for estimating the state of each follower. An output feedback control protocol is further given based on the fully distributed dynamic compensator and neighbour-based observer to cope with the bipartite containment control problem of the linear heterogeneous multi-agent systems. Different from the related works, a neighbour-based observer is presented for heterogeneous multi-agent systems, in which the analysis method used in the Luenburger observer cannot be applied directly, because of the heterogeneity. Besides, the dynamics of the agents are allowed to not only have nonidentical coefficient matrices but also have different dimensions. A simulation example is shown to verify the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In recent years, the security issues of cyber-physical systems (CPSs) have attracted extensive attention from researchers. In this paper, a novel window-type detector (WTD) is designed for stealthy false data injection (FDI) attack targeted at CPSs. In the absence of attack, the evaluation value of a WTD is χ 2 distributed because of the existence of Gaussian noise. In the presence of attack, the evaluation value of the WTD is the result of accumulating the residual data caused by attack signals, which is similar to the mechanism of a summation (SUM) detector. It is proved that when the duration of system operation is much larger than the attack duration squared, evaluation value of an SUM detector is χ 2 distributed due to the huge number of tolerable historical data. And the structure of finite window size prevents the WTD from failure in such a situation, which equips it with better performance in the detection of attacks with medium duration. Furthermore, it is proved that the closer the window size is to the attack duration, the larger the evaluation value is, which means that the better detection performance could be achieved. Finally, numerical examples are given to verify the characteristics of the WTD. [ABSTRACT FROM AUTHOR]

In this paper, the leader–follower consensus problem is investigated for a class of lower-triangular nonlinear multi-agent systems with unknown measurement sensitivities. By developing a dual-domination gain method, a distributed compensator is proposed for each follower by utilising the output information of the follower and its neighbour agents. Based on the compensator, an output feedback control law is designed to achieve consensus. These two gains are used to deal with the unknown measurement noises and nonlinear terms, respectively. Then the consensus problem is transformed into a stability problem by introducing an appropriate state transformation. Based on the Lyapunov stability theory, it is proved that the states of the leader and followers can achieve consensus asymptotically. In the end, numerical simulations are provided to verify the correctness of the proposed consensus algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the parameter estimation problem for the multivariate system disturbed by coloured noises. Since coloured noises will reduce the estimation accuracy, the model transformation technique is employed to whiten the original system without changing the input-output relationship. In order to alleviate the heavy computational burden caused by high-dimensional variables and different types of parameters, the transformed model is divided into several sub-models according to the numbers of outputs. However, after the decomposition, all the sub-models contain a same parameter vector, resulting in many redundant estimates. A model transformation based distributed stochastic gradient (MT-DSG) algorithm is derived to cut down the redundant estimates and exchange the information among the sub-models. Compared with the centralised multivariate generalised stochastic gradient algorithm, the MT-DSG algorithm has more accurate estimates and less computational complexity. Finally, an illustrative example is employed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The fraction model has been widely used to represent a range of engineering systems. To accurately identify the fraction model is however challenging, and this paper presents a regularised fast recursive algorithm (RFRA) to identify both the true fraction model structure and the associated unknown model parameters. This is achieved first by transforming the fraction form to a linear combination of nonlinear model terms. Then the terms in the denominator are used to form a regularisation term in the cost function to offset the bias induced by the linear transformation. According to the structural risk minimisation principle based on the new cost function, the model terms are selected based on their contributions to the cost function and the coefficients are then identified recursively without explicitly solving the inverse matrix. The proposed method is proved to have low computational complexity. Simulation results confirm the efficacy of the method in fast identification of the true fraction models for the targeted nonlinear systems. [ABSTRACT FROM AUTHOR]

A finite-time adaptive control is discussed in this paper for stochastic nontriangular nonlinear systems with unknown backlash-like hysteresis. The performance function is introduced to ensure that the tracking error of the system converges to the specified region in a finite time. Secondly, a controller is proposed using the fuzzy logic systems to approximate the unknown functions and backstepping technique. In addition, adding the dynamic event-triggered mechanism is able to reduce the network resources more effectively. At last, a few simulation examples are presented to demonstrate effective performance. [ABSTRACT FROM AUTHOR]

