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In this paper, a new fault diagnosis (FD) and fault tolerant control (FTC) algorithm for a non-Gaussian nonlinear singular stochastic distribution control (SDC) system is studied. The rational square-root fuzzy logic model is used to approximate the output probability density function of non-Gaussian processes and a Takagi-Sugeno (T-S) fuzzy model is employed to transform the non-Gaussian nonlinear SDC system into a fuzzy SDC system. An adaptive fuzzy fault diagnosis observer is constructed to achieve reconstruction of system state and fault. Based on the estimated fault information, the controller is reconfigured by minimising the performance index with regard to the rational entropy subjected to mean constraint. Minimum rational entropy fault tolerant control is introduced to make the output of the past-fault SDC system still have the minimum uncertainty. Simulation results are provided to demonstrate the validity of the FD and minimum rational entropy FTC algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of non-smooth state feedback control for a group of high-order p-normal form interconnected nonlinear systems with prescribed performance. Each subsystem is in the form of triangular structure with the unmodelled dynamics. With the help of appropriate Lyapunov function, a state feedback controller is constructed via backstepping method. It is strictly proved that the closed-loop system is asymptotically stable and both transient and steady-state performances of the outputs are preserved. Finally, simulation results are presented to illustrate the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of cooperative state estimation of general linear multi-agent systems subject to heterogeneous bounded external disturbances. This problem specifies the objective that each agent estimates its own state by using only relative information from its neighbours. Because of the existence of external disturbances, the problem is challenging especially when stringent exact estimation performance is desired. To this end, two cooperative estimation protocols are proposed, including the discontinuous nonlinear protocol for exact state estimation and the continuous nonlinear protocol for estimation with bounded errors. The overall network stability and convergence properties are analysed using the Lyapunov function method. A simulation example has also been used to demonstrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, the problem of adaptive neural network asymptotical tracking is investigated for a class of nonlinear system with unknown function, external disturbances and input quantisation. Based on neural network technique, an adaptive asymptotical tracking controller is provided for an uncertain nonlinear system via backstepping method. In order to reduce complexity of the control algorithm in the backstepping design process, a sliding mode differentiator is employed to estimate the virtual control law and only two parameters need to be estimated via adaptive control technique. The stability of the closed-loop system is analysed by using Lyapunov function method and zero-tracking error performance is obtained in the presence of unknown nonlinear function, external disturbances and input quantisation. Finally, an application example is employed to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the adaptive consensus control for a class of nonlinear systems with different unknown control directions where communications among the agents are represented by a directed graph. Based on the backstepping technique, a fully distributed adaptive control approach is proposed without using global information of the topology. Meanwhile, a novel Nussbaum-type function is proposed to address the consensus control with unknown control directions. It is proved that boundedness of all closed-loop signals and asymptotic consensus tracking for all the agents' outputs are ensured. In simulation studies, a numerical example is illustrated to show the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

This paper is focused on the optimisation control design for a class of nonlinear time-delay systems. A distributed model predictive control scheme is proposed for the first time to apply the constraint control input in manipulating the presence of time-delay. The continuous controller is divided into two parts in terms of the stabilisation and the optimisation, which guarantees the stability of the closed-loop systems based on the appropriate assumptions. The novelty of this paper is a reasonable separation of the actual control to deal with the function with time-delay. Finally, a simulation is given to verify the correctness of the theoretical results. [ABSTRACT FROM AUTHOR]

