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This paper develops a real-time identification algorithm for structures of network systems. We have already proposed a structure identification method for nonlinear network systems with nonlinear couplings by combining Koopman operator theory and a sparse identification method. However, the proposed method is only applicable for offline use under the assumption that the state of an isolated system is available. To relax this assumption, this paper shows that the dynamical model of isolated systems can be extracted from measured data of interconnected systems under some weak restrictions on network structures. We also modify the identification algorithm to obtain real-time identification results with fewer calculation resources and analyse the convergence of the proposed methods. Numerical examples demonstrate that the proposed method can identify network structures in real-time and detect changes in networks correctly. [ABSTRACT FROM AUTHOR]

This paper will investigate the prescribed-time stabilisation problem for a class of nonlinear systems with state constraints. A recursive design algorithm is proposed to solve the prescribed-time stabilisation problem. Firstly, a barrier Lyapunov function (BLF) is employed and a time-varying coordinate transformation is utilised. Then, a modified method of L g V -backstepping is proposed, and a novel stabilising function by adding a fractional term proposed in this paper which is capable of decreasing the BLF to the origin within any desired settling time and then achieving the prescribed-time stabilisation. Such a fractional term plays an important role in achieving prescribed-time stabilisation. Next, the analysis of the prescribed-time stabilisation with state constraints is presented. Finally, an example shows the effectiveness of the proposed method. The feature of this paper is that the settling time is not only independent of the design parameters, nor does it depend on the initial conditions, and can be set according to per our will. Other is that the constraints of all the states are not violated. [ABSTRACT FROM AUTHOR]

This paper presents the design of a robust Restricted-Model-Based control providing tools for both stability and performance analysis of a class of Single-Input-Single-Output (SISO) Takagi–Sugeno (T–S) systems. The proposed method is evaluated in terms of induced L 2 -gain (or so-called H ∞ norm) to be robust to disturbances, sensor noises and uncertainties on the premise variables. Unlike the common approaches reported in the literature that consider exact premise variables, this work deals with the problem of unmeasured premises. The main results of this paper illustrate that stability and performance conditions can be evaluated by examining the feasibility of parametrised sets of linear matrix inequalities (LMIs). The proof of stability is based on the non-linear sector approach of the closed loop under Lyapunov conditions, L 2 norm and system transformations. The result is a control structure with only one parameter tuned via simple conditions. The performance and applicability of the proposed approach are illustrated through numerical simulations of an academic example. [ABSTRACT FROM AUTHOR]

This paper addresses the H ∞ filtering for discrete-time Markovian jump singular systems (MJSSs). While the existing researches have only given the sufficient conditions, in this paper a sufficient and necessary condition is established to guarantee that the filtering error system is regular, causal, stochastically stable and H ∞ performance in light of supermartingale-liked approach and property of conditional expectation as well as – bounded real lemma. For obtaining the filter, an equivalent matrix inequality to the obtained one in nonlinear form is provided in terms of congruence transformation. Thus the desired filter can be developed by choosing matrix variables with certain structures. Finally, two simulations are provided to show the effectiveness of the proposed techniques in this paper. [ABSTRACT FROM AUTHOR]

This paper studies the input-to-state stability (ISS) and the finite-time input-to-state stability issues for the discrete-time stochastic switched nonlinear systems with time-varying delay. Based on the improved vector-version comparison principle and by employing the stochastic analysis method, sufficient conditions are first derived to guarantee the discrete-time stochastic switched system to be finite-time input-to-state stable in mean and finite-time stochastic input-to-state stable. Then, the comparison principle is further utilised to establish the p-th moment ISS result. As an application, the mean-square ISS is analysed for the linear discrete-time stochastic switched system. At the end of the paper, two numerical examples are provided to illustrate the feasibility/effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