In this paper, the output feedback control scheme is developed to investigate the exponential stabilisation problem for a class of strict-feedback nonlinear systems subject to different time-delay states. First of all, the memoryless full-order observer and output feedback controller which depend on the same designed parameter are provided respectively. And then, by tactfully introducing the suitable Lyapunov–Krasovskii functional and selecting the applicable parameter to be designed, the exponential stability of the closed-loop system can be achieved. Finally, a numerical example is used to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider a problem of global stabilisation of approximate feedback linearised systems with perturbed nonlinearity by an event-triggered control. We categorise the perturbed nonlinearity into three cases and obtain a characterisation function for each case. Then we propose an event-triggered controller with a gain-scaling factor along with an event-triggering condition which is developed based on the characterisation function. Utilising the system analysis results, we suggest a control parameter selection rule depending on the priority on either system output regulation speed or size of the interexecution times. Via practical examples, we demonstrate the validity of our control approach. [ABSTRACT FROM AUTHOR]

In this paper, an interval type-2 fuzzy model is presented to describe the nonlinear singular fractional order systems (SFOSs) with 0 < α < 2. The problem of actuator and sensor fault estimation is addressed by designing a non-fragile sliding mode observer (SMO). Firstly, a new criterion is provided to analyse the admissibility of SFOSs, which does not need to divide the fractional order interval (0 , 2) into interval (0 , 1) and interval [ 1 , 2) to study them separately. Then, by expanding the dimension of the SFOS, the non-fragile SMO and the sliding surface are constructed and the accurate estimations of faults and the SFOS state simultaneously are achieved. A new reaching law is designed, which can divide the process of the system moving to the sliding surface into three stages. Further the control law is constructed to guarantee the observation error reaches the sliding surface in finite time. Finally, three examples show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Chemical reactors, heat exchangers, level-loops, and distillation columns are commonly encountered in the chemical process industry, and these are having a significant role in the functioning of any chemical process. For the smooth and efficient functioning of any such process, a robust control law design is an important work to be considered. In this paper, a modified 2DOF robust PID control law has been developed to ensure satisfactory performance in both the step and ramp type references and disturbances. Controller parameters are derived with Maximum sensitivity, Gain, and Phase margin specifications. Case studies with different chemical processes like CSTR, distillation column, heat exchangers, and boiler drum level-loop are included for both the nominal and perturbed cases. Lastly, a performance comparison of ISE (Integral of square error), ITAE (Integral time absolute error), IAE (Integral absolute error), and Total variation (TV) of the control signal is also included. [ABSTRACT FROM AUTHOR]

Diffusion processes, as fundamental mechanisms for particle movement in systems with different concentrations, are used to describe many real-world physical, chemical, biological, engineering, economic and social phenomena. A diffusion process can be modelled via a fractional-order transfer function with time-delay, where its parameters may be affected by circumstance. Hereupon, this study proposes a pioneer robustness indicator to achieve the phase margin invariance regardless of concurrent uncertainty on different parameters of a diffusion process. Afterwards, an analytical procedure is suggested to tune a Fractional-Order Proportional-Integral-Derivative (FO-PID) controller for a diffusion process, to favourably regulate the values of gain crossover frequency and phase margin, such that the proposed robustness criterion is met. Moreover, the solvability of the problem is analytically investigated. Finally, a numerical simulation on robust temperature control during magnetic local hyperthermia, i.e. a common method to treat cancerous tumours, is presented to validate the efficiency of the paper achievements. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed-time control problem for a class of wheeled mobile robot (WMR) subject to nonparametric skidding, slipping and input disturbance in an inner-outer loop framework. First, by backstepping approach, the virtual linear and angular velocities are acquired to follow the reference path to one special point. Then, different from the conventional ones for canonical integral cascade model, a novel prescribed-time extended state observer (PTESO) is designed for WMR such that the unknown skidding and slipping can be observed and compensated. By developing the active disturbance rejection control technique, the zero-error control is achieved for WMR in prescribed time instead of ultimately uniform boundedness. Moreover, the influence of the uncertainty is attenuated by the compensation scheme based on the PTESO estimation. Finally, some simulation results are presented to demonstrate the superiority and effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper investigates the optimisation problem of second-order multi-agent systems. Distributed optimisation algorithms are proposed based on sampling data. Two kinds of sampling techniques, namely, aperiodic sampling and dynamic event-triggered sampling, are utilised. Moreover, the proportional integral (PI) strategy is utilised in the proposed algorithms. Compared with the existing distributed optimisation algorithm based on periodic sampling, the proposed algorithm dependent on aperiodic sampling is more general. Compared with the existing steady event-triggered algorithm, the distributed optimisation algorithm based on dynamic event-triggered sampling has the merit of lower energy consumption. Under the assumption that the global cost function is strongly convex about global minimum point, it is proved that the proposed algorithms solve the optimisation problem. Lyapunov stability theory is applied to give sufficient criteria guaranteeing convergence to optimal point. Finally, the effectiveness of the proposed algorithms is illustrated by numerical simulation. [ABSTRACT FROM AUTHOR]

