Showing papers in "Journal of The Franklin Institute-engineering and Applied Mathematics in 2019"

Novel fluid inerter based tuned mass dampers for optimised structural control of base-isolated buildings

Abstract: This work studies the advantageous features of the fluid inerter device for optimised structural control of buildings. Experimental data are first presented to characterise the fluid inerter dynamics, and validate the simplified analytical formulations. Building on these observations, the device is modelled as an inerter in parallel with a nonlinear dashpot representing a power law damping term. The latter dissipative effects are mainly induced by the pressure drops occurring in helical channels due to the fluid viscosity and density. Then, novel passive vibration control schemes are implemented for the earthquake protection of base-isolated buildings by combining the fluid inerter with a tuned mass damper system. To account for the uncertain nature of the earthquake input, the base acceleration is modelled as a Kanai–Tajimi filtered stationary random process. The optimal fluid inerter parameters, namely inertance and damping, are identified numerically by minimising stochastic performance indices relevant to displacement, acceleration, and energy-based measures of the structural response. The nonlinear damping behaviour of the fluid inerter is fully incorporated in the optimal design procedure via the statistical linearisation technique. Nonlinear response history analysis under an ensemble of 44 natural earthquake ground motions is carried out to assess the seismic performance of the system. Since inertance and damping are coupled characteristics in a real fluid inerter, design guidelines are finally outlined to determine the actual geometrical and mechanical properties of the device to achieve targeted parameters resulting from the optimisation procedure.

Distributed event-triggered control for networked control systems with stochastic cyber-attacks

Abstract: This paper is concerned with the problem of distributed event-triggered controller design for networked control systems (NCSs) with stochastic cyber-attacks. A decentralized event-triggered scheme is introduced to save the energy consumption and alleviate the transmission load of the network. Each sensor can make its own decision to determine whether the sampled data is delivered to the network or not. By taking two kinds of random cyber-attacks into consideration, a novel mathematical model is constructed for distributed event-triggered NCSs. Sufficient conditions which can guarantee the stability of the control system are obtained by applying Lyapunov stability theory, and the design method of the controller gain is presented in an exact expression. Finally, an example is given to demonstrate the effectiveness of the proposed method.

Particle filtering based parameter estimation for systems with output-error type model structures

Abstract: The output-error model structure is often used in practice and its identification is important for analysis of output-error type systems. This paper considers the parameter identification of linear and nonlinear output-error models. A particle filter which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilized to estimate the unmeasurable true process outputs. To improve the convergence rate of the proposed algorithm, the scalar innovations are grouped into an innovation vector, thus more past information can be utilized. The convergence analysis shows that the parameter estimates can converge to their true values. Finally, both linear and nonlinear results are verified by numerical simulation and engineering.

A new optimized fuzzy FOPI-FOPD controller for automatic generation control of electric power systems

Abstract: In the rapidly expanding size and complexity of the electricity network, automatic generation control (AGC) is contemplated to be the most remarkable option for offering good quality electric power supply to the end users. An AGC system entails highly vigorous, competent and intelligent control technique to deliver a healthy power under stochastic nature of consumers’ power demand. Hence, in this paper, a hybrid fuzzy fractional order proportional integral-fractional order proportional derivative (FFOPI-FOPD) controller is proposed as a new expert control technique to tackle AGC profitably in isolated and interconnected multi-area power systems. A recently developed imperialist competitive algorithm (ICA) is utilized for the optimization of the output gains (KP/KP1/KI/KD) and other parameters such as order of integrator (λ) and differentiator (μ) of FFOPI-FOPD controller exercising integral of squared error criterion. The proposed technique is firstly implemented on 1-area thermal system, then to express its potential and extensibility, the work is extended to 2-area hydro-thermal and 3-area thermal power systems widespread in the literature. The eminence of the method is betokened by comparing the results with the various newly published control methodologies and FPI/FFOPI controller designed in the study via ICA in terms of minimum values of various error criteria and undershoots/overshoots/settling times of frequency and tie-line power deviations following a sudden load demand in an area. The sensitivity analysis substantiates that the suggested controller is robust and performs staunchly under the wide variations in the system parameters, random load pattern and in the company of physical constraints to produce more clean electricity.

