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

The robust stabilisation for a general class of disturbed nonlinear systems subject to state-dependent uncertainties is addressed in this paper. Different from most existing results, the lumped uncertainty, including external disturbances and model uncertainties, is relaxed to be bounded by some nonlinear functions rather than constants in this paper. Based upon the classical design procedure of disturbance observer-based control (DOBC), a new continuous saturation-based dynamic controller is proposed to estimate the lumped uncertainty. By the delicate design of several compact sets, the qualitative robust stability of the closed-loop system is guaranteed in the sense that all the signals of the closed-loop system can converge to an arbitrarily small compact set in a finite time. Furthermore, when the external disturbances are constants, the semi-global asymptotic stability can also be guaranteed by the proposed controller. Finally, the relationship between system uncertainties and observer gain is shown by a numerical simulation example. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive tracking control strategy based on neural networks (NNs) is proposed for the stochastic nonlinear strict-feedback system with a time-varying input delay and unmeasurable states. A state observer is constructed to solve the problem of unmeasurable state and the effect of the time-varying input delay is compensated by using an auxiliary signal. All the virtual controllers and the actual controller are designed as the optimised solution of the corresponding subsystems to ensure the entire obtained controllers are optimised. To ensure that the states of the system are constrained within some given compact sets, the barrier Lyapunov function (BLF) is used to design the controller and perform stability analysis. Meanwhile, this paper adds a self-triggered mechanism to reduce the communication burden of the data transfer. Thus, the proposed control strategy can achieve optimised control and all system states can be constrained within the given ranges. On the other hand, all closed-loop signals of system are bounded in probability. Finally, it is demonstrated that the proposed control strategy can achieve the expected system performance by a simulation experiment. [ABSTRACT FROM AUTHOR]

This paper investigates the aperiodic sampling stabilisation problem for a class of switched linear systems under asynchronous switching. Based on a novel dynamic sampling rule, a sampling-point-based partition-dependent discontinuous Lyapunov functions control approach are developed to make the considered closed-loop switched systems be globally asymptotically stable. Compared with the existing results, the proposed control method permits frequent switching in a sampling interval for subsystems. But the frequent information transmission to the matched controllers and the negative effect of long asynchronous running on system performance can be nicely handled in this paper. Moreover, the partitioned Lyapunov functions are allowed to increase in some segments of the synchronous operating intervals. Tighter synchronous duration and switching number which are determined by more than affine functions make the switching signal more flexible. Finally, simulation analyses of two examples show the advantages of the developed control strategy. [ABSTRACT FROM AUTHOR]

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

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

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

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

Distributed switched large-scale systems are composed by dynamically coupled subsystems, in which interactions among subsystems vary over time according a switching signal. This paper presents a distributed robust switched model-based predictive control (DSwMPC) to control such systems. The proposed method guarantees stabilising the origin of the whole closed-loop system and ensures the constraints satisfaction in the presence of an unknown switching signal. In the distributed model-based predictive control (DMPC) used in this work, by considering the interactions among subsystems as an additive disturbance, the effect of the switch is reflected on the dynamic equation, local, and consistency constraint sets of the nominal subsystems. To compensate the effect of switching signal which creates a time-varying network topology, a robust tube-based switched model-based predictive control (RSwMPC) with switch–robust control invariant set as the target set robust to unknown mode switching is used as local controller. The scheme performance is assessed using three typical examples. The simulation results show that the input and state constraints are satisfied by the proposed DSwMPC at all times. They also validate that the closed-loop system converges to the origin. Also, a comparison of the DSwMPC with a centralised SwMPC (CSwMPC) and a decentralised SwMPC (DeSwMPC) are performed. [ABSTRACT FROM AUTHOR]