The presence of noise results in the decrease of the data quality, and thus affects seriously the learning performance. In this paper, we investigate theoretically and empirically how adjusting the temperature value of the softmax function to control the prediction uncertainty can help improve the model robustness to noise. Moreover, we design a modified loss function, where a regularisation term based on the uncertainty of distinguishing those non-ground truth labels is incorporated in the loss function to simultaneously improve the training accuracy and the model robustness to noise. This study theoretically and experimentally validates that the temperature parameter of the softmax function significantly impacts the classification accuracy and the proposed loss function is effective in improving the performance in the presence of noise. The study provides some practical guidelines for improving classifier performance through clearly expressing and understanding the relationship between uncertainty and noise robustness. [ABSTRACT FROM AUTHOR]

This paper addresses a non-fragile robust model predictive control design for a class of continuous-time uncertain systems with multiple state-delay and constrained control signals. The parameters of the system and the control gain are assumed to have perturbation in the additive form. The Lyapunov–Krasovskii functional approach is employed to derive sufficient conditions for determining a non-fragile robust state-feedback controller for all admissible uncertainties by minimising the upper bound of the defined quadratic cost function with respect to some linear matrix inequalities (LMIs). An additional inequality condition is imposed to address the constraint associated with the control signals. Numerical simulations are provided to assess the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper proposes a nonparametric identification method based on Gaussian process regression (GPR) for completely unknown nonlinear distributed parameter systems (DPSs). Inspired by linear parameter-varying (LPV) modelling approach, an interpolated spatio-temporal Volterra model is developed to represent the DPSs in nonparametric form, in which local Volterra models are interpreted as Gaussian processes. According to the empirical Bayesian approach, we design the third-order stable kernel structure used for embedding prior knowledge and derive the estimation of hyperparameters. The hyperparameters included in local weighting functions and kernel functions are determined by the maximum likelihood method. By utilising the nonparametric identification approach to avoid model structure selection, the proposed method can improve identification result for completely unknown distributed parameter systems. Finally, two case studies validate the effectiveness of the proposed identification method. [ABSTRACT FROM AUTHOR]

A test-based model-free adaptive iterative learning control algorithm (TB-MFAILC) with strong robustness is proposed in this paper. The algorithm improves the situation where existing model-free adaptive iterative learning control algorithms fail to converge or converge relatively slowly in noisy environments. Also, this work demonstrates the convergence and robustness of the proposed algorithm in different environments. Subsequently, the effectiveness of the proposed algorithm is illustrated by numerical comparison simulations with the existing model-free adaptive iterative learning control algorithm and the PD-based adaptive switching learning control algorithm in noisy environments. Finally, the advantages of the proposed algorithm are further illustrated through the analysis of relevant parameters. [ABSTRACT FROM AUTHOR]

This paper investigates the stability for a class of distributed-order nonlinear systems via event-triggered control method. First of all, an inequality of the solution is established for distributed-order nonlinear inequality systems by employing Laplace transform. And then, by designing an appropriate state feedback controller and event-triggered strategy, and using Lyapunov stability theory and matrix inequality technique, a sufficient condition to ensure the asymptotic stability of the considered distributed-order nonlinear systems is obtained in the form of linear matrix inequality. Moreover, a criterion to exclude Zeno behaviour in event-triggered strategy is provided. Finally, the feasibility and effectiveness of the proposed method are verified by a simulation example. [ABSTRACT FROM AUTHOR]