The event-triggered adaptive control problem of stochastic nonlinear multi-agent systems subject to stochastic faults and asymmetric output constraints is investigated in this paper. Radial basis function neural networks (RBFNNs) are employed to adaptively approximate the unknown nonlinearities and changes in system dynamics model due to stochastic failures. By utilising a one-to-one nonlinear mapping, the asymmetric output constraints stochastic system is converted into a system without any constraints. Furthermore, to save the communication resources between controller and actuator, an improved dynamic event-triggered mechanism is developed, which contains threshold parameters and an exponential convergence term. Then, based on the stochastic Lyapunov function method, an event-triggered adaptive fault-tolerant controller is proposed for the considered systems. It is shown that the developed adaptive fault-tolerant controller can guarantee that all the signals remain semi-globally uniformly ultimately bounded while the output constraint is satisfied, even if the system is affected by stochastic failures. Eventually, the example results are provided to illustrate the effectiveness of the proposed control methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a dual backstepping variable structure switching control strategy to realise the stable reference signal tracking of bounded uncertain nonlinear system. Compared with the conventional backstepping control scheme, the proposed approach contains double distinct backstepping recursive sequences instead of the single sequence. According to the saturation switching condition, the first backstepping sub-controller is applicable to the saturation switching phase to ensure stability in a large region. The second backstepping sub-controller is applicable to the linear transient phase to shrink the convergence domain further for improving the control performance. The proposed control mechanism consists of double distinct backstepping sub-controllers which are regulated and tuned based on variable structure switching logic. The most significant advantage is that the switching controllers expand the traditional single backstepping variable structure control to double backstepping. This method not only guarantees the tracking error global uniform convergence but also improves the tracking control performance. In addition, this method absorbs both the advantages of backstepping and variable structure to protect the beneficial nonlinear properties in states and suppress the chatting. To evaluate the control performance and tracking effectiveness of the proposed control mechanism, a numerical comparing example is presented and discussed. [ABSTRACT FROM AUTHOR]

In this article, we investigate the finite-time output feedback control issue for switched nonlinear systems. In the design procedure, fuzzy logic systems are used for identifying the unknown functions in switched nonlinear systems. A common state observer is constructed such that the unmeasured states can be measured. Based on the framework of the backstepping algorithm, an observer-based finite-time adaptive tracking control strategy is proposed via the common Lyapunov function approach. The stability analysis process confirms in detail that all the closed-loop signals are bounded under arbitrary switchings and the tracking error can converge to a small neighbourhood of the origin after a finite period of time. In order to verify the feasibility of the proposed control scheme, a numerical simulation example is shown in this paper. [ABSTRACT FROM AUTHOR]

In the paper, some classes of time-varying nonlinear switched systems are considered. The stability problem is investigated. The Lyapunov direct method and the theory of differential inequalities are used. The non-stationary switched comparison equation with hybrid degrees is constructed for the considered system. Switching in the comparison equation can be caused by changing the operating modes, as well as changing non-stationary parameters of the system. Sufficient conditions of the asymptotic and finite-time stability are obtained. The region of attraction for the given equilibrium position is estimated. Some examples are presented to illustrate the results. In particular, some classes of nonlinear mechanical system are studied. [ABSTRACT FROM AUTHOR]

Model predictive control with less prior knowledge of system uncertainty and external disturbance is a long-standing theoretical and practical problem. In this paper, a solution is presented by proposing a disturbance observer-based adaptive nonlinear model predictive control scheme for a class of nonlinear MIMO systems. Our scheme requires the state and parameterdependent state-space model to linearise the nonlinear system along the prediction horizon. To cope with the unknown system uncertainty, the multiple estimation model and the concept of second-level adaptation [Pandey, V. K., Kar, I., & Mahanta, C. (2014). Multiple models and second level adaptation for a class of nonlinear systems with nonlinear parameterisation. In Industrial and Information Systems (ICIIS), 2014 9th International Conference on (pp. 1–6). IEEE.] technique for the nonlinear system is used. To ensure the boundedness of the estimated parameter, a projection-based adaptive law [Hovakimyan, N., & Cao, C. (2010). L1 adaptive control theory: Guaranteed robustness with fast adaptation. SIAM.] is used. Using a twin-rotor MIMO system (TRMS), the effectiveness of the proposed control algorithm has been verified. Simulation and real-time results show that the proposed control algorithm performs better than the existing control algorithm in the presence of unknown external disturbance and parameter uncertainties. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive smooth finite-time neural controller design for large-scale stochastic nonlinear systems subject to command filter and time-delay interactions. The main features are (1) the communication interconnection has the output delay between subsystems; (2) the smooth finite time strategy is given for stochastic disturbances; (3) all states are confined in the constrained space without feasibility conditions. First, the state time-delay interactions are tackled by the appropriate establishment of Lyapunov–Krasovskii functionals. Second, for full-state constraints, state-related mappings are employed to remove the feasibility. Then, by constructing the switching virtual control signals, the smooth fast finite-time feature is attached to the controller. Finally, based on finite-time stability theory in probability, command filter, and backstepping technique, the smooth fast finite-time neural controller is constructed to make that all system states converge in finite time, all closed-loop variables are bounded in probability, and full-state constraints are satisfied. Simulation results are provided to test the correctness of the method. [ABSTRACT FROM AUTHOR]