This paper addresses the $ H_{\infty } $ H ∞ static output feedback control of discrete-time semi-Markov jump systems (S-MJSs) with mismatched modes in finite-time horizon. Different from the existing relevant work, transient dynamics in short-time is focused instead of the Lyapunov stability over infinite-time horizon. Considering the immeasurable system states, output control strategy is developed subject to mismatched modes. Firstly, S-MJSs in closed-loop form are established by designing a hidden mode-dependent output feedback controller. Based on the closed-loop system (CLS), new Lyapunov function relies on both system mode and elapsed-time are created, according to which fresh analysis criteria ensuring FTB and $ H_{\infty } $ H ∞ FTB are derived. To circumvent the non-convex difficulty arising in controller design, a separation technique is introduced such that the design prerequisites are obtained from the above performance analysis conditions. Finally, the boost converter circuit and a numerical example are borrowed to test the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper proposes a novel control structure to solve the infinite-time linear quadratic tracking (LQT) problem. The major challenge in the LQT problem is the boundedness issue of the cost function in an infinite time framework. In many studies, a discount factor is utilised to overcome the challenge. However, it can affect the stability of the closed-loop system and the steady-state error. This paper proposes an optimal control structure that guarantees zero steady-state error with bounded cost function without utilising the discount factor. The optimal gains of the proposed control structure are computed via model-based and model-free reinforcement learning (RL) algorithms. As a novelty in model-based RL algorithms, a model predictive RL algorithm is proposed to reduce the number of iterations in the learning phase. A model-free reinforcement learning algorithm is utilised to obtain optimal control for tracking the reference online and without any knowledge of system dynamics. Finally, the simulation results verify the advantages of the proposed optimal control structure. [ABSTRACT FROM AUTHOR]

This paper presents a new robust convergence condition of iterative learning control with initial state learning (ILC-ISL) for linear multivariable discrete-time systems in the presence of iteration-varying uncertainty. This method is based on linear matrix inequality (LMI) and provides fixed learning gains during time and iteration. Since the convergence of the ILC algorithm may change due to uncertainty in the parameters of a system, and the ILC algorithm is incapable of dealing with iteration-related challenges, it is a major challenge to reject the effect of iteration varying uncertainty. Besides, in the basic ILC algorithm, the initial state is constant in each iteration and consequently always leads to a tracking error. In this paper, first, a convergence condition of the ILC-ISL algorithm is designed based on closed-loop system stability in the iteration domain, and second, a new robust convergence condition is achieved by the LMI approach. Finally, the effectiveness of the proposed robust convergence scheme is evaluated through a numerical example and a mechanical system, respectively. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive prescribed finite-time asymptotic tracking control problem is considered for the unknown nonlinear switched systems with unknown initial conditions and full-state constraints. A class of nonlinear mappings (NMs) and a new prescribed finite-time performance function (PFTPF) are introduced so that the control design is independent of initial conditions of the controlled states. Based on the neural network approximation approach, NMs, PFTPF and the Barbalat's lemma, an adaptive prescribed finite-time asymptotic tracking controller with full-state constraints is obtained. To avoid overlarge initial control input, the design method with zero initial control input is adopted, the definition of input tuning function (ITF) is expanded and its effectiveness is proved theoretically. As results, the full-state constraints and the boundedness of all the signals in the closed-loop system are guaranteed, and the tracking error of the system can converge to zero asymptotically. Finally, the effectiveness and superiority of the proposed scheme are verified by the simulation results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the $ H_{\infty } $ H ∞ resilient and event-triggered control of singular Markov jump systems against deception attacks. The deception attacks are modelled as a random bounded signal which is governed by a Bernoulli distributed random variable. The event-triggered scheme is adopted to achieve a trade-off between system performance and network resources. Based on the technique of stochastic Lyapunov–Krasovskii functionals and linear matrix inequalities, efficient criteria are developed such that the closed-loop system is stochastically admissible with a certain $ H_{\infty } $ H ∞ performance under deception attacks. Then, the co-design of resilient controller gains and event-triggered rules is provided in terms of a group of feasible LMIs. Finally, two examples are employed to verify the validity of our design. [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 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]

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]

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 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 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]

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]

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]

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, 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]