Control of nonlinear systems with time-varying delays is a common problem, particularly when the time-varying delays are unknown. This paper presents an adaptive compensation control method based on multiple neural networks. Firstly, considering the nonlinear systems with time-varying delays, a neural network method is adopted to approximate the unknown nonlinear structure with time-varying delays. On this basis, a backstepping controller is designed. Considering the unknown time-varying delays, the control effect of the neural network controller based on the backstepping method is not optimal. To deal with the problem, a double-layer adaptive compensator for the neural network is proposed. Furthermore, for the neural network adaptive compensation controller based on the backstepping method, the stability is also proved by a Lyapunov method. Finally, simulation results demonstrate the effectiveness of the scheme, especially when the nonlinear systems have an unknown time-varying delay. [ABSTRACT FROM AUTHOR]

This paper considers a multi-period location and sizing problem for an emergency medical service (EMS) system based on a distributionally robust optimisation (DRO) chance-constrained programming approach. The dynamic uncertain emergency medical requests are described in the ambiguity set, which is constructed based on Wasserstein-metric. The model of this problem focuses on minimising long-term operation costs. The chance constraints ensure the reliability of EMS system for the entire geographic areas. A reformulation of chance constraints is provided in Mixed Integer Linear Program form. For problem solution, a generalised Benders decomposition (GBD) implementation is proposed. A numerical simulation is conducted to illustrate the performance of two solution approaches in terms of computational convergence speed and optimality of the problem. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with distributed finite-time optimisation of multi-agent systems with unknown high-frequency gain signs under switching digraphs. First, a distributed finite-time gradient estimator-based optimal signal generator is constructed by adopting finite-time stability theory and graph theory. Then, considering the existence of unknown high-frequency gain signs in multi-agent systems, we propose new distributed asymptotic optimisation algorithms via the Nussbaum-type function. Additionally, in order to accelerate convergence, the novel distributed finite-time optimisation algorithms are presented. Both of the newly proposed algorithms can realise optimal consensus of multi-agent systems with unknown high-frequency gain signs under switching digraphs. Finally, two simulation examples are illustrated to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the decentralised tracking control (DTC) problem is investigated for a class of continuous-time large-scale systems with external disturbance by utilising adaptive dynamic programming (ADP). Firstly, the DTC problem is solved by designing corresponding optimal controllers of the isolated subsystems, which are formulated with N augmented subsystems consisting of the tracking error and the reference trajectory. Then, considering the external disturbance, we can effectively construct the DTC scheme by means of adding suitable feedback gains to the optimal control strategies associated with each augmented tracking isolated subsystems (ATISs). Due to the approximate nature, a series of critic neural networks are constructed to solve the Hamilton–Jacobi–Isaacs equation, so as to derive the estimation of the Nash equilibrium solution containing the optimal control strategy and the worst disturbance law. Herein, a modified weight updating criterion is developed by employing a stabilising term. Consequently, we remove the requirement of initial admissible control in the proposed algorithm. After that, stability analysis of the ATIS is performed through the Lyapunov theory, in the sense that tracking states and weight approximation errors are uniformly ultimately bounded. Finally, an experimental simulation is demonstrated to ensure the validity of the proposed DTC scheme. [ABSTRACT FROM AUTHOR]

In this paper, the mixed H 2 / H ∞ guaranteed cost for continuous-time linear parameter-varying (LPV) systems is proposed. In addition, the gain scheduling (GS) strategy and the state derivative feedback (SDF) were considered. The time-varying parameter is assumed to be available for online measurement, and the conditions are based on linear matrix inequalities (LMIs). To improve the behaviour of the system, the D -stability is used. Examples show that the proposed conditions achieved good results in reducing the disturbance effect in the system. Furthermore, with the proposed methods, it is possible to guarantee the asymptotic stability and a mixed H 2 / H ∞ guaranteed cost minimisation. [ABSTRACT FROM AUTHOR]

This paper considers a constrained co-design problem for linear time-invariant (LTI) systems. With a practical vision of real-world problems, constraints on the control signal and system states are involved in the proposed algorithm. The main goal is to design a sub-optimal controller by simultaneously obtaining the control policy and the model parameters. To this end, the conventional problem of solving the Hamiltonian-Jacobi-Bellman (HJB) equation is transformed into a nonlinear non-convex optimisation problem. Then, by reformulating the constraints of the optimisation problem, it is relaxed into a convex Semi-Definite Programming (SDP). By presenting an iterative method, the control cost, the performance, and the number of iterations are improved compared to conventional methods, and a closer result to the optimal solution is obtained. The performance and efficacy of the proposed algorithm are investigated through a case study on the physical load positioning system. [ABSTRACT FROM AUTHOR]

