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This article presents a composite anti-disturbance security control approach for the continuous-time nonlinear hidden Markov jump systems, in which the nonlinearities are characterized by the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model. To offset and suppress the influence of multiple disturbances on the system stability, a composite control method based on disturbance observer and ${\mathcal {H}}_{\infty }$ control is established. In addition, considering potential cyber-attacks, this article takes deception attacks as an example, assuming that the attack signal is generated by a nonlinear bounded function, and the Bernoulli distribution is employed to depict whether the attack occurs or not. Then, in accordance with the IT2 T-S fuzzy model, the final composite system is derived. With the help of tools, such as the Lyapunov stability theory and fuzzy theory, the stability of the target system is analysed, and the specific forms of the fuzzy composite controller and disturbance observer are obtained. Finally, the correctness and effectiveness of the control method proposed in this article are verified through two examples.

Unlike the traditional studies on multiagent systems (MASs), this article investigates a class of MASs with spatial properties, whose agents are modeled by partial differential equations (PDE), in addition, to make the model more comprehensive, the Markovian switching topology with partially unknown probability is taken into account. The purpose of this article is to achieve the bipartite consensus for the above PDE-based MASs. In fact, the consideration of spatial factors in MASs will exacerbate the network transmission pressure, to overcome this problem, a novel spatiotemporal event-triggered mechanism is developed. Compared with the existing event-triggered mechanism, the proposed one is not only time-dependent but also covers the influence of spatial variables on the trigger conditions, which helps to further reduce the triggering frequency. Finally, three examples are given to verify the validity of the conclusion we proposed.

A new dynamic-scaling-based global adaptive quantized control approach is proposed for nonlinear systems with input and state quantization, which reduces some conservatism of relevant results, including matched nonlinearities, global Lipschitz continuity, and quantization error satisfying constant bound. A unique idea is used to deal with the quantized-state feedback problem, that is, the coefficient deviation problem in the decomposition of sector-bounded quantizer is converted to that of solving novel coupled matrix inequalities. Also, a nonidentification adaptive gain is introduced to compensate the mismatched nonlinearities and uncertainties. For a class of bounded differentiable reference signals, tracking error can be regulated to any small neighborhood of the origin as long as quantization dead-zone size is set small enough.

This article considers the nonfragile observer-based guaranteed cost control (GCC) problem of nonlinear networked control systems (NCSs), which are represented by the Takagi–Sugeno (T–S) fuzzy model. The studied T–S fuzzy NCSs are subjected to multipath quantizations and packet dropouts which mean that the coexistence of quantizations and packet dropouts not only in the control inputs, but also in the measurable outputs. The dynamic quantization strategy is adopted to quantize the multipath signals. The multipath packet dropouts are modeled as two mutually independent Bernoulli distribution random sequences. The observer-based controller design of T–S fuzzy NCSs is fulfilled through a novel observer design strategy. New results are derived to ensure that the closed-loop T–S fuzzy NCSs are stochastically stable with a certain GC function. By a two-step approach, results based on linear matrix inequalities are deduced to determine the observer parameters and controller gains. Eventually, a practicle example is given.

This article studies the problem of adaptive fuzzy tracking for a class of nonstrict feedback stochastic systems with input delay and asymmetric state constraints. Input delay is addressed based on the Pade approximation and introducing an intermediate variable, then the control problem for the original systems is transformed into one for nondelay system. For state constraints in the system, this article proposes nonlinear state dependent function instead of barrier Lyapunov function, which removes the feasibility conditions. By fuzzy logic system and variable separation technology, this article effectively solves algebraic rings created by nonstrict feedback structures. A fuzzy adaptive controller is proposed to ensure that all variables in the system are bounded in probability and the asymmetric state constraints are well kept all the time. Finally, simulation examples confirm the effectiveness of control strategy.

This article provides a secure distributed output feedback model predictive control (DOFMPC) solution for the leader-following consensus problems of homogeneous linear disturbed multi-agent systems against multiple cyber attacks. The false data injection (FDI) attacks on the sensor-controller communication channel and denial-of-service (DoS) attacks on the controller-actuator communication channel occur simultaneously. To defend against dual-channel multiple attacks, we propose a secure DOFMPC scheme consisting of three modules. First, a robust multivariate observer is built to separate FDI attacks from uncompromised states. Second, a distributed output feedback model predictive controller is designed to generate effective control sequences. Third, a buffered actuator is added to realize adequate compensation to defend against DoS attacks. The proposed secure DOFMPC scheme ensures the recursive feasibility of the formulated OCP and achieves the leader-following consensus of the multiagent system. Finally, an illustrative example is presented to demonstrate the secure performance of the proposed DOFMPC scheme.

In this paper, the asynchronous state estimation problem of two-time-scale Markov jump complex networks under analog fading channels is investigated, in which the mode of the designed state estimator is asynchronous to the system mode and the asynchronous probabilities are partially known. The two-time-scale phenomenon of complex networks is modeled by a singular perturbation parameter and the changes in connection mode between the complex networks are subject to a Markov chain. The output measurements are transmitted via analog fading channels and fading gains are used to represent the magnitude of the decline of the transmitted information. The purpose of this study is to design an asynchronous state estimator related to the asynchronous mode of the network topology such that the stochastic stability with an $H_{\infty }$ performance index of the resultant error dynamics can be guaranteed. By designing the Lyapunov function which is associated with the singular perturbation parameter and the system mode, the stochastic stability condition of the estimation error system is derived. A new decoupling method to obtain the state estimator gains is proposed, which removes the limitation on the relaxation matrix of previous research results. Finally, the effectiveness of the methods are verified by a numerical simulation example and a capacitor-resistance circuit model.

In this article, a robust optimal output regulation framework for nonlinear systems with unknown parameters is proposed, in which a suboptimal feedforward-feedback controller is designed. Specifically, a novel internal model principle-based feedforward controller is designed to cope with the adverse effects of unknown parameters. Considering the system performance and control cost, an optimal-feedback controller is then designed via the adaptive dynamic programming method. It is proved that under the proposed suboptimal controller, all the signals of the closed-loop systems remain bounded and the tracking error is arbitrarily small. Furthermore, the predefined performance index is minimized. Finally, two simulation examples are given to verify the effectiveness of the proposed framework.