This article investigates the decentralized control of semilinear parabolic systems under communication constraints. A co-design framework is proposed that integrates a spatially decentralized dynamic event-triggered mechanism with a full-link quantization strategy to alleviate network congestion. The core of this work lies in a unified framework that systematically addresses the coupled challenges of aperiodic, state-dependent transmissions and quantization effects across the entire sensor-controller-actuator chain. This is achieved through a novel Lyapunov-Krasovskii formulation and LMI-based synthesis, which explicitly accounts for multiplicative quantization errors from both channels. The proposed spatially decentralized triggering mechanism further enhances scalability by enabling independent local decision-making. Simulation results on a chemical reactor process demonstrate that the proposed scheme ensures system stability while dramatically reducing communication traffic, outperforming both time-triggered and centralized event-triggered counterparts.

This paper investigates the optimal output synchronization problem for a class of discrete-time heterogeneous complex dynamical networks. A novel control methodology is proposed for networks with partially unknown system dynamics. First, the distributed observers are designed for each node to reconstruct the leader’s state. Then, within the output regulation framework, a distributed dynamic compensation controller is developed for each node to achieve asymptotic output synchronization. A hybrid iterative learning algorithm is proposed to solve the optimal feedback control gains and dynamic compensation terms. The advantage of this approach lies in its independence from precise knowledge of the system matrices and its elimination of the need for an initially stabilizing controller. Finally, numerical simulations demonstrate the effectiveness and superiority of the proposed controller in achieving the asymptotical synchronization and control performance.

This work investigates the secure data-driven iterative learning control (ILC) problem for a kind of nonlinear discrete-time nonaffine multiagent systems under channel fading (CF) phenomenon and deception attack (DA). The stochastic fading behavior in the output channel is established as an independent Gaussian distribution model, the DA initiated by malicious attackers in the network damages the security of original data of each agent by injecting false data information. Relying solely on the incomplete output/intput data of every agent, the system model could be transformed into an equivalent data-driven form with adjacent-agent dynamic linearization (ADL) technology. And then the data-driven ILC algorithm gained through optimizing the two performance index functions makes the tracking error converges to a small neighborhood of zero in the sense of mathematical expectation. Finally, after rigorous theoretical analysis, the experiment confirms the practicability of the proposed algorithm.

This investigation introduces a sophisticated robust fuzzy distributed protocol, which synergistically merges the strengths of robust control and fuzzy control to confront the global consensus conundrum in unknown multi-agent systems. The technological ingenuity of this protocol lies in its integration of a seamless switching function, which ensures the robust and effective functionality of the fuzzy protocol across a broad global spectrum. Furthermore, the study delves into the global consensus dilemma in both first-order and second-order stochastic unknown multi-agent systems, outlining the specific design framework for robust fuzzy controllers. To uphold the stability of the closed-loop systems, the investigation innovatively formulates a novel type of Lyapunov function, inspired by the tenets of Lyapunov quadratic form design. Conclusively, through a series of simulation experiments, the investigation substantiates the practical effectiveness of the proposed algorithms. Note to Practitioners—Practitioners in automation, robotics, and distributed decision-making face a significant challenge in achieving global consensus in multi-agent systems amidst uncertainties and disturbances. This research introduces a sophisticated robust fuzzy distributed protocol that integrates robust control and fuzzy control, leveraging a switching function to ensure effectiveness across various scenarios. The study provides a detailed design framework for robust fuzzy controllers in first- and second-order stochastic unknown multi-agent systems, crucial for developing resilient strategies to maintain system stability and performance. Innovatively, a novel Lyapunov function, inspired by Lyapunov quadratic form design, upholds closed-loop system stability, offering a theoretical foundation for control strategies. Simulation experiments confirm the protocol’s practical effectiveness, achieving high-efficiency and reliable global consensus in unknown MASs. Preliminary results suggest promising practical implementation, benefiting practitioners across various fields.

For a class of multi-machine power systems (MMPSs) with unknown dynamics and actuator faults, two model-free optimal fault-tolerant (FT) control frameworks based on policy iteration (PI) are proposed. First, the actuator failure is modeled within the interconnected MMPSs. Building on this foundation, a stable FT-PI framework is developed that eliminates the conventional dependence on initial admissible controllers through a proposed two-stage implementation. Additionally, an enhanced Q-learning equivalent framework is introduced, which further relaxes the conditions on the initial stabilizing policy typically required by traditional integral Q-learning methods. Both of these data-driven algorithms dynamically solve the FT continuous algebraic Riccati equation (FT-CARE) without requiring knowledge of the system dynamics and actuator fault information. Meanwhile, the convergence and stability of the algorithms are rigorously proved. Finally, the effectiveness of the proposed algorithms is verified through a three-machine power system.