The finite frequency domain H ∞ consensus control problem of neutral multi-agent systems with the input delay is investigated in this paper for the first time. According to the linear transformation, the problem of H ∞ consensus is converted into a set of H ∞ stability problems. Through the Lyapunov theory, combined with the time-delay interval decomposition method, a less conservative controller design method for the state feedback controller is presented. And the obtained controller can ensure closed-loop multi-agent systems to fulfil the consensus. Utilizing the generalised Kalman-Yakubovich-Popov (GKYP) lemma, the sufficient condition is derived for multi-agent systems achieving the H ∞ performance in the finite frequency domain (FFD). Then, via the orthogonal spatial information of the input matrix, the conservativeness of obtained design approach is further declined. Moreover, the design method with less conservativeness is proposed for the dynamic output feedback controller. Finally, a numerical case and a multi-pendulum system controlled by the multi-motor system are presented in this paper to demonstrate the availability and applicability of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper discusses the output-feedback-based model-free optimal output tracking control problem of discrete-time systems with completely unknown system models under mild assumptions. The only information that allows utilisation is the system output and the reference output. To overcome these challenges, the paper aims to solve a model-free optimal output regulation problem for achieving optimal output tracking control; solving an optimal output regulation problem is equivalent to solving a linear quadratic regulation (LQR) problem and a constrained static optimisation problem. The state reconstruction is first given to represent the system state in terms of the input and output sequences. A data-driven value iteration (VI) algorithm is then proposed to iteratively approximate the solution to the discrete-time algebraic Riccati equation (ARE) of the corresponding LQR problem on the basis of input and output data. Next, based on the iterative solutions with respect to the ARE, a model-free solution is provided for the corresponding constrained static optimisation problem. Finally, an alternative reference system equivalent to the original reference system is established to avoid the requirement of having the knowledge of the reference system dynamics and the reference state. A numerical example is employed to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article focuses on investigating a dynamic output feedback control issue for uncertain nonlinear networked control systems (NCSs) in the presence of Denial-of-Service (DoS) attacks and sensor failures. By using the low and upper membership functions (LUMFs) of interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model, the uncertainties existing in the considered NCSs are depicted effectively. Based on the IT2 fuzzy system, a switching fuzzy model is further established to correspond to different DoS attack scenarios. According to the average dwell-time approach and Lyapunov stability theory, an IT2 fuzzy dynamic output feedback control scheme is proposed so that the exponential stability of the considered NCSs can be achieved regardless of DoS attacks and sensor failures. Lastly, illustrative examples are presented to confirm the theoretical results proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper studies disturbance estimation and compensation for discrete-time large-scale nonlinear descriptor systems with unknown measurement noises. Interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy model is used to represent nonlinear dynamics, where fuzzy representation is assumed to be appearing not only in both the state and input matrices but also in the matrix of derivative state. First, by using a novel model transformation, the fuzzy representation in the derivative matrix is formulated into the linear one. Then, an augmented fuzzy observer is introduced to implement a synchronous estimation for the system state and the unknown measurement noise, which guarantees that reachable sets of the closed-loop system are bounded by a given ellipsoid. Moreover, a compensation-based controller is employed to remove or relieve the uncertainties induced by measurement noises. Finally, the validity of the obtained theories is testified by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, a trajectory tracking control strategy is proposed for multi-legged underwater robot in the presence of input saturation and full-state constraints. An anti-windup compensator is introduced to solve the input saturation problem, which can compensate for the saturation difference directly. Besides, the state constrains are addressed by a new state saturation function and the external unknown disturbances are solved by interval type-2 fuzzy neural network approximator. The dynamic surface control method combines with backstepping method is utilised to construct Lyapunov function. The control law and signals of the closed-loop system can be guaranteed to achieve semi-global uniform boundedness. Numerical simulations are presented to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel fractional-order adaptive controller is presented for a class of nonlinear systems with unknown dynamics. The dynamics of the system is considered to be fully unknown. The multi-layer perceptron (MLP) neural network using restricted Boltzmann machine (RBMs) is employed for online dynamic identification. A deep learning method on the basis of contrastive divergence (CD) algorithm combined with the extended Kalman filter (EKF) is proposed for online optimisation. The proposed controller has two parts. The first part is a simple error feedback controller and the second one is the suggested DT2-FLS. The parameters of DT2-FLS are tuned such that a cost function of tracking error to be minimised and the closed-loop system to be stable. For the best knowledge of the authors, for the first time the tuning rules for the membership function and rule parameters of DT2-FLS are derived by error feedback learning method. The closed-loop stability is demonstrated with Lyapunov method and the well performance of the schemed controller is shown by applying on the induction motor and brushless DC motors. In addition to unknown dynamics, some disturbances are also considered such as abruptly changes in load torque and time-varying rotor resistance. Furthermore, the performance of the suggested scheme is compared with some popular controllers and FLSs. [ABSTRACT FROM AUTHOR]