In this paper, the global output regulation problem (ORP) for nonlinear output feedback affine systems with unknown functions (UFs) and unmeasured states is studied. Firstly, a Lemma is proposed to solve not only the global ORP of nonlinear systems, but also the 'explosion of terms' problem of backstepping. Secondly, the control scheme is designed based on backstepping method, which avoids an Assumption to solve the ORP. Therefore, the algorithm reduces the conservatism and increases the applicability. Finally, the algorithm is applied to Duffing system to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered adaptive decentralised control strategy for a class of switched interconnected nonlinear systems is presented, which considers full-state constraints and unmodeled dynamics, simultaneously. In the controller design process, the approximation capability of radical basis function neural networks (RBF NNs) is used to estimate the unknown functions of the system. The interference caused by unmodeled dynamics is overcome by introducing a dynamic signal. In addition, the barrier Lyapunov function (BLF) is constructed for each subsystem to dispose the influence of state constraints. An adaptive control scheme with event-triggered mechanism is proposed to reduce communication burden. It is shown that the proposed event-triggered controller and an adaptive neural decentralised control strategy are designed such that all the signals in the closed-loop system are guaranteed to be bounded, the tracking errors of the system converge to a small neighbourhood of the origin and the full state constraints are not violated. Finally, a simulation result shows the effectiveness of the developed approach. [ABSTRACT FROM AUTHOR]

The application of neural networks can present some limitations for the control of strongly nonlinear systems. In this paper, a new control scheme based on a neural multicontroller (NMC) is proposed. Indeed, the developed strategy considers a set of local neural controllers which adapt their parameters thanks to an online adaptation algorithm. The instantaneous choice of the adequate local controller is based on a switching mechanism. The advantages of the proposed method are (1) to avoid the computational complexity issues due to the search for an optimal adapting rate when a single neural controller is used, and in presence of strongly nonlinear systems, and (2) to improve the control law by selecting the suitable controller which generates the most valid control law satisfying the desired closed-loop performances. Numerical examples are illustrated to evaluate the performance of the proposed control scheme compared to the classical one. An application of the new strategy on a chemical reactor model validates satisfactory results. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered fixed-time control scheme is developed for a class of stochastic nonlinear systems with prescribed boundary constraints and actuator faults. For the controlled systems with unknown nonlinear functions, the neural networks are employed to reestablish the system model. Based on the event-triggered control technology, an original adaptive fixed-time control strategy with prescribed performance and the fault state is proposed by using the backstepping technique. Under the developed adaptive controller, the tracking error satisfies the predefined boundary functions and all the closed-loop system signals are bounded in probability in a fixed time, and the convergence time is irrelevant to the initial states of the system. The practicability of the designed controller is illustrated by a simulation example. [ABSTRACT FROM AUTHOR]

This paper studies the problem of synchronisation to a desired trajectory for non-linear multi-agent systems. By introducing extended state observer approach, decentralised adaptive controllers are designed for distributed systems which have non-identical unknown non-linear dynamics. The non-identical unknown non-linear dynamics allows for a tracked command dynamics which is also non-linear and unknown. State variables of agents can be obtained only in the case where leader agent and the network communication topology for multi-agent systems is strongly connected digraph network structures. A Lyapunov-function-based approach is given to show that the tracking error is ultimately bounded. Some simulation results are given to demonstrate the effectiveness of the developed techniques in this paper. [ABSTRACT FROM PUBLISHER]

In this paper, a switching tuning backstepping control scheme is proposed to stabilise a class of mixed switched nonseparated parameterised nonlinear systems. First, the mixed arbitrarily switching nonlinear system consists of different structure subsystems. The subsystems contain different nonseparated parameters nonlinearly. Next, an uniform backstepping controller is designed to stabilise the nonlinear subsystems. Then, a unidirectional exploring switching tuning scheme is proposed to inject into the controller to jumping compensate the double uncertainty and unknown online. The main contribution of the paper is that the adaptive backstepping controller with the exploring switching tuning scheme stabilises the mixed arbitrarily switching nonlinear systems. The detailed simulation synthesis is finally given to exhibit the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