This article presents a novel distributed robust fuzzy control scheme to address the global consensus problem of unknown nonlinear multiagent systems (MASs). By replacing the nonlinear dynamic model constrained by the global Lipschitz condition with a more general system model, the proposed approach enhances applicability. A robust fuzzy control scheme based on a smooth switching function is introduced, effectively resolving the global consensus problem for unknown nonlinear systems. Furthermore, time-varying σ-modification terms are incorporated into the adaptive parameter design, replacing constant terms to avoid asymptotically uniform ultimate boundedness and ensuring global asymptotic consensus of the closed-loop systems. The efficacy of the proposed scheme is demonstrated through simulation results.

This article studies the human-in-the-loop fuzzy iterative learning control of leader-following consensus for unknown mixed-order nonlinear multi-agent systems. The human operator participates in the cooperative control of multi-agent systems, which indirectly affects the followers by directly controlling the leader. Moreover, the leader's input is unknown to all followers. The mixed-order multi-agent systems contain both first- and second-order agents, which include the special case of the second-order multi-agent systems. By using fuzzy logic systems to approximate unknown nonlinear dynamics, a fully distributed fuzzy iterative learning controller with time-varying coupling gain is designed. In the estimation parameters, a$\sigma$-modification related to the number of iterations is designed to ensure the convergence of the closed-loop systems. Based on the new composite energy function, the exact consensus of the closed-loop systems is proved. Finally, the simulation results verify the effectiveness of the designed control algorithm.

In this article, the fuzzy dynamic event-triggered tracking control problem of semilinear parabolic systems (SLPSs) with time-varying delay is investigated. First, T-S fuzzy partial differential equation models are introduced to describe the SLPSs. Second, a less conservative and more general fuzzy dynamic event-triggered strategy (DETS) is proposed to reduce communication consumption and avoid unnecessary continuous signal monitoring. Since the dynamic threshold is closely related to the currently sampled signal and the latest successfully transmitted signal, it can be promptly dynamically adjusted. In addition, on the basis of a reasonable assumption, a novel linear matrix inequality (LMI) relax technique is introduced to deal with the mismatched premise variables between the fuzzy systems and the fuzzy controller. By constructing the appropriate Lyapunov–Krasovskii candidate functional, the criterion that the SLPSs can asymptotically track the target systems is derived, and the desired dynamic event-triggered control gains can be obtained by solving a set of LMIs. The DETS reduces effectively communication resource consumption. Finally, the control problem of temperature distribution of catalytic rod in practical engineering application is given to verify the effectiveness and superiority of the proposed control scheme.

This article investigates the consensus problem of mixed-order nonlinear multiagent systems (MASs). First, a new research framework of consensus control for MASs with hybrid-order dynamics is established. In this framework, the order of low-order dynamic subsystems is increased to higher-order dynamic subsystems by means of increasing order technology, so that the mixed-order MASs can be changed into the same-order MASs. Thus, the distributed controller of hybrid-order MASs can be designed by using the consensus control method of the same-order MASs. Second, through a case study of a stochastic mixed first- and second-order nonlinear MASs, this article further expounds the design idea of the framework structure and gives the concrete design form of the distributed controller and the stability analysis of the closed-loop system. Finally, simulations are given to verify the effectiveness of the distributed control protocol in this case.

This paper aims to investigate the problem of robust nonfragile guaranteed cost control for uncertain discrete-time Takagi–Sugeno fuzzy systems with Markov jumping parameters and time-varying delay. A nonfragile fuzzy-basis-dependent and mode-dependent controller is designed and a sufficient condition is developed to ensure that the resulting closed-loop system is robust asymptotically stable in mean square with guaranteed cost index not exceeding the specified upper bound. Subsequently, the controller gain and minimum upper bound of the guaranteed cost index can be obtained by solving a set of linear matrix inequalities. Compared with the existing literature, this paper enormously reduces the conservatism of the result obtained. Finally, numerical and practical examples are provided to demonstrate the performance of the proposed approach.

This paper investigates the global consensus problems for the first-order and second-order unknown nonlinear multiagent systems (MASs) with uncertain input disturbance. Fuzzy logic systems are applied to solve the global consensus problem for unknown nonlinear MASs. A fully distributed adaptive fuzzy control is designed to enable followers asymptotically to track the leader without using any dynamics of the leader. The global consensus conditions are also derived for the first-order and second-order unknown MASs, which overcomes the drawback of the semiglobal consensus in existing literature. It is worth mentioning that the proposed approach can greatly alleviate the computation burden because it only needs to update a few parameters. An efficient framework is also given to achieve the global formation control of the second-order unknown nonlinear MAS with an undirected connected graph. Finally, four simulated examples are given to illustrate the effectiveness of the proposed control protocols.

In this paper, the problem of stabilization with optimal L1-gain for positive T-S fuzzy systems is investigated with the use of linear Lyapunov function. A T-S fuzzy model for positive nonlinear system is established to study the stabilization control for the positive system. Sufficient condition for