An adaptive approximated inverse control scheme for a class of high-order nonlinear multi-agent systems with unknown control directions is proposed in this paper. The adaptive control problem under the case it is not required to be identical for the unknown control directions is solved by some novel Nussbaum functions and a monotonously increasing sequence, leading our multiple Nussbaum functions reinforce rather than counteract each other. Also, it is introduced in the design and analysis like some integrable auxiliary signals and a novel contradiction argument. Moreover, hysteresis nonlinearity is counteracted by constructing its approximated inverse compensator. With these efforts, stability analysis ensures that all the closed-loop system signals are uniformly bounded, and it is successfully achieved the asymptotic convergence of tracking error to zero, circumventing the obstacle caused by the unknown control directions. Experiment results illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper addresses the output regulation problem with prescribed performance for nonlinear lower-triangular systems. To achieve the transient prescribed performance, the output transformation technique and barrier Lyapunov function technique are introduced to solve the output regulation problem. The prescribed performance control scheme based on normal backstepping is constructed not only to restrict the output error of the closed-loop system keeping in a prescribed performance bound, but also to achieve that the output tracks a designed trajectory asymptotically. The effectiveness of the proposed method is illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive finite-time impedance control strategy based on optimised backstepping (OB) technique is proposed for robotic manipulators subject to state constraints. The existing OB methods approximately solve the intractable Hamilton-Jacobi-Bellman equation by reinforcement learning (RL) with Bellman residual error, which has intricate actor-critic updating laws and persistence excitation requirement. To overcome this drawback, we construct the simplified RL updating laws by converting the problem to the solution of a positive-definite function, which is composed of actor-critic network weights. Then, the simplified RL updating laws can significantly reduce the controller complexity and relax the persistence excitation. Based on the barrier Lyapunov function, a barrier-type performance index function is constructed for the optimised controller under state constraints. The finite-time stability theory can guarantee the finite-time convergence property of the closed loop system without violating the prescribed constraints. Finally, we demonstrate the effectiveness of the proposed method in the simulation example with environment-robot interaction. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of event-triggered control for a class of nonlinear systems with unknown control coefficients, time-varying delays, full state constraints and external disturbances, simultaneously. Firstly, based on integral Barrier-Lyapunov Functionals, an adaptive event-triggered controller is designed to account for the impacts of complete state constraints, unknown time-varying delays and external disturbances. Using fuzzy logic systems, the unknown functions of systems are then roughly estimated. The system redundancy can be significantly reduced by implementing the event-triggered control technique in the meanwhile. By introducing the separation technique and Lyapunov–Krasovskii functionals, it is shown that the strategy can ensure tracking performance greatly and all the closed-loop signals of the system are bounded. Compared with the existing results, the proposed design scheme is less conservative and has a wilder application range. Finally, the simulation results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper addresses a low-cost adaptive prescribed time tracking control strategy for switched nonlinear systems with input quantisation and unknown backlash-like hysteresis. Initially, a transformation function is introduced into the control design, which allows the tracking error of the controlled systems to a specified accuracy within a preassigned settling time. Unlike the existing prescribed time control scheme that only uses transformation function technique in the first step of the backstepping design, the transformation function is introduced to each step of the controlled systems, which makes only one adaptive parameter that needs to be updated online. Then, to synthesise a low-cost control scheme, a first-order sliding mode differentiator and a single-parameter estimation method are implemented based on the newly constructed coordinate transformations. Meanwhile, the Nussbaum technique successfully solves the effects caused by the unknown control direction, unknown backlash-like hysteresis and quantised input. All signals of the closed-loop systems are bounded under the proposed control scheme with arbitrary switching signal. Finally, the validity of the presented method is confirmed via two simulation examples. [ABSTRACT FROM AUTHOR]

This paper presents an efficient analytical approach for solving the problem of dissipative control in one-sided Lipschitz singular Caputo fractional-order systems subject to nonlinear perturbations. The approach is based on mathematical transformations, and fractional calculus. A state feedback controller is designed to ensure that the closed-loop system is regular, impulse-free, and finite-time bounded with a dissipativity performance index. The results are established using tractable strict linear matrix inequalities (LMIs) without any equality constraint. Two numerical examples are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies the problem of output feedback disturbance attenuation for a class of nonlinear systems with input matching uncertainty, whose nonlinearities are bounded by unmeasured states multiplying unknown input and output polynomial functions. Combining the technique of dynamic gain and extended state observer, an adaptive output feedback controller is designed to guarantee that the states of the closed-loop system are globally bounded, and the disturbance attenuation is achieved in the -gain sense. [ABSTRACT FROM AUTHOR]