For the nonlinear systems with input saturation constraint, a novel MTN-based output-feedback control strategy is developed via backstepping in this paper. Firstly, an MTN-based nonlinear state observer is designed to estimate the unmeasured states. Secondly, multi-dimensional Taylor networks (MTNs) are used to deal with the unknown nonlinear functions, based on this, the procedure of the adaptive MTN output-feedback controller design is developed by combining backstepping approach and dynamic surface control technique, and then the stability of the closed-loop system is proved based on the principle of Lyapunov stability theory. Finally, the theoretical results of this paper are demonstrated by two examples. [ABSTRACT FROM AUTHOR]

This paper investigates finite-horizon optimal control problem of continuous-time uncertain nonlinear systems. The uncertainty here refers to partially unknown system dynamics. Unlike the infinite-horizon, the difficulty of finite-horizon optimal control problem is that the Hamilton–Jacobi–Bellman (HJB) equation is time-varying and must meet certain terminal boundary constraints, which brings greater challenges. At the same time, the partially unknown system dynamics have also caused additional difficulties. The main innovation of this paper is the proposed cyclic fixed-finite-horizon-based reinforcement learning algorithm to approximately solve the time-varying HJB equation. The proposed algorithm mainly consists of two phases: the data collection phase over a fixed-finite-horizon and the parameters update phase. A least-squares method is used to correlate the two phases to obtain the optimal parameters by cyclic. Finally, simulation results are given to verify the effectiveness of the proposed cyclic fixed-finite-horizon-based reinforcement learning algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time memory fault detection filter (MFDF) is designed for nonlinear discrete systems with randomly occurring deception attacks, where the phenomenon of the randomly occurring deception attacks is characterised by a Bernoulli distributed random variable with known probability. To be specific, the finite-horizon data are employed to construct the MFDF. The main purpose of this paper is to design the MFDF such that, for all nonlinearities, external disturbances and randomly occurring deception attacks, the resultant augmented system is finite-time stable and attains the H ∞ performance. Accordingly, some sufficient conditions are developed to guarantee the existence of the desired fault detection filter parameters, where the solvability of the addressed problem is verified by the feasibility of certain matrix inequalities. Moreover, in order to show that the MFDF has better detection performance, a non-memory fault detection filter (NMFDF) is constructed to compare with the MFDF. Finally, two numerical simulations are utilised to illustrate the effectiveness of the proposed fault detection strategies and explain the superiority of the proposed MFDF. [ABSTRACT FROM AUTHOR]

In this paper, the iterative learning control problem for a class of nonlinear singular impulsive systems is discussed. Then, a D-type (derivative-type) iterative learning control algorithm is presented such that the output tracks the desired output trajectory as accurate as possible. Furthermore, the sufficient condition for the convergence of the proposed algorithm is established in detail. Finally, a numerical example is included to corroborate the theoretical analyses. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of designing a controller for a class of discrete-time nonlinear systems which is represented by discrete-time polynomial fuzzy model. Most of the existing control design methods for discrete-time fuzzy polynomial systems cannot guarantee their Lyapunov function to be a radially unbounded polynomial function, hence the global stability cannot be assured. The proposed control design in this paper guarantees a radially unbounded polynomial Lyapunov functions which ensures global stability. In the proposed design, state feedback structure is considered and non-convexity problem is solved by incorporating an integrator into the controller. Sufficient conditions of stability are derived in terms of polynomial matrix inequalities which are solved via SOSTOOLS in MATLAB. A numerical example is presented to illustrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper studies the problem of robust stability and stabilisation for uncertain large-scale interconnected nonlinear descriptor systems via proportional plus derivative state feedback or proportional plus derivative output feedback. The basic idea of this work is to use the well-known differential mean value theorem to deal with the nonlinear model such that the considered nonlinear descriptor systems can be transformed into linear parameter varying systems. By using a parameter-dependent Lyapunov function, a decentralised proportional plus derivative state feedback controller and decentralised proportional plus derivative output feedback controller are designed, respectively such that the closed-loop system is quadratically normal and quadratically stable. Finally, a hypersonic vehicle practical simulation example and numerical example are given to illustrate the effectiveness of the results obtained in this paper. [ABSTRACT FROM AUTHOR]

The output tracking control problem is considered for a class of uncertain strict-feedback nonlinear systems with time-varying delays. In the paper, the time-varying delays are assumed to be any non-negative continuous and bounded functions, and it is not necessary for their derivatives to be less than one. It is also assumed that the upper bounds of nonlinear delayed state perturbations and external disturbances are unknown. On the basis of backstepping algorithm, a novel design method is proposed by which some simple adaptive robust output tracking control schemes are synthesised. The proposed design method can avoid the repeated differentiation problem which appears in using the conventional backstepping algorithm, and need not know all the nonlinear upper bound functions of uncertainties, which are repeatedly employed at each step of the backstepping algorithm. In particular, it is not necessary to know any information on the time-varying delays to construct our simple output tracking control schemes. It is also shown that the tracking error can converge uniformly exponentially towards a neighbourhood of the origin. Finally, a numerical example and its simulations are provided to demonstrate the design procedure of the simple method proposed in the paper. [ABSTRACT FROM PUBLISHER]

This paper presents an adaptive neural tracking control scheme for strict-feedback stochastic nonlinear systems with guaranteed transient and steady-state performance under arbitrary switchings. First, by utilising the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, radial basis function neural networks approximation are used to handle unknown nonlinear functions and stochastic disturbances. At last, by using the common Lyapunov function method and the backstepping technique, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterisation, and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded, and the prescribed tracking control performance are guaranteed under arbitrary switchings. Three examples are presented to further illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper considers the issue of adaptive neural decentralised tracking control for a class of output-constraint switched interconnected nonlinear systems with unknown backlash-like hysteresis control input. First, neural networks (NNs) are applied to approximate unknown nonlinear functions, and an NNs switched state observer is designed to estimate unmeasured system states. Then, the dynamic surface control technique is used to avoid the influence of explosion of complexity. In addition, the problem of output constraints is solved by introducing the barrier Lyapunov functions. Based on the Lyapunov stability theory, all signals in the switched closed-loop system can be verified to be uniformly ultimately bounded under the proposed control method. Moreover, the system output can track the target trajectory well within a small bounded error. Finally, a numerical simulation result is given to illustrate the effectiveness of the adaptive decentralised control scheme. [ABSTRACT FROM AUTHOR]

The paper studies the fault identification problem for nonlinear control systems under unmatched disturbances. A novel approach to construct a sliding mode observer is proposed for systems that do not satisfy common conditions required for fault estimation, in particular matching condition, minimum phase condition, and detectability conditions. The suggested approach is based on the reduced order model of the original system and on the canonical form of matrices describing such a model. This allows to reduce the complexity of sliding mode observers and relax the limitations imposed on the original system. Three different cases for disturbances are considered and the appropriate solutions are suggested. [ABSTRACT FROM AUTHOR]

The adaptive neural tracking control problem of a class of strict-feedback nonlinear systems with unknown control directions (UCD) and full-state constraints is investigated in this paper. The neural network (NN) is adopted to identify the totally unknown nonlinear functions. In the meanwhile, by resorting to the Nussbaum gain technique, the effects caused by the UCD and output dead zone are counteracted. Given physical limits and safety demands, a novel barrier Lyapunov function (BLF)-based adaptive neural control scheme is devised for the strict-feedback nonlinear systems to ensure that the constraints are not violated during operations. Besides, a rigorous theoretical analysis has been given to indicate that all of the closed-loop signals are bounded and the tracking error achieves asymptotic convergence performance. Finally, the effectiveness and flexibility of our proposed scheme are illustrated by two numerical examples. [ABSTRACT FROM AUTHOR]

This paper studies the interval type-2 fuzzy decentralised event-triggered control design problem for the nonlinear time-delay interconnected systems. The considered plant is first represented by an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy time-delay interconnected system and then an event-triggered mechanism is established through using the sampled states. By using the PDC algorithm, a fuzzy decentralised event-triggered controller is developed. Furthermore, the asymptotic stable conditions with a H ∞ performance of closed-loop system are obtained, which are formulated in the form of linear matrix inequalities. Finally, an example of the nonlinear Tuned Mass Damper system with time delays is given to illustrate the effectiveness of the proposed fuzzy decentralised controller. [ABSTRACT FROM AUTHOR]

This paper is concerned with the adaptive fuzzy control for uncertain switched discrete-time nonlinear systems in a pure-feedback form under arbitrary switchings. The design and the stability analysis of the adaptive fuzzy control with state constraints are based on the adaptive fuzzy backstepping control and the Lyapunov function. The feature of the proposed approach is as follows. (i) The proposed adaptive fuzzy control is designed by suppressing the explosion problem of the complexity in the backstepping control, (ii) In the fuzzy inference approach, the nonlinear uncertainties are approximated by using the fuzzy logic systems based on the simplified extended single-input rule modules, and (iii) The proposed estimator to take the estimates for the unmeasurable states, the adjustable parameters and the real control is in a simplified structure designed. The simulation experiment provides that the proposed approach improves the system performance. [ABSTRACT FROM AUTHOR]

Model structure selection for nonlinear system identification has been widely studied over the last 30 years due to its great importance. There are many methods in the literature to deal with structure selection, although these methods have their specific benefits, they face some difficulties in selecting the structure for a parsimonious model. In this paper, two methods based on the Randomised Model Structure Selection (RaMSS) approach are introduced in order to deal with the structure selection problem. The first one is the Randomised Model Structure Selection with Error Reduction Ratio (RaMSS-ERR) that uses the error reduction ratio as a filter for the terms analysis, improving the convergence, and the second one is the Randomised Model Structure Selection with Genetic Inheritance (RaMSS-EGI) that uses a genetic inheritance in order to get a faster convergence. The methods were applied to benchmark models and the results are encouraging. Applications to systems with a large candidate regressor set and to a continuous stirred-tank reactor are also carried out. The results show that the proposed method may be used to identify both linear and nonlinear model structures with a reduced number of iterations, computational time, and number of explored models. [ABSTRACT FROM AUTHOR]

In this paper, the stabilisation problem is considered for a class of nonlinear interconnected systems with matched uncertainties and mismatched unknown interconnections. A composite sliding surface is designed firstly, and a set of conditions is developed to guarantee that the corresponding sliding motion is uniformly asymptotically stable. Then, decentralised state feedback sliding mode control is proposed to drive the interconnected systems to the designed sliding surface in finite time, and a sliding motion is maintained thereafter. The bounds on the uncertainties and interconnections have more general nonlinear forms, which are employed in the control design to reject the effects of uncertainties and unknown interconnections to enhance the robustness. It is not required either the isolated nominal subsystems linearisable or the interconnections linearisable. Finally, a numerical simulation example is presented to demonstrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The problem of delay-dependent stability is concerned for two-dimensional switched systems in Fornasini-Marchesini local state-space model with mixed time-varying delays and with a class of generalised Lipschitz nonlinearities in this paper. The mixed time-varying delays consist of both the discrete and the distributed delays, and the time-varying delays are allowed in the states. Adopting the fast average dwell-time switching technique with Lyapunov functional, a sufficient condition of the exponential stability for two-dimensional nonlinear switched systems with all subsystems unstable is derived in terms of linear matrix inequality. Specifically, two-dimensional Abel lemma-based finite-sum inequalities approach and Jensen inequalities approach are applied to reduce the conservativeness of the result. The obtained result on stability analysis is then utilised to design a dynamic output feedback controller to stabilise the nonlinear closed-loop switched system. Finally, two numerical examples are shown to demonstrate the validity of the proposed results. [ABSTRACT FROM AUTHOR]

This paper presents a prescribed performance control for MIMO stochastic discrete-time nonlinear systems in a strict-feedback form using a set of noisy measurements. The prescribed performance control is proposed as follows. Transforming the un-constrained states into the constrained states, the proposed prescribed performance control with state constraints is designed based on the approach of backstepping control and the Lyapunov function without using approximate approaches. The nonlinear uncertainty is approximated as the fuzzy logic system based on the simplified extended single input rule modules to reduce the number of the fuzzy IF–THEN rules. The estimator to take the estimates for the unmeasurable states and the adjustable parameters is in a simplified structure designed. The effectiveness of the proposed approach is indicated through the simulation experiment of a simple numerical system. [ABSTRACT FROM AUTHOR]

In this paper, a robust adaptive fault tolerant controller guaranteeing with time-varying performance bounds is designed for a class of time delay uncertain nonlinear systems subject to actuator failures and external disturbance. The influence of time delay on the system is mitigated and the system performance can be guaranteed by introducing a positive nonlinear control gain function and the generalised restricted potential function. A new method with more design degrees of freedom is developed to ensure the norm of the system state within a-priori, user-defined time varying performance bounds. Using the online estimation information provided by adaptive mechanism, a robust adaptive fault-tolerant control method guaranteeing time varying performance bounds is proposed. It is shown that all the signals of the resulting closed-loop system are bounded and the system state less than a-priori, user-defined performance bounds. Finally, simulation results are given to demonstrate the efficacy of the proposed fault-tolerant control method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of iterative learning control is addressed for a class of uncertain nonlinear systems with unknown input–output coupling parameters. Firstly, we prove that the control system is realisable if and only if all the input–output coupling parameters are nonzero. Secondly, to surmount the obstacle arising from the unknown input–output coupling parameters, a learning based identification technique is developed for the unknown input–output coupling parameters. Thirdly, a control protocol composed of iterative learning control update law and current state feedback is proposed for the controlled system. The findings suggest that the state feedback part does not change the realizability of controlled system and can be used to improve the transient tracking performance. Especially under certain condition, it can guarantee the monotone convergence of ILC process. Finally, simulation examples are provided to illustrate the effectiveness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

In this paper, the observer-based synergetic adaptive neural network control method is designed for a class of discrete-time systems with dead-zone. A macro-variable is introduced by a synergetic approach to control theory and neural networks are utilised to estimate unmeasured states and unknown functions in the system. Furthermore, by employing an adaptive design procedure and Lyapunov stability theory, the closed-loop system stability is guaranteed, and the desired system performance is achieved simultaneously. Finally, some simulation results are given to prove the validity of the developed control method. [ABSTRACT FROM AUTHOR]

In this paper, a sampled-data adaptive output feedback controller is proposed for a class of uncertain nonlinear systems with unmeasured states, unknown dynamics and unknown time-varying external disturbances. To approximate uncertain nonlinear functions, radial basis function neural networks (RBFNNs) are employed. The state observer and the disturbance observer (DO) are constructed to estimate the unmeasured state and the external disturbance, respectively. Then, the sampled-data adaptive output feedback controller and adaptive laws are designed by using the backstepping design technique. The allowable sampling period T is derived to guarantee that all states of the resulting closed-loop system are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper deals with finite-time stabilisation problem for a class of high-order nonlinear systems. By generalising a dynamic gain design method and adding a power integrator technique, a new state feedback controller is constructed by choosing an appropriate Lyapunov function. It is proved that the corresponding closed-loop system is globally finite-time stable by the constructed controller, and the proposed method can accelerate the convergent speed and decrease the settling time. Finally, a simulation example is given to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the sliding mode control (SMC) problem for a class of delta operator uncertain time-delay nonlinear systems through Takagi–Sugeno fuzzy models. Some new sufficient conditions for asymptotic stability analysis of the sliding motion are obtained in terms of linear matrix inequalities with some convexification techniques. Then two new dynamic SMC design methods are synthesised to force the resulting closed-loop system states onto the sliding surface in finite time. Two simulation examples are finally given to illustrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

In this paper, a gain determining method for nonlinear adaptive backstepping technique is proposed by the pole placement method. By introducing a subsystem that can be linearised as a linear system, controller gains can be solved by the pole placement method. Compared with the existing techniques, the proposed method can achieve a transient performance like a linear system with the pole placement method instead of constraining the system in a predefined performance range. Meanwhile, the proposed method does not change the structure of adaptive backstepping controllers, which preserves the advantages of the original backstepping design and avoids increasing the computing effort for the control system. In addition, the applications of the proposed method are demonstrated by three examples, including a 2nd-order nonlinear system, a 3rd-order nonlinear system, and a single-link robot arm. The corresponding simulations are conducted, and the simulation results are illustrated and analysed. Moreover, comparative simulations are conducted to verify the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a mixed event-triggered and continuous-time control method is proposed, which can guarantee finite-time stabilisation of the fixed point in a class of time-varying nonlinear systems. Benefiting from an event-triggered framework, which is constructed based on the indefinite Lyapunov theory, the communication/computation costs in the transient time can be reduced by using the proposed method. In a special case, this method is converted to a fully event-triggered control strategy for asymptotic stabilisation of the fixed point in the considered class of time-varying nonlinear systems. The effectiveness of the proposed method is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper considers the asymptotic adaptive tracking control issues for the nonstrict feedback nonlinear systems. By adding the negative exponential function to the Lyapunov function, the exponentially asymptotic convergence is realised for the first time. In addition, the prior knowledge of the lower and upper bounds of unknown nonlinear functions have been removed in this article. The unknown nonlinear functions and uncertain external disturbance are solved by using fuzzy logical systems (FLS) and the bound-estimation method. It is shown that the tracking error can converge to zero at an exponential rate and the whole signals of the controlled system are bounded. Finally, simulation examples have been presented to prove the effectiveness of the raised scheme. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered adaptive approximately optimal tracking control approach is proposed for a class of non-affine nonlinear single-input single-output (SISO) systems via output feedback. With the help of fuzzy logic systems (FLSs), a fuzzy state observer is designed for the estimation of internal states by approximating unknown nonlinear functions, where a low-pass filter (LPF) is added for the algebraic loop problem. Then, the output feedback control approach, in the backstepping framework and the event-triggered mechanism, is presented. It contains the adaptive backstepping control and the approximately optimal control via adaptive dynamic programming (ADP) technology, and the computation cost and transmission load are reduced while guaranteeing that the performance index of the system is approximately minimised. Finally, two simulation examples are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a neural network-based adaptive output-feedback control problem is investigated for a class of output-constrained switched stochastic nonlinear systems. By introducing nonlinear mapping, the asymmetric and symmetric output constrained stochastic nonlinear system is transformed into a new system without any constraint. It is the first time that a switching system is used to convert symmetric and asymmetric output constraints in the same system. An adaptive neural output-feedback controller is developed based on the backstepping technique. A state observer is designed to estimate the unmeasurable system state signals. An adaptive controller is designed to ensure that the output tracking error converges to a small region of the origin. The control scheme ensures that all signals in the closed-loop systems are semi-global uniformly ultimately bounded. Results of simulation cases are presented to prove the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy. [ABSTRACT FROM PUBLISHER]

This paper is focused on reliable gain-scheduled controller design for a class of discrete-time systems with randomly occurring nonlinearities and actuator fault. Further, the nonlinearity in the system model is assumed to occur randomly according to a Bernoulli distribution with measurable time-varying probability in real time. The main purpose of this paper is to design a gain-scheduled controller by implementing a probability-dependent Lyapunov function and linear matrix inequality (LMI) approach such that the closed-loop discrete-time system is stochastically stable for all admissible randomly occurring nonlinearities. The existence conditions for the reliable controller is formulated in terms of LMI constraints. Finally, the proposed reliable gain-scheduled control scheme is applied on continuously stirred tank reactor model to demonstrate the effectiveness and applicability of the proposed design technique. [ABSTRACT FROM AUTHOR]

This paper concerns the problem ofH∞filtering for piecewise homogeneous Markovian jump nonlinear systems. Different from the existing studies in the literatures, the existence of variations in transition rates for Markovian jump nonlinear systems is considered. The purpose of the paper is to design mode-dependent and mode-independent filters, such that the dynamics of the filtering errors are stochastic integral input-to-state stable withH∞performance index. Using the linear matrix inequality method and the Lyapunov functional method, sufficient conditions for the solution to theH∞filtering problem are derived. Finally, three examples are proposed to illustrate the effectiveness of the given theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we consider the global exponential stabilisation problem by output feedback for a class of nonlinear systems with the extended structure of nonlinearity over the existing control scheme. The new feature is that we present a newly designed reduced-order observer-based output feedback controller with a gain-scaling factor. In our proposed method, we provide more flexibility in the selection of control parameters by utilising a matrix inequality approach under the extended structure of nonlinearity. Using examples, we illustrate the improved results of the proposed control scheme over the existing control scheme. [ABSTRACT FROM AUTHOR]