In this paper we propose a robust output feedback control scheme for constant reference tracking in uncertain linear systems subject to state and control constraints. First, we establish conditions under which a polyhedral set is Output Feedback Controlled Invariant with respect to a linear system subject to polytopic uncertainties, i.e. a set guaranteeing robust constraint satisfaction via output feedback. Then, we build a dynamic output feedback controller for the uncertain model based on set-membership observers, allowing the reduction of the set of possible states consistent with the measurements. Finally, to reduce the tracking error, we propose a model update procedure that adjusts the nominal model used for tracking to the output measurements. Numerical experiments illustrate the ability of the proposed controller to achieve reference tracking with reduced offset for the uncertain systems under consideration. To the best of our knowledge, it is one of the first works that addresses constant reference tracking in uncertain linear systems under constraints with output feedback. [ABSTRACT FROM AUTHOR]

This paper analyzes a class of stochastic coupled systems with time-varying delays in infinite dimensions. The existence and uniqueness as a prerequisite to ensure synchronization of the solution is analyzed, based on the idea of the contraction mapping principle, graph theory, and mild Itô's formula. Next, the p-th moment exponential synchronization (PMES) of infinite-dimensional systems is realised using the discrete control strategy, namely, aperiodically intermittent control (AIC). By combining graph theory with the Lyapunov method, several criteria for synchronizing infinite-dimensional systems are obtained using the mild Itô's formula. These criteria show the effects of control parameters, topology, and time delays on PMES. Finally, the theoretical results are applied to a class of neural networks with reaction-diffusion, and some numerical simulations are also given. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the problem of designing locally stabilizing dynamic output feedback controllers and estimate the domain of attraction for discrete-time Takagi-Sugeno (T-S) fuzzy systems. A realistic scenario is assumed where the control signal is subject to saturation, and the premise variables are partially or completely unmeasured, that is, not available for the control law. As a result, the fuzzy output controller can have a different number of fuzzy rules and a different set of membership functions from the T-S model. To obtain locally stabilizable conditions, we propose modeling the variation rate of the membership functions without using upper bounds, a new contribution in the context of output control of discrete-time T-S systems. The design conditions are expressed as linear matrix inequality relaxations based on fuzzy Lyapunov functions using slack variables introduced by Finsler's lemma. Numerical examples illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

A switched Boolean network (SBN) is an important model for describing hybrid phenomena in gene regulatory networks, and it has received increasing attention since the development of the semi-tensor product of matrices. This paper aims to present a comprehensive survey on SBNs. First, the fundamental issues for SBNs with arbitrary switching signals are reviewed, including stability, stabilisation, controllability, observability, optimal control, and synchronisation. Next, a brief review of the stability analysis of SBNs with constrained switching, consisting of time-dependent switching, state-dependent switching, and logical switching, is given. In addition, some basic results on the SBNs under non-ideal conditions are also introduced comprehensively. [ABSTRACT FROM AUTHOR]

Communication interference affects the performance of networked iterative learning control (ILC) systems. In this paper, instead of designing new ILC controllers, we design a filter on the side of objects to calculate the updated input of ILC controllers under the effects of communication delays and noises in both links. To design the filter, a transmission model is developed to describe four different reception cases caused by communication delays and noises. Utilising the knowledge of ILC controllers and the developed transmission model, the filter is designed according to the orthogonality projection principle. Theoretical analysis and simulation results show that the calculated input effectively improves the convergence of objects controlled by the P-type ILC controller with communication delays and noises. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive tracking control approach is developed for full-state constrained switched nonlinear systems that have actuator saturation, periodic disturbances and unknown control direction. To deal with the full-state constraints, the Barrier Lyapunov functions are introduced to limit the state variables within the corresponding constraint conditions. Meanwhile, the Fourier series expansion technology is employed to deal with unknown periodic disturbances and unknown nonlinear dynamics jointly. Additionally, a Nussbaum-type function is used in the controller design to cope with the and unknown control gain and input saturation. On the basis of the Lyapunov stability theory, it is demonstrated rigorously that all signals of the closed-loop system are uniformly ultimately bounded, and the proposed controller ensures that the tracking error is kept within a compact set close to zero. In the end, the validity of the designed control protocol is verified by a simulation example. [ABSTRACT FROM AUTHOR]

While applying control signals to every node is typically considered in multiagent networks to mitigate the adverse effects of misbehaving nodes, this control strategy may not always be possible due to physical constraints and/or economical limitations. Motivated by this standpoint, we have recently focused on how to control misbehaving multiagent networks through sending control signals to a subset of nodes (i.e. driver nodes) for suppressing the adverse effects of misbehaving nodes in the overall multiagent network, where the driver nodes only use their own state information to generate their control signals. To address the open problem where the driver nodes cannot obtain their own state information, the main contribution of this paper is that we now take into account that these nodes need to generate their control signals using the state information received from other subset of nodes (i.e. observer nodes). We characterise which nodes in the network behave as desired based on the selection of driver nodes and observer nodes in control of misbehaving multiagent networks. In addition, we provide numerical examples to illustrate the effectiveness of the proposed approach as well as the importance of the selection of driver and observer nodes for maximising the number of nodes that exhibit the desired responses. [ABSTRACT FROM AUTHOR]

This paper investigates a parameter-dependent (PD) functional observer-based finite-time adaptive memory integral sliding mode controller (ISMC) scheme for continuous linear parameter varying (LPV) systems with unknown nonlinear functions. First, a novel functional observer including parameters and parameter rates is proposed, and the observer gains can be directly designed. Different from the previous studies, a memory ISMC scheme with a time-varying delay through the functional observer is used for the first time in LPV systems to improve the performance. Moreover, to fully adopt the model characteristics of LPV systems and the functional observer, an integral PD surface function that contains PD input matrices and a memory parameter is established simultaneously. And then, an adaptive compensator is deduced for eliminating the influence of unknown nonlinear functions, so that the designed memory ISMC can make closed-loop LPV systems stable within a preset finite time. Furthermore, stability criteria with less conservatism for the LPV systems are proposed by memory Lyapunov–Krasovskii functional with measurable parameters. These criteria can guarantee LPV systems are finite-time boundedness with H ∞ performance (FTB- H ∞ ) on the sliding stage, arriving stage and entire finite-time region. Finally, a riderless bicycle is given to display the validity and superiority of the developed approach. [ABSTRACT FROM AUTHOR]

The problem of sequential formation control in multiagent systems is studied in this paper, where a team of agents is required to establish needed formations in order at user-defined times. To address this problem, our contribution is a finite-time distributed control architecture predicated on multiplex networks over directed graph topologies. The key feature of the multiplex networks is that it allows leader agent(s) to spatially alter the size and orientation of the resulting formation without requiring global information exchange ability. In addition, the key feature of the finite-time approach is to ensure that the overall multiagent system establishes a particular formation determined by the leader agent(s) at user-defined times. System-theoretical analysis of the proposed architecture is given using the time transformation approach along with Lyapunov stability theory, and an illustrative numerical example is also included to demonstrate the ability of the proposed architecture in addressing the sequential formation control problem. [ABSTRACT FROM AUTHOR]

This paper investigates the neural network (NN) adaptive consensus output-feedback control problem for a class of nonlinear multi-agent systems (MASs) encountered sensor attacks. To overcome the impact of unknown sensor attacks, a NN estimation algorithm is adopted to estimate the unknown sensor attacks. Subsequently, a novel NN observer is established to estimate the states of encountered sensor attacks. Consequently, under the framework of backstepping control design, an adaptive NN consensus control method is proposed. By using the Lyapunov stability theory, the proposed consensus control method can not only ensure that all the signals of controlled MASs remain bounded, but also make all followers maintain consensus with the trajectory of the leader. Simulation results and comparative results illustrate the effectiveness of the proposed consensus control scheme. [ABSTRACT FROM AUTHOR]

In this paper, a new uncertainty modelling concept called strait fuzzy set is introduced, which brings new perspectives to both theoretical and practical advances in fuzzy mathematics. This set type allows objects/points to be graded with fuzzy membership intervals that are partitions of [0,1] (so that the union of these partitions is [0,1] and their intersection is empty) instead of fuzzy membership degrees represented as exact values in [0,1]. Moreover, some basic operations and properties of strait fuzzy sets are studied in detail. The concept of strait fuzzy rough set is put forward and its theoretical aspects are discussed. In addition, two different similarity approaches are proposed for strait fuzzy sets and strait fuzzy rough sets, and applied to measure the similarity rates of vaccines against influenza viruses. [ABSTRACT FROM AUTHOR]

This paper investigates the consensus problem of the multi-agent system by means of the sliding mode control (SMC) approach. For each agent, the component-based weighted try-once-discard protocol is used for scheduling the transmission of error signals via communication network to the controller. Only one node with the greatest difference at each moment can send its information through the network to the controller. Then, taking the protocol's impact into account, a token-dependent sliding mode controller is designed. Both the asymptotic stability of the closed-loop error system and the reachability of the specified sliding surface are achieved. In addition, by utilising the optimised solving algorithm, the controller gains are attained. Eventually, a simulation example proves the effectiveness of the suggested SMC approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the synchronisation and topology identification of stochastic delayed multi-group models with multi-dispersal and Markovian switching (SDMM). Our model is characterised by a combination of the stochastic perturbation, multi-dispersal and Markovian switching, which can reasonably model the actual system. By utilising the graph theory, Lyapunov method and stochastically finite-time attractiveness, two sufficient criteria are obtained to ensure the topology identification of SDMM and finite-time topology identification of SDMM based on adaptive synchronisation and finite-time synchronisation, respectively. These criteria are closely related to the topology property of the network and can be easily verified in practice. Finally, two numerical examples are given to show the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the bipartite consensus problem via a sampled-data delayed output feedback consensus protocol in the presence of competition among the agents for a class of feedback nonlinear multi-agent systems. Firstly, the bipartite consensus problem of the system under investigation is converted into a stabilisation problem of another system by introducing a suitable state transformation. Then, a sampled-data delayed output feedback consensus protocol is designed by making use of all agents' output signals sampled at the present time and all agents' delayed output signals at the preceding n−1 sampling points. By virtue of Lyapunov–Krasovskii functional, it is verified that the bipartite consensus problem of the system under study can be addressed by the designed consensus protocol, with the sampling period T and constant gain L selected appropriately. Two simulation examples are provided to illustrate the effectiveness of the designed consensus protocol. [ABSTRACT FROM AUTHOR]

This paper investigates synchronisation for Markovian master-slave neural networks (NNs), where the transition probabilities of Markov chain are partially unknown and uncertain. To cope with the communication channel bandwidth constraint, an event-triggered impulsive transmission strategy is adopted, a corresponding impulsive controller is then designed. In this method, information transmission occurs only at some discontinous instants, which are determined by a state-dependent event-triggered condition as well as a predesigned forced impulse interval. Synchronization for Markovian master-slave NNs is guaranteed by a sufficient condition, and the controller gains are designed by using the obtained results. A numerical simulation is given to show the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]

This paper focuses on the stability of delay differential systems under impulsive control suffered by logic choice. Firstly, an impulsive control suffered by logic choice is constructed for a delay differential system to make it more suitable for some practical problems. Secondly, by using a semi-tensor product, an effective method for qualitative analysis, such as stability analysis, of a differential delay system under impulsive control suffered by logic choice is proposed. That is, the properties of a differential delay system under impulsive control suffered by logic choice can be obtained by the properties of a corresponding non-impulsive differential delay system. Thirdly, some stability criteria are obtained. Finally, three illustrative examples are also discussed. [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 studies the problem of designing an incentive strategy for a leader–follower dynamic game affected by some sort of uncertainties. As is traditionally understood in the standard theory of incentives, the leader has complete knowledge of the game parameters, including the follower's performance index. So then he can compute the strategy that will lead the game to the global optimum that is favourable for him. Most of the current work is devoted to this situation. Nevertheless, such an assumption is unrealistic. This paper proposes an incentive scheme in which the game's dynamic depends on an unknown value that belongs to a finite set. The solution of the incentive strategy is computed in terms of the worst-case scenario, of the team's optimal solution. Based on the Robust Maximum Principle, the new incentive is presented in the form of a mini-max feedback control. Two numerical examples illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider the specified-time platoon control for the networked vehicles modelled by the third-order dynamics with uncertain external bounded disturbance. The vehicles are subjected to disturbance and parameter uncertainties. To ensure the platoon can converge within a predefined time, a novel disturbance observer with a time-varying dynamic gain is proposed. By the time transformation approach, the disturbance observer is proved to be uniformly ultimate bounded (UUB) stable in a specified time. The time-varying dynamic gain is also applied to design the novel specified-time controller for the vehicle platoon and rigorous proof is presented based on Lyapunov stability. Under the proposed disturbance observer and controller, the vehicle platoon can be stabilised within the predefined time without depending on any system parameters and initial conditions. Finally, some comparison simulation results are given to verify the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

Nowadays, scheduling problem in the field of production planning has been considered by many researchers and this issue is used in various industries. Using the same parallel machines reduces the queue of WIP behind the machine and also increases production. Since scheduling problems in large scale are NP-hard, to solve them, metaheuristic algorithms are used. In this paper, Ant colony optimisation and Harmony Search algorithm are used to solve the problem as well as comparison with two other algorithms and finally, their performance is analysed and the efficient algorithm is investigated. [ABSTRACT FROM AUTHOR]

This paper deals with the robust observer-based synchronisation problem for a class of complex dynamic networks subject to multi-weights, time-varying coupling delay and external disturbance. Specifically, the gain fluctuation appears in the proposed controller and is represented in terms of a random variable obeying the Bernoulli distribution. The main objective of this paper is to design a non-fragile memory state feedback controller such that the resulting error system is stochastically synchronised under a prescribed performance level . First, a Luenberger-type state observer is constructed to estimate the state variables of the addressed system. Second, by constructing an appropriate Lyapunov–Krasovskii functional and using Wirtinger-based integral inequality techniques, the required set of sufficient conditions for the synchronisation of the proposed system is established in the form of linear matrix inequalities. In the end, two numerical examples are provided to demonstrate the validity and feasibility of the developed theoretical results. [ABSTRACT FROM AUTHOR]

This paper deals with the state feedback control for linear systems in the presence of state delay using a direct numerical design methodology. The design algorithm iteratively searches the solution space of controller parameters to find a proper controller gain that guarantees the delay-independent stability of the closed-loop system. The algorithm divides the solution space to some simplexes, selects one simplex and evaluates two checking methods on this simplex. The first method checks the stabilisability of the corner points (to establish the feasible point) and the other detects the total infeasibility (total de-stabilisability) of the simplex (to omit the undesired parts). One interesting property of the method is its capability in detecting total de-stabilisability of a simplex through its corner points. If none of these methods is successful, the simplex is divided into two smaller ones which will be checked in the next algorithm's iterations. According to the direct search nature, the algorithm is non-conservative and assuredly reaches a stabilizable point in the feasible space of the design space. The proposed algorithm and previous methods are evaluated on a set of random generated systems to compare the feasibility of the methods. Simulation results reveal the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the global tracking control problem for a class of nonlinear systems. Compared with the existing results, one significant distinction of this work is that it allows the high-order powers, zero dynamics, time-delay and external disturbances. By constructing a new Lyapunov–Krasovskii (L–K) functional and using the modified adding a power integrator method, we successfully design a non-smooth tracking controller which guarantees that all the signals of the closed-loop system are bounded. The tracking error can be adjusted small enough using the designed parameters. As a practical application, in the simulation, the single-link robot system is studied to show the validness of the strategy. [ABSTRACT FROM AUTHOR]

The indefinite linear quadratic (ILQ) optimal control problem has many important applications in financial, economic systems, etc., which has been widely researched in stochastic systems and descriptor systems, etc. However, for ILQ problem of rectangular descriptor Markov jump systems (DMJSs), because there are impulses simultaneously in descriptor subsystems and at the switching time, it is theoretically difficult to research and there is no result yet. This paper discusses the ILQ optimal control problem for continuous-time linear rectangular DMJSs. Firstly, under some rank conditions and inequality conditions, the ILQ problem for rectangular DMJSs can be equivalently transformed into standard LQ problem for Markov jump systems (MJSs) by using elementary linear algebra method. Then based on the LQ theory of MJSs, the solvable sufficient condition of the ILQ problem for rectangular DMJSs and the non-negative optimal cost value are obtained. The optimal control can be synthesised as state feedback, and the resulting optimal closed-loop system has the stochastically stable solution. In addition, with some rank inequality assumptions, the differential subsystem of the resulting optimal closed-loop system can be ensured to have a unique solution. Finally, two numerical examples are provided to illustrate the effectiveness of the methods proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the problem of circle formation of multi-agent system in a plane is investigated. Inspired by the idea of decouple design, we decompose the overall control objective into two sub-goals, the first one is circle forming that agents need to move to a circle with a desired radius and centred on a predefined target, and the second one is space adjustment that each agent form and maintain a desired relative position with its neighbours. Since the completion of the second objective requires information from neighbouring agents, a distributed event-triggered controller is proposed to save communication resources and reduce the number of controller updates, which consists of two parts corresponding to two sub-goals. Furthermore, it is proved that there exists no Zeno behaviour under the proposed control laws. Finally, a numerical simulation verifies the effectiveness of the distributed event-triggered circle formation controller proposed in this paper. [ABSTRACT FROM AUTHOR]

A new methodology is proposed in this paper to solve multiobjective optimisation problems (MOPs) by invoking genetic algorithms and solutions concepts of cooperative games. The weighting problems that are formulated by assigning some suitable weights to the multiple objective functions are usually solved to obtain the Pareto optimal solutions of MOPs. In this paper, the suitable weights will be taken from the core values of a cooperative game that is formulated from the original MOP by regarding the objective functions as players. Under these settings, we shall obtain a large set of all so-called Core-Pareto optimal solutions. In order to choose the best Core-Pareto optimal solution from this set, we shall invoke the genetic algorithms by setting a reasonable fitness function. [ABSTRACT FROM AUTHOR]

This paper is concerned with the differential privacy-based containment control problem for a class of discrete-time multi-agent systems (MASs) with time-varying topology. The classical containment control implementation relies on the explicit information exchange between agents, which will lead to the possible leakage of agents' private information, especially the initial states. In this paper, to prevent such data disclosure to neighbours inside the MASs and the outside eavesdroppers, uncorrelated noise is injected into the data during information transmission, which will bring about a more complicated scenario to the containment control. Under a milder assumption on the connectivity of the MAS – 'average graph topology', by resorting to the Lyapunov stability theory and algebraic graph theory, and utilising stochastic analysis techniques, sufficient conditions are derived to ensure that the discrete-time MAS subjecting to the privacy protection mechanism can achieve bounded containment control. Then, convergence accuracy is studied from the viewpoint of probability. Specially, Markov inequality is first utilised to estimate the lower bound of the probability that followers' states converge to the neighbourhood of the convex hull formed by leaders' states. Besides, differential privacy analysis is carried out to verify the validity of the proposed privacy protection mechanism in protecting the followers' initial states. Finally, two numerical examples are simulated to verify the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the proportional–integral-type estimator design problem is studied for recurrent neural networks under the encoding–decoding communication mechanism. In the process of the measurement data transmission, an encoding–decoding mechanism is introduced to improve the security of the network by encrypting the measurement data. The purpose of this paper is to design a proportional–integral-type estimation algorithm such that the estimation error dynamics is exponentially ultimately bounded in mean square. First, a sufficient condition is obtained for the existence of the desired estimator. Then, the parameters of the estimator are obtained by solving certain matrix inequality. Finally, a simulation example is given to verify the effectiveness of the designed estimation algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we address the distributed resilient fusion filtering (DRFF) problem for a class of nonlinear multi-sensor networked systems (MSNSs) with random sensor delay (RSD) under round-robin protocol (RRP). The RSD is depicted by the Bernoulli random variable with known occurrence probability. In order to relieve the network congestion, the RRP that can deal with information overload issue of the transmission process from sensor to the estimator is utilised. The major objective of this paper is that the resilient fusion filter is designed for nonlinear MSNSs with RSD and RRP in the light of the inverse covariance intersection approach, where the local upper bound regarding the filtering error covariance is obtained and then minimised by suitably exploiting the local filter gain. Finally, a simulation example that can show the validity of the provided DRFF algorithm is presented. [ABSTRACT FROM AUTHOR]

In this paper, the set-membership filtering problem is investigated for a class of polytopic uncertain two-dimensional shift-varying systems with delays and sensor saturation. The measurement information is transmitted between the sensors and the filter through a shared communication network. To avoid data congestions, the Round-Robin (R-R) protocol is employed to regulate the data transmission order. This paper aims to design a robust set-membership filter such that the real system states are always contained in the state estimate ellipsoid in the presence of the state delays, the sensor saturation, the parameter uncertainties, the R-R protocol and the unknown-but-bounded external perturbations. By using the mathematical induction, sufficient condition for the existence of the desired robust set-membership filter is given in the form of recursive linear matrix inequalities, and a convex optimisation method is also proposed to determine the optimal state estimation ellipsoid in the trace sense. One numerical example is also given to illustrate effectiveness of the proposed filtering strategy. [ABSTRACT FROM AUTHOR]

This paper studies the non-fragile consensus problem of nonlinear multi-agent systems (MASs) with state and input time-varying delays. The main purpose of this paper is to provide new approaches for the non-fragile consensus research of MASs with time-varying delay. To achieve this goal, a quadratic polynomial Lyapunov–Krasovskii functional is constructed and the generalised free-matrix-based integral inequality is employed. In addition, the quadratic function negative-determination method is applied to the consensus analysis of MASs with controller gain fluctuation. Finally, two novel consensus criteria are established. A simulation example is given to examine the effectiveness and superiority of the obtained criteria. [ABSTRACT FROM AUTHOR]

With the developments of wireless data communication and network technology, time-delays are widely found in nowadays' control systems, e.g. networked control systems, mobile robot systems, and multi-agent systems. Predictor-based control is an effective method dealing with long time-delays because it can generally lead to a delay-free closed-loop system by introducing a prediction for future states. Recently, various predictor-based control methods have been developed for numerous control systems subject to different time-delays, which motivates this survey. This paper presents a comprehensive review of the up-to-date results on the predictor-based control of time-delay systems. Firstly, the ordinary differential equation-based approaches for designing and analysing predictor-based controllers are summarised. Secondly, one reports an alternative method of predictor-based control, in which the systems/controllers are understood in the sense of partial differential equations. Next, several integration-free predictor-based controllers are introduced: by abandoning the infinite-dimensional integral terms, the control laws become easier to realise in practice. Hereafter, the paper discusses the real-time implementations and the practical applications of predictor-based control methods to several particular control systems. Finally, this paper suggests some new trends of predictor-based control for future research. [ABSTRACT FROM AUTHOR]

In this paper, the recursive state-of-charge (SOC) estimation problem is investigated for the Li-ion batteries. The uncertain parameters, which are used to account for the effects of the changing temperatures, the battery power and the drift current of current sensors, are considered in the modelling process of the Li-ion batteries. Moreover, the uncorrelated/correlated noises are also considered based on the engineering practice. The aim of the paper is to design a SOC estimation scheme such that an upper bound on the estimation error covariance is guaranteed, and such an upper bound is then minimised by appropriately designing the estimator gain. Finally, simulation experiments are carried out to demonstrate the effectiveness of our proposed SOC estimation scheme. [ABSTRACT FROM AUTHOR]

The robust linear parameters varying systems identification method with multiple-model approach is addressed in this paper. Various noise and outliers commonly exist in practical industrial processes and have a serious impact on data-driven system identification methods. A statistic approach is proposed in the paper where the centralised asymmetric Laplace (CAL) distribution is employed to model the noise and therefore the parameters estimation algorithm based on CAL distribution is robust to the symmetric/asymmetric noise and outliers. CAL distribution is represented as the product of exponential distribution and Gaussian distribution, and therefore the parameters estimation formulas are deduced in the expectation maximisation algorithm framework. The efficacy and the robustness of the proposed algorithm are verified by a numerical example and the continuous stirred tank reactor system experiment. [ABSTRACT FROM AUTHOR]

The problem of event-triggered control for a class of uncertain nonlinear systems subject to p-norm is investigated in this paper. Note that the input-to-state stability (ISS) assumption related to the measurement error and the assumption on the existence of a stable controller are removed in this paper. With the help of adding a power integrator technique, intermediately control signals are presented to address the uncertainties and nonlinearities of the system under a fixed/relative threshold strategy, which plays an important role in the design of the event-triggered controllers. A rigorous Lyapunov stability analysis method is put forward to verify that all the signals of the closed-loop system are globally uniformly bounded and converge to an arbitrarily small set. Finally, an application of an underactuated mechanical system demonstrates the effectiveness of the designed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a direct discretization method is used to approximate the nonlinear scale delayed optimal control problems. The technique is a pseudospectral collocation approach based on the Legendre-Gauss-Lobatto points. Firstly the domain of interest is divided into several adaptive subintervals, and then the traditional pseudospectral approach is used for each segment. This approach discretizes the optimal control problem with a scale delay function and transforms it into a nonlinear programming problem whose solution can be achieved via the existing solvers. The main advantages of this method are the simplicity of the structure and the ease of its implementation and execution. Moreover, the necessary and sufficient conditions of optimality associated with the scale-delayed control problems are obtained. To do this, a new transformation technique is proposed which transforms the scale delayed control problem into a constant delayed one. These conditions can be used to measure the accuracy of the approximate findings obtained by applying the suggested method. The effectiveness and usefulness of the discretization procedure are demonstrated by the implementation of the proposed method in some experimental examples. [ABSTRACT FROM AUTHOR]

In this paper, the boundary stabilisation of a coupled time fractional hyperbolic equation system is investigated by both state and output feedback. A target system is first introduced, which, by the semigroup method and the Lyapunov one, is proved to admit a unique solution and is Mittag–Leffler stable. A boundary state feedback controller and state observer are designed via the backstepping technique, with explicit formulae for the gain functions. An observer-based output feedback stabilising controller is then proposed for the controlled system. Some rigorous theoretical proofs are given to demonstrate that the resulting closed-loop system under the state and output feedback controllers has a unique solution and is Mittag–Leffler stable. Some numerical simulations are performed to validate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

This paper addresses the exponential stabilisation of time-varying delay systems by means of Lyapunov–Krasovskii approaches, Bessel–Legendre inequalities and Linear Matrix Inequalities (LMIs). The key aspect of the proposed control synthesis method is that the LMI conditions are proved for the first time to be hierarchical under time-varying delays, that is to say, a progressive conservatism reduction is achieved as long as the degree of Legendre polynomials is increased. To this end, Finsler's lemma has been properly applied to decouple the Lyapunov matrices from the controller gain. Finally, simulation results are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]