This paper concentrates on the study of the adaptive fast finite-time control method for a class of nonlinear time-delay systems with unknown control directions and dead zone. Fuzzy logic systems (FLSs) are employed to handle the problem of uncertain nonlinear terms. The Nussbaum function is utilised to deals with the problem of unknown control directions. Based on the fast finite-time stability theory, a novel adaptive fast finite-time fault tolerant control scheme is proposed under the framework of adaptive backstepping control. Choosing an appropriate Lyapunov-Krasovskii function, it is theoretically proved that the proposed control scheme can not only ensure that the control system is semi-global practical fast finite-time stable but also the tracking error converges to a small neighbourhood of the origin in a fast finite-time. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The problem of finite-time mean-nonovershooting control is investigated for a class of stochastic nonlinear systems in this paper. For general stochastic strict-feedback nonlinear systems, a new controller is designed, which uses the framework of the backstepping technique, to assure that the mean of overshoot can be adjusted arbitrarily small (by changing controller gains) while all the states of the closed-loop system in finite-time to get bounded in probability. In contrast with corresponding deterministic systems, it has better performance that control gains did not rely on the initial conditions of system states and derivatives of the reference trajectory. Finally, a simulation example is showing the appealing performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Liquid crystal elastomer-based actuators (LCEAs) exhibit large, reversible deformation, which demonstrates superior advantages in developing remote controlled, light-weighted soft robots. However, deformation of LCEAs undergoes complicated physical dynamics and exhibits highly nonlinear hysteretic characteristics, posing challenges in the realization of related applications. It is necessary to develop a dynamic model that captures the characteristics of LCEAs. With this purpose, this paper proposes a dynamic modelling approach for LCEAs to provide a hybrid model for LCEAs with reduced computational complexity and high precision. In the research, a modelling scheme based on a physics-phenomenon-combined approach for LCEAs is proposed, which considers the elastic free energy, the nematic free energy and dissipation of the LCEA. Physics-based models are then developed to describe these energies. Meanwhile, a phenomenon-based model is proposed to characterise the hysteresis as a component of the nematic free energy. The above models are combined together to provide the final hybrid model for the LCEA. To verify the proposed model, a photo-responsive LCEA experimental platform is established and experiments are conducted. Model parameters are identified based on the nonlinear least-squares method. The experimental results demonstrate the proposed model as an excellent representative to characterise LCEAs' dynamic behaviours. [ABSTRACT FROM AUTHOR]

This paper is concerned with the observability of linear stochastic impulsive systems with multiplicative noise. The concept of 2pth moment observability is first introduced. Then, to explore the moment observability, a deterministic expanded system is established for the linear stochastic impulsive systems by the generalised H -representation technique. It shows that the linear stochastic impulsive system is 2pth moment observability if and only if the expanded system is completely observable. Subsequently, a series of algebraic criteria and geometric criteria for 2pth moment observability of linear stochastic impulsive systems are established by analysing the solution of the expanded system. The impulsive observer-based stabilisation problem for linear stochastic systems is further studied based on these observability criteria. Finally, two numerical examples illustrate the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates a distributed active disturbance rejection containment control problem for stochastic nonlinear multiagent systems under a directed topology. First, a class of nonlinear extended state observers predicated on the fractional power functions are proposed and extended state estimations are employed to compensate completely unknown stochastic terms and total disturbances in real time. Then the tracking differentiator is adopted to avoid the issue of computational burden caused by the repeated differentiation of virtual controllers. In light of the active disturbance rejection control technique, a distributed containment control scheme is put forward, which effectively deals with the problems of unknown nonlinearities and uncertain dynamics. Moreover, it is proved that containment errors of all followers converge to a small neighbourhood of the origin via the stochastic Lyapunov theory. Finally, the effectiveness of the proposed control method is verified by simulation results. [ABSTRACT FROM AUTHOR]