Observer-based fuzzy adaptive stabilization of uncertain switched stochastic nonlinear systems with input quantization

Abstract: This paper proposes an observer-based fuzzy adaptive output feedback control scheme for a class of uncertain single-input and single-output (SISO) nonlinear stochastic systems with quantized input signals and arbitrary switchings. The SISO system under consideration contains completely unknown nonlinear functions, unmeasured system states and quantized input signals quantized by a hysteretic quantizer. By adopting a new nonlinear disposal of the quantized input, the relationship between the control input and the quantized input is established. The hysteretic quantizer that we take can effectively avoid the chattering phenomena. Furthermore, the introduction of a linear observer makes the estimation of the states possible. Based on the universal approximation ability of the fuzzy logic systems (FLSs) and backstepping recursive design with the common stochastic Lyapunov function approach, a quantized output feedback control scheme is constructed, where the dynamic surface control (DSC) is explored to alleviate the computation burden. The proposed control scheme cannot only guarantee the boundedness of signals but also make the output of the system converge to a small neighborhood of the origin. The simulation results are exhibited to demonstrate the validity of the control scheme.

New results on exact controllability of a class of fractional neutral integro-differential systems with state-dependent delay in Banach spaces

Abstract: This article deals with the use of Krasnoselskii’s fixed point theorem and Leray–Schauder alternative theorem combined with resolvent operator and some analytical methods to investigate the exact controllability and continuous dependence of a class of neutral fractional integro-differential systems with state-dependent delay in Banach spaces. An application to exemplify the concept is provided at the end.

State space model identification of multirate processes with time-delay using the expectation maximization

Abstract: This paper presents the problems of state space model identification of multirate processes with unknown time delay. The aim is to identify a multirate state space model to approximate the parameter-varying time-delay system. The identification problems are formulated under the framework of the expectation maximization algorithm. Through introducing two hidden variables, a new expectation maximization algorithm is derived to estimate the unknown model parameters and the time-delays simultaneously. The effectiveness of the proposed algorithm is validated by a simulation example.

Moving horizon estimation for multirate systems with time-varying time-delays

Abstract: This paper presents a moving horizon estimation approach for the multirate sampled-data system with unknown time-delay sequence. To estimate the unknown variables of interest, two main challenging issues need to be addressed: (a) synthesizing the multirate input and output data for state estimation, (b) simultaneously estimating the continuous state and discrete time-delay sequence. In this work a moving horizon estimation based approach is developed to tackle these issues. The proposed approach can simultaneously estimate both the continuous states and discrete time-delay sequence for dynamic systems. The effects of different noise level on the estimation of continuous states and discrete time-delay sequence are analyzed. The effectiveness of this method is illustrated through a simulation study.

Hierarchical recursive generalized extended least squares estimation algorithms for a class of nonlinear stochastic systems with colored noise

Abstract: This paper considers the parameter identification problem of a bilinear state space system with colored noise based on its input-output representation. An input-output representation of a bilinear state-space system is derived for the parameter identification by eliminating the state variables in the model, and a recursive generalized extended least squares algorithm is presented for estimating the parameters of the obtained model. Furthermore, a three-stage recursive generalized extended least squares algorithm is proposed for reducing the computational cost. The validity of the proposed method is evaluated through a numerical example.