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

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

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

In this paper, we study an adaptive method to select online the pole value for a Laguerre scheme in Model Predictive Control (MPC) that yields high performance. It has been observed that, while still using a small numbers of decision variables, the location of the pole affects the closed-loop behaviour significantly. In the present paper, an adaptive algorithm is developed to systematically improve the closed-loop performance of the system as well as the volume of the feasible region and robust feasible region in the case of using a small numbers of decision variables. In order to do this, a method to select a pole value that yields high performance for the initial condition of the system is proposed. The method generates a lookup table of the high-performance pole value obtained through off-line computations. Then, the table is used to assign the pole in the online process. Closed-loop stability for the scheme is established using sub-optimality arguments. Simulations illustrate the suggested method's effectiveness to achieve a balance between performance, optimality, and computational load. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of adaptive control for strict-feedback nonlinear systems with input delay and unknown control directions. The Nussbaum function is utilised to deal with the unknown control directions and a novel compensation system is introduced to handle the time-varying input delay. By using neural network(NN) approximation and backstepping approaches, an adaptive NN controller is designed which can guarantee the semi-global boundedness of all the signals in the closed-loop system. Two simulation examples are also given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates H ∞ event-triggered model predictive control (MPC) problem for switched systems. In network transmission processes, two problems (including limited resources and denial of service (DoS) attacks) profoundly affect the usability of control systems. Especially, DoS attacks that occur in feedback channel can make the systems open-loop. In this regard, a novel memory-based anti-attack strategy is presented, which is implemented by adding a cache function to network controllers. An event-triggering mechanism is adopted to improve network resource utilisation and reduce the probability of transmission data being attacked. MPC technology is employed for network controllers, which optimises the control input and state by minimising an upper bound of a quadratic cost function. Then, closed-loop switched system is established, and the coupling relationship between switching and attacking is clearly discussed. Based on average dwell time approach, H ∞ performance is analysed. The co-design of MPC controllers and event-triggering matrices are given by solving linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate the feasibility. [ABSTRACT FROM AUTHOR]

In addition to stability and accuracy, rapidity is another important index of the performance of control systems. The current fixed time design cannot meet the requirement for rapidity. The design to meet this requirement must be such that the settling time can be given in advance, that is, the so-called predefined-time design. This paper deals with the predefined-time consensus problem for a class of nonholonomic chained-form multiagent dynamic systems with unknown disturbances. First, a distributed observer for each follower is investigated such that the leader state can be estimated by the followers in a predefined time. Based on this observer, a switching consensus tracking controller is proposed to ensure that the tracking errors converge to zero within a predefined time. Compared with the existing finite-time and fixed-time schemes, the upper bound of the settling time is an directly tunable control parameter, and the settling time can be easily tuned by the control parameter. The stability of the closed-loop system is proved by the Lyapunov method. A simulation example of wheeled mobile robots is performed to demonstrate the effectiveness of the proposed controllers. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of the state-feedback control design for a class of interconnected nonlinear full-state time-delay systems with strong interconnection and prescribed performance. Based on the dynamic gain approach and Lyapunov-Krasovskii method, it overcomes the rigorous restriction that the interconnected time-delay functions should satisfy linear growth condition. Compared with the existing results, the main contribution is that the dynamic gain approach is extended to state-feedback control for interconnected time-delay systems, and it is strictly proved that the closed-loop system is globally bounded stable with the proposed decentralised control strategy. The problem of the strong interconnection terms is solved by combining the backstepping approach and graph theory. Furthermore, the expected transient and steady-state performance of the output can be guaranteed by considering prescribed performance control into such strongly interconnected systems. Finally, simulation is performed and the results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with reset control and L 2 -gain stability of piecewise-affine systems with input saturation and external disturbance under the framework of hybrid systems. Firstly, we present a reset controller and establish exponential stability conditions of piecewise-affine systems with input saturation under the reset controller. Secondly, sufficient conditions for L 2 -gain stability of the closed-loop systems with input saturation and external disturbance are provided and a reset controller is designed to enhance the L 2 -gain performance of piecewise-affine systems with input saturation. Furthermore, robustness to inflations of the flow and jump sets is established. Finally, a numerical simulation is provided to illustrate the results. [ABSTRACT FROM AUTHOR]

This paper studies the problem of membership-function-dependent memory output feedback (MOF) controller design for interval type-2 (IT2) fuzzy systems under fading channels. A time-varying stochastic process is introduced to model the unpredictable channel fading phenomenon. Taking the impact of channel fading into consideration, a non-parallel distribution compensation (non-PDC) strategy is employed to construct a class of memory output feedback fuzzy controllers. By fully exploiting the membership functions, the membership-function-dependent performance analysis and controller synthesis results have been obtained, such that the closed-loop system can be guaranteed with mean-square exponential stability and H ∞ performance γ. Finally, simulation studies have been presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Integrating processes, whose one or more poles are located at the origin, are common in the process industry. This paper focuses on maximum sensitivity (Ms)-based control of these types of processes. Integral–proportional derivative (I-PD) controllers are designed by exploiting the direct synthesis method for different forms of integrating processes. The suggested design approach is based on comparing the characteristic equation of the closed-loop system, which comprises the integrating system and I-PD controller with a lead/lag filter, with the desired characteristic equation. Simple and analytical adjusting rules are followed to determine the parameters of the I-PD controller and the lead/lag filter according to desired robustness specified by maximum sensitivity (Ms). The formulas provided contain process transfer function parameters and a tuning parameter that is used for setting the desired Ms. The benefits of the proposed technique are demonstrated by simulation examples and a real-time application of cart position control on an experimental set-up. Comparisons with some reported proportional–integral–derivative (PID) and I-PD design techniques are presented to demonstrate the advantages of the proposed design method more evidently. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered neural critic learning algorithm is investigated to address constrained nonzero-sum game problems with discrete-time nonaffine dynamics. First, in order to ensure the saturation independence of two controllers in the nonzero-sum game problem, we adopt two different boundaries to constrain them respectively. Then, a novel triggering condition is designed to reduce the update times of the controllers, which achieves the purpose of less calculation. It is emphasised that the triggering condition is established based on the iteration of the time-triggered mechanism. Meanwhile, we prove that the real cost function possesses a predetermined upper bound, which realises the cost guarantee of the controlled system. In addition, we prove that the closed-loop system using the developed algorithm is asymptotically stable and that the system state and the sampling state are uniformly ultimately bounded during the process of training neural networks. Finally, two simulation examples are conducted to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of trajectory tracking for a quadcopter subject to parametric uncertainties and disturbances. Using the backstepping technique and ignoring parametric uncertainties and disturbances, a nominal controller is proposed for the quadcopter. In order to ensure that the controller is adaptive to parametric uncertainties (i.e. unknown but constant mass and tensor of inertia) and external disturbances, an exponentially stable estimator is designed based on the Kalman–Bucy filter. The closed-loop system resulting from the interconnection of the plant, the controller, and the estimator is such that the backstepping error is uniformly ultimately bounded. Furthermore, simulation and experimental results are presented, validating the stability and robustness properties of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates robust sliding mode control (SMC) for discrete singular systems which include time-varying delays, parameter uncertainties and nonlinear perturbations. An appropriate discrete sliding surface function is constructed such that the corresponding sliding mode dynamics are gained. By using some free-weighting matrices, a linear matrix inequality constraint is established to make sure that the closed-loop system is regular, causal and stable. Furthermore, in consideration of the improved discrete reaching condition, an SMC law is synthesised for reaching motion and the chattering can be weakened, while few existing papers focus on how to employ it to study the SMC problem for the discrete singular systems with time-varying delays. At last, the designed law is tested through an example. [ABSTRACT FROM PUBLISHER]

This paper investigates the prescribed performance distributed output consensus problem under directed graphs. With the utilisation of a filter, the original system of each follower can be converted into a strict-feedback system. Then, we design a prescribed performance output feedback distributed control protocol by applying the backstepping approach in the converted system. The proposed protocol can guarantee that the consensus tracking error of each agent evolves in predefined decaying bounds to achieve the prescribed performance, that is, the consensus tracking error of each agent converges to a predetermined residual set at a convergence rate no less than a prespecified value and exhibiting a maximum overshoot less than a preassigned constant. Moreover, during the process of consensus, all the signals in the closed-loop system are globally uniformly bounded. A simulation example is given to verify the effectiveness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of observer-based reliable passive control for uncertain Takagi–Sugeno (T–S) fuzzy systems with time-delay and actuator faults. By employing Lyapunov stability theory, fault treatment method and linear matrix inequalities (LMIs), a sufficient condition on the existence of an observer-based reliable passive controller is given. Further, the LMI conditions are proposed for the observer-based reliable passive controller design. The gain of reliable controller and observer can be obtained by solving the proposed LMIs. Using the reliable passive controller, the closed-loop system is not only internally stable but also strictly passive even though some actuator components fail and system state fails to be measured completely. Finally, a truck-trailer system example is given to verify the effectiveness and advantages of the designed approach. [ABSTRACT FROM AUTHOR]