This paper studies the sampled-data resilient control for stochastic nonlinear cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. First, under the sampled-data control scheme, DoS attacks are described and under-considered in the stochastic nonlinear CPSs. Secondly, according to the influence of DoS attack, a new switching stochastic nonlinear closed-loop system is established under a full-state feedback controller. Then, by using the piecewise Lyapunov functional method, sufficient conditions are provided to ensure the mean square exponential stability of the obtained closed-loop system. In addition, some criterions are given to design controller parameters. Finally, an example is given to illustrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This paper investigates the characteristics of the variable-augmented-based free-weighting matrices and studies their application to the stability analysis of the systems with time-varying delay. First, the higher-order terms of time-varying delay are avoided in estimating the derivative of the Lyapunov–Krasovskii functionals by introducing the variable-augmented-based free-weighting matrices such that an improved stability condition is obtained. Then, a general form of the variable-augmented-based free-weighting matrices is given and their common characteristics and additional functions are analysed. As a result, a basic criterion to remove the unnecessary free-weighting matrices is given to derive some results with less free-weighting matrices. Finally, a numerical example is given to show the effectiveness and merits of the presented method and analysis. [ABSTRACT FROM AUTHOR]

This paper investigates the settling time problem for dynamical systems, and some novel results are proposed. First, by reviewing classic achievements about the settling time problem of the control system, a new framework is designed. Then, based on the proposed framework, the classic fixed time control scheme is improved to predefined time control and exact time control results, respectively. Similarly, finite time control scheme is transformed into predefined time control and exact time control results. Furthermore, considering the bounded control results are vital in actual applications, the novel predefined time-bounded control and exact time-bounded control schemes are given. In the end, numerical simulations are conducted for robotic manipulators to demonstrate the effectiveness of the developed control strategies. [ABSTRACT FROM AUTHOR]

Token financing is operated by a platform based on blockchain technology. This paper mainly studies the influence of ESG factors on the decision-making of various participants on the platform and analyses the influence of the persistent shock environment on the operation of the token platform. We first characterise developers' token holdings by a jump-diffusion stochastic differential equation. Besides, the nonpecuniary benefits specific to the ESG environment is introduced to the developers' objective function and an incentive of government capital gain tax for green platform developers is also considered. Moreover, the Hamilton–Jacob–Bellman (HJB) equation satisfied by the developers' value function is derived while the equilibrium price of the token is obtained. Then, we compare the differences between some features, which include users' transaction volume, speculators' transaction volume and expected token returns, on the green platform (ESG score greater than 0) and the same features on the brown one (ESG score less than 0). We also analyse the impact of speculators' ESG preferences on the expected token returns, users' and speculators' transaction volume. Finally, by using numerical simulation, we illustrate the impact of the intensity of persistent shock and investment risk (token return volatility) on the operation of the token platform. [ABSTRACT FROM AUTHOR]

Based on the two-dimensional (2D) Roesser model, this paper focuses on the asynchronous controller design for directional 2D Markov jump systems (MJSs). Considering the more practical situation that the modes of the 2D MJSs in the horizontal and vertical directions are independent, and the system modes are asynchronous with that of the controller, the directional 2D MJSs are proposed and the asynchronous controller is designed, respectively. Based on the Lyapunov theory, a sufficient condition for the mean square exponential stability of the controlled directional 2D MJSs is given. Moreover, the partially unknown transition probabilities in systems and controllers are considered. Finally, an example is given to illustrate that the proposed method is effective. [ABSTRACT FROM AUTHOR]

In this paper, the distributed fault-tolerant consensus control problem is studied for multi-agent systems with event-triggered communication. Unknown time-varying actuator faults including partial loss of effectiveness and bias are taken into consideration. For the sake of fault compensations, a novel distributed event-triggered control law is put forward via the adaptive fault-tolerant technique in the leaderless network. Then, two event-triggered mechanisms are constructed, and a positive L 1 function introduced therein is greatly useful for scheduling the information transmission as well as excluding the Zeno behavior. Under the proposed protocol, the consensus errors are proved to converge to zero asymptotically in a scalable and fully distributed way. Furthermore, the results obtained in the leaderless network are extended to the leader-following case. Finally, simulation examples are conducted to confirm the validity of the presented results. [ABSTRACT FROM AUTHOR]

This paper proposes a multiobjective optimisation scheme for a new cage-secondary permanent magnet linear eddy current brake, where the secondary is designed to own the cage configuration with variable parameters. This unique structure is suitable for applying to the high-speed rail transit braking system. Via speed-based multiobjective optimisation for this eddy current brake, the objectives of short braking distance, light weight, and low manufacturing cost can be achieved. First, the detailed structure and principle are described. Second, the braking force performance is accurately predicted by the proposed 3D analytical method. And the influence of secondary materials and secondary parameter values on braking force performance in all speed ranges is analysed. Third, the sensitivity of the optimisation design variables is analysed by the gene distribution maps. Then, the genetic algorithm based on the analytical model is used for multiobjective optimisation in all speed ranges and the best design scheme is selected. Finally, the accuracy of optimisation and the analytical model is verified by prototype experiment and simulation. [ABSTRACT FROM AUTHOR]

Event-triggered fault diagnosis has attracted tremendous research attention in the last decade due to its superiority in improving the utilisation efficiency of communication resources. Different from traditional works of time-driven, event-triggered schemes are used to determine whether the current measurement output should be released to the fault detection filter, while the sensor data not satisfying a predefined triggering condition will be discarded directly. As such, research on event-triggered fault diagnosis has been a challenging issue and many outstanding results have been reported. This paper presents a survey of model-based event-triggered fault detection (FD) and fault estimation (FE) methods mainly based on the techniques of residual generation. First, an overview of recent advances in state estimation-based methods of event-triggered FD is provided, which include the event-triggered FD for dynamic systems subject to Gaussian noises, the H ∞ filtering formulation of event-triggered FD, and the event-triggered H i / H ∞ optimisation-based FD. Second, the representative results of parity space-based event-triggered FD are reviewed. Third, recent results on event-triggered FE are also reviewed. Finally, several challenging issues on event-triggered fault diagnosis are provided for future research. [ABSTRACT FROM AUTHOR]

In this paper, we propose an octonion-valued neural network model governed by dynamic equations, and study the existence and stability of its almost periodic solutions by employing the fixed point method and the time scale calculus theory. The method we use is the direct one, that is, we do not split the considered systems into real-valued ones, but directly study octonion-valued systems. Even when the time scale T = R , in other words, even if we consider continuous time real-valued systems, our results are novel. Finally, we illustrate the effectiveness of our results through a numerical example and computer simulation. [ABSTRACT FROM AUTHOR]

The primal-dual gradient dynamics is a broadly investigated approach for handling optimisation problems. In this paper, we provide an extension of such dynamics under the adaptive updating framework for solving equality-constrained quadratic programmes. We show that the performance of the proposed method is theoretically guaranteed and it has asymptotic convergence to the solution of the optimisation problem and the minimum inter-event time is non-trivial. A numerical example and an application show the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Communication network could suffer uncertainties originating from link faults and network attacks. This situation, in multi-agent systems, can be featured by unknown weights of network topology. To impair the influence of the uncertain topology, typical compensation should be taken into account in distributed protocols to the workability of multi-agent systems. This paper, in the context of uncertain topology, addresses finite-time leader-following consensus for second-order uncertain nonlinear multi-agent systems. In addition to uncertainties in topology, the systems also allow unknown control coefficients, which together with the unknown weights renders the realisation of finite-time leader-following consensus nontrivial. Specifically, a fully distributed protocol based on distributed finite-time observer is designed via integrating the adaptive compensation scheme. Notably, in the designed protocol, dynamic high gains are introduced for the compensations of the network uncertainties and agents uncertainties. It turns out that the designed fully distributed protocol guarantees the global finite-time leader-following consensus. Simulation examples are provided to illustrate the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

The paper deals with active fault diagnosis of stochastic large-scale systems consisting of several subsystems with separate inputs and observations, which are coupled through the system state. The subsystems are described by multiple models expressing their fault-free and faulty behaviour. The transition between the models is governed by a Markov chain. The paper proposes a distributed design of an active fault diagnosis algorithm, which takes into account the coupling among the subsystems in all stages of the algorithm. This results in a higher quality of the excitation signal and consequently in better decisions. The numerical example shows the improved performance of the proposed algorithm in comparison with the algorithms based on the decentralised design. [ABSTRACT FROM AUTHOR]