New results on stability analysis of systems with time-varying delays using a generalized free-matrix-based inequality

Abstract: This paper addresses the delay-dependent stability problem of linear systems with interval time-varying delays. A generalized free-matrix-based inequality is proposed and employed to derive stability conditions, which are less conservative than the Bessel–Legendre inequality. An augmented Lyapunov–Krasovskii functional is tailored for the generalized free-matrix-based inequality. Then, some items in the Lyapunov–Krasovskii functionals are integrated so as to relax its positive definite condition, which provides a more accurate lower bound for the Lyapunov–Krasovskii functionals. Therefore, some less conservative stability criteria are presented. Two numerical examples illustrate the effectiveness of the method.

Multi-objective optimal design of inerter-based vibration absorbers for earthquake protection of multi-storey building structures

Abstract: In recent years different inerter - based vibration absorbers (IVAs) emerged for the earthquake protection of building structures coupling viscous and tuned - mass dampers with an inerter device . In the three most popular IVAs the inerter is functioning either as a motion amplifier [tuned - viscous - mass - damper (TVMD) configuration], mass amplifier [tuned - mass - damper - inerter (T MDI) configuration], or mass substitute [tuned - inerter - damper (TID) configuration]. Previous work has shown that through proper tuning , IVAs achieve enhanced earthquake - induced vibration suppression and/or weight reduction compared to conventional dampers/absorbers , but at the expense of increased control forces exerted from the IVA to the host building structure . These potentially large forces are typically not accounted for by current IVA tuning approaches. In this regard, a multi-objective IVA design approach is herein developed to identify the compromise between the competing objectives of (i) suppressing earthquake-induced vibrations in buildings, and (ii) avoiding development of excessive IVA (control) forces, while, simultaneously, assessing the appropriateness of different modeling assumptions for practical design of IVAs for earthquake engineering applications . The potential of the approach to pinpoint Pareto optimal IVA designs against the above objectives is illustrated for different IVA placements along the height of a benchmark 9-storey steel frame structure. Objective (i) is quantified according to current performanc e-based seismic design trends using first-passage reliability criteria associated with the probability of exceeding pre-specified thresholds of storey drifts and/or floor accelerations being the engineering demand parameters (EDPs) of interest . A variant, simpler, formulation is also considered using as performance quantification the sum of EDPs variances in accordance to traditional tuning methods for dynamic vibration absorbers. Objective (ii) is quantified through the variance of the IVA force. It is found that reduction of IVA control force of up to 3 times can be achieved with insignificant deterioration of building performance com pared to the extreme Pareto optimal IVA design targeting maximum vibration suppression , while TID and TMDI a chieve practically the same building performance and significantly outperform the TVMD. Moreover, it is shown that the simpler variant formulation may provide significantly suboptimal reliability performance . Lastly, it is verified that the efficacy of optimal IVA designs for stationary conditions is maintained for non-stationary stochastic excitation model capturing typical evolutionary features of earthquake excitations .

Event-triggered control for active vehicle suspension systems with network-induced delays

Abstract: This paper presents a novel event-triggered H∞ static output-feedback control for active vehicle suspension systems with network-induced delays. The proposed control schema introduces an event-triggering mechanism in the suspension system such that the communication resources can be significantly saved. By applying some improved slack inequalities and an augmented Lyapunov–Krasovskii functional (LKF), a new design condition expressed in the form of linear matrix inequalities (LMIs) is developed to derive the desired event-triggered controller. The obtained algorithm is then employed to solve the static output-feedback control gain. Compared with the traditional sampled-data H∞ control scheme, the proposed controller is able to provide an enhanced disturbance attenuation level while saving the control cost. Finally, comparative simulation results are provided to show the performance of the proposed event-triggered controller.

Stability and synchronization criteria for fractional order competitive neural networks with time delays: An asymptotic expansion of Mittag Leffler function

Abstract: Competitive neural networks(CNNs) has not been well developed in nonlinear fractional order dynamical system, which is developed first time in this paper. Then, by means of a proper Lyapunov functional, asymptotic expansion of Mittag-Leffler function properties, together with some Caputo derivative properties, the testable novel sufficient conditions are given to guarantee the existence, uniqueness of the equilibrium point as well as global asymptotic stability for a class of fractional order competitive neural networks (FOCNNs) are all derived in the form of matrix elements. Furthermore, the boundedness for the solution of FOCNN is presented by employing Cauchy–Schwartz inequality and Gronwall inequality. Besides, a linear feedback control and adaptive feedback control are designed to achieve the global asymptotic synchronization criterion for FOCNNs with time delay and these explored consequences are extended from some previous integer order CNNs output. At last, two numerical simulations are performed to illustrate the effectiveness of our proposed theoretical results.

Leader-following fixed-time quantized consensus of multi-agent systems via impulsive control

Abstract: In this paper, we mainly tend to consider distributed leader-following fixed-time quantized consensus problem of nonlinear multi-agent systems via impulsive control. An appropriate quantized criterion and some novel control protocols are proposed in order to solve the problem. The protocols proposed integrates the two control strategies from the point of view of reducing communication costs and constraints, which are quantized control and impulsive control. The fixed-time quantized consensus of multi-agent is analyzed in terms of algebraic graph theory, Lyapunov theory and comparison system theory, average impulsive interval. The results show that if some sufficient conditions are met, the fixed-time consensus of multi-agent systems can be guaranteed under impulsive control with quantized relative state measurements. In addition, compared with finite-time consensus, the settling-time of fixed-time quantized consensus does not depend on the initial conditions of each agent but on the parameters of the protocol. Finally, numerical simulations are exploited to illustrate the effectiveness and performance to support our theoretical analysis.

Output Feedback Control of Electro-Hydraulic Servo Actuators With Matched and Mismatched Disturbances Rejection

Abstract: In this paper, two output feedback controllers are proposed for motion control of double-rod electro-hydraulic servo actuators with matched and mismatched disturbances rejection. All of them employ an linear extended state observer (LESO) to achieve real-time estimates of the unmeasured system states and matched disturbance, and a nonlinear disturbance observer (NDO) to estimate the largely unknown mismatched disturbance at the same time. Thus, the disturbances are compensated via their online estimates in a feedforward way when implementing the resulting control algorithms, respectively. Furthermore, a continuously differentiable friction model is employed to compensate the majority of nonlinear friction existing in the system and reduce the burden of the NDO. Specially, one of the proposed control schemes utilizes model-based compensation terms depending on the desired trajectory to be tracked instead of the estimated system states. By doing this, online computation burden can be reduced. The stability of the whole closed-loop system under each control scheme is guaranteed by theoretical analysis. Moreover, the applicability of each control scheme are validated by experiments in different working conditions.

Security-based resilient event-triggered control of networked control systems under denial of service attacks

Abstract: This paper is concerned with the security control problem of the networked control system (NCSs) subjected to denial of service (DoS) attacks. In order to guarantee the security performance, this paper treats the influence of packet dropouts due to DoS attacks as a uncertainty of triggering condition. Firstly, a novel resilient triggering strategy by considering the uncertainty of triggering condition caused by DoS attacks is proposed. Secondly, the event-based security controller under the resilient triggering strategy is designed while the DoS-based security performance is preserved. At last, the simulation results show that the proposed resilient triggering strategy is resilient to DoS attacks while guaranteing the security performance.

Formation consensus for discrete-time heterogeneous multi-agent systems with link failures and actuator/sensor faults

Abstract: In this paper, a distributed control protocol is presented for discrete-time heterogeneous multi-agent systems in order to achieve formation consensus against link failures and actuator/sensor faults under fixed and switching topologies. A model equivalent method is proposed to deal with the heterogeneous system consists of arbitrary order systems with different parameters. Based on graph theory and Lyapunov theory, stability conditions to solve formation consensus problem are developed for the underlying heterogeneous systems with communication link failures. In order to tolerate actuator/sensor faults, a distributed adaptive controller is proposed based on fault compensation. The desired control is designed by linear matrix inequality approach together with cone complementarity linearisation algorithm. After applying the new control scheme to heterogeneous systems under the directed topologies with link failures and faults, the resulting closed-loop heterogeneous system is validated to be stable. The effectiveness of the new formation consensus control strategy and its robustness are verified by simulations.

Stability and bifurcation control of a delayed fractional-order eco-epidemiological model with incommensurate orders

Abstract: In this paper, a delayed fractional eco-epidemiological model with incommensurate orders is proposed, and a control strategy of this model is discussed. Firstly, for the system with no controller, the stability and Hopf bifurcation with respect to time delay are investigated. Secondly, under the influence of the controller, the stability and Hopf bifurcation of the system is discussed, and it is indicated that the stability of the system can be changed by increasing the feedback control delay. In particular, a separate study is carried out on the bifurcation with respect to the extended feedback delay, and the bifurcation point is calculated. At last, to support the theoretical results, some numerical simulations are depicted.

Event-triggered sliding mode control of uncertain switched systems under denial-of-service attacks

Abstract: This study is concerned with the event-triggered sliding mode control problem for a class of cyber-physical switched systems, in which the Denial-of-Service (DoS) attacks may randomly occur according to the Bernoulli distribution. A key issue is how to design the output feedback sliding mode control (SMC) law for guaranteeing the dynamical performance of the closed-loop system under DoS attacks. To this end, an event-triggered mechanism is firstly introduced to reduce the communication load, under which the measurement signal is transmitted only when a certain triggering condition is satisfied. An usable output signal for the controller is constructed to compensate the effect of unmeasured states and DoS attacks. And then, a dynamic output feedback sliding mode controller is designed by means of the attack probability and the compensated output signals. Both the reachability and the mean-square exponential stability of sliding mode dynamics are investigated and the corresponding sufficient conditions are obtained. Finally, some numerical simulation results are provided.

Resilient event-triggered consensus control for nonlinear muti-agent systems with DoS attacks

Abstract: The distributed event-triggered secure consensus control is discussed for multi-agent systems (MASs) subject to DoS attacks and controller gain variation. In order to reduce unnecessary network traffic in communication channel, a resilient distributed event-triggered scheme is adopted at each agent to decide whether the sampled signal should be transmitted or not. The event-triggered scheme in this paper can be applicable to MASs under denial-of-service (DoS) attacks. We assume the information of DoS attacks, such as the attack period and the consecutive attack duration, can be detected. Under the introduced communication scheme and the occurrence of DoS attacks, a new sufficient condition is achieved which can guarantee the security consensus performance of the established system model. Moreover, the explicit expressions of the triggering matrices and the controller gain are presented. Finally, simulation results are provided to verify the effectiveness of the obtained theoretical results.

Further mean-square asymptotic stability of impulsive discrete-time stochastic BAM neural networks with Markovian jumping and multiple time-varying delays

Abstract: In this paper, the asymptotic stability analysis is investigated for a kind of discrete-time bidirectional associative memory (BAM) neural networks with the existence of perturbations namely, stochastic, Markovian jumping and impulses. Based on the theory of stability, a novel Lyapunov–Krasovskii function is constructed and by utilizing the concept of delay partitioning approach, a new linear-matrix-inequality (LMI) based criterion for the stability of such a system is proposed. Furthermore, the derived sufficient conditions are expressed in the structure of LMI, which can be easily verified by a known software package that guarantees the globally asymptotic stability of the equilibrium point. Eventually, a numerical example with simulation is given to demonstrate the effectiveness and applicability of the proposed method.

Composite learning adaptive sliding mode control of fractional-order nonlinear systems with actuator faults

Abstract: This paper considers the tracking control of fractional-order nonlinear systems (FONSs) in triangular form with actuator faults by means of sliding mode control (SMC) and composite learning SMC (CLSMC). In SMC design, a fractional sliding surface is introduced, and an adaptation law is designed to update the estimation of the mismatched parametric uncertainty in the actuator faults. The proposed SMC can guarantee the convergence of the tracking error where a persistent excitation (PE) condition should be satisfied. To overcome this limitation, by using the online recorded data and the instantaneous data, a prediction error of the parametric uncertainty is defined. Both the tracking error and the prediction error are utilized to generate a composite learning law. A composite learning law is designed by using the prediction error and the tracking error. The proposed CLSMC can guarantee not only the stability of system but also the accurate estimation of the parametric uncertainties in the actuator faults. In CLSMC, only an interval-excitation (IE) condition that is weaker than the PE one should be satisfied. Finally, simulation example is presented to show the control performance of the proposed methods.

A modified Lyapunov functional with application to stability of neutral-type neural networks with time delays

Abstract: This paper investigates the problem for stability of neutral-type dynamical neural networks involving delay parameters. Different form the previously reported results, the states of the neurons involve multiple delays and time derivative of states of neurons include discrete time delays. The stability of such neural systems has not been given much attention in the past literature due to the difficulty of finding Lyapunov functionals which are suitable for stability analysis of this type of neural networks. This paper constructs a generalized Lyapunov functional by introducing new terms into the well-known Lyapunov functional that enables us to conduct a theoretical investigation into stability analysis of delayed neutral-type neural systems. Based on this modified novel Lyapunov functional, sufficient criteria are derived, which guarantee the existence, uniqueness and global asymptotic stability of the equilibrium point of the neutral-type neural networks with multiple delays in the states and discrete delays in the time derivative of the states. The applicability of the proposed stability conditions rely on testing two basic matrix properties. The constraints impose on the system matrices are determined by using nonsingular M-matrix condition, and the constraints imposed on the coefficients of the time derivative of the delayed state variables are derived by exploiting the vector-matrix norms. We also note that the obtained stability conditions have no involvement with the delay parameters and expressed in terms of nonlinear Lipschitz activation functions. We present a constructive numerical example for this class of neural networks to give a systematic procedure for determining the imposed conditions on the whole system parameters of the delayed neutral-type neural systems.

Event-triggered bipartite leader-following consensus of second-order nonlinear multi-agent systems under signed digraph

Abstract: This paper investigates the bipartite leader-following consensus of second-order multi-agent systems with signed digraph topology. To significantly reduce the communication burden, an event-triggered control algorithm is proposed to solve the bipartite leader-following consensus problem, where a novel event-triggered function is designed. Under some mild assumptions on the network topology and node dynamics, a sufficient condition is derived using Lyapunov stability method and matrix theory to guarantee the bipartite consensus. In particular, it is shown that the continuous communication can be avoided and the Zeno-behavior can be excluded for the designed event-triggered algorithm. Numerical simulations are presented to illustrate the correctness of the theoretical analysis.

Design of a Fast Real-Time LPV Model Predictive Control System for Semi-Active Suspension Control of a Full Vehicle

Abstract: This article is concerned with the control of a Semi-Active suspension system of a 7DOF Full Vehicle model, equipped with four Electro Rheological (ER) dampers, taking into account their incipient dissipativity constraints. Herein, a real-time, fast, advanced control structure is presented within the Model Predictive Control framework for Linear Parameter Varying (LPV) systems. The control algorithm is developed to provide a suitable trade-off between comfort and handling performances of the vehicle in a very limited sampling period ( T s = 5 ms ), in view of a possible realtime implementation on a real vehicle. The control structure is tested and compared to other standard fast control approaches. Full nonlinear realistic simulation results illustrate the overall good operation and behaviour of the proposed control approach.

A novel general type-2 fuzzy controller for fractional-order multi-agent systems under unknown time-varying topology

Abstract: In this paper, a robust adaptive control scheme is proposed for the leader following control of a class of fractional-order multi-agent systems (FMAS). The asymptotic stability is shown by a linear matrix inequality (LMI) approach. The nonlinear dynamics of the agents are assumed to be unknown. Moreover, the communication topology among the agents is assumed to be unknown and time-varying. A deep general type-2 fuzzy system (DGT2FS) using restricted Boltzmann machine (RMB) and contrastive divergence (CD) learning algorithm is proposed to estimate uncertainties. The simulation studies presented indicate that the proposed control method results in good performance under time-varying topology, unknown dynamics and external disturbances. The effectiveness of the proposed DGT2FS is verified also on modeling problems with high dimensional real-world data sets.

Lag projective synchronization of fractional-order delayed chaotic systems

Abstract: This paper considers the lag projective synchronization of fractional-order delayed chaotic systems. The lag projective synchronization is achieved through the use of comparison principle of linear fractional equation at the presence of time delay. Some sufficient conditions are obtained via a suitable controller. The results show that the slave system can synchronize the past state of the driver up to a scaling factor. Finally, two different structural fractional order delayed chaotic systems are considered in order to examine the effectiveness of the lag projective synchronization. Feasibility of the proposed method is validated through numerical simulations.

Time-varying output formation tracking of heterogeneous linear multi-agent systems with multiple leaders and switching topologies

Abstract: This paper studies the time-varying output formation tracking problems for heterogeneous linear multi-agent systems with multiple leaders in the presence of switching directed topologies, where the agents can have different system dynamics and state dimensions. The outputs of followers are required to accomplish a given time-varying formation configuration and track the convex combination of leaders’ outputs simultaneously. Firstly, using the neighboring relative information, a distributed observer is constructed for each follower to estimate the convex combination of multiple leaders’ states under the influences of switching directed topologies. The convergence of the observer is proved based on the piecewise Lyapunov theory and the threshold for the average dwell time of the switching topologies is derived. Then, an output formation tracking protocol based on the distributed observer and an algorithm to determine the control parameters of the protocol are presented. Considering the features of heterogeneous dynamics, the time-varying formation tracking feasible constraints are provided, and a compensation input is applied to expand the feasible formation set. Sufficient conditions for the heterogeneous multi-agent systems with multiple leaders and switching directed topologies to achieve the desired time-varying output formation tracking under the designed protocol are proposed. Finally, simulation examples are given to validate the theoretical results.

Consensus control via iterative learning for distributed parameter models multi-agent systems with time-delay

Abstract: Most of the available results of iterative learning control (ILC) are that solve the consensus problem of lumped parameter models multi-agent systems. This paper considers the consensus control problem of distributed parameter models multi-agent systems with time-delay. By using the knowledge between neighboring agents, considering time-delay problem in the multi-agent systems, a distributed P-type iterative learning control protocol is proposed. The consensus error between any two agents in the sense of L2 norm can converge to zero after enough iterations based on proposed ILC law. And then we extend these conclusions to Lipschitz nonlinear case. Finally, the simulation result shows the effectiveness of the control method.

Fractional-order adaptive neuro-fuzzy sliding mode H∞ control for fuzzy singularly perturbed systems

Abstract: This paper investigates the fractional-order (FO) adaptive neuro-fuzzy sliding mode control issue for a class of fuzzy singularly perturbed systems subject to the matched uncertainties and external disturbances. Firstly, a novel FO fuzzy sliding mode surface is presented. Secondly, by introducing an appropriate e-dependent Lyapunov function, some H∞ performance analysis criteria are given, which also ensure the robust stability of the sliding mode dynamics. Furthermore, a hybrid neuro-fuzzy network system (HNFNS) is introduced to estimate the matched uncertainty. Moreover, an FO adaptive fuzzy sliding mode controller is designed to drive the state trajectories of fuzzy singularly perturbed systems to the predefined FO sliding mode surface within a finite-time. Finally, two verification examples are presented to illustrate the validity of the proposed FO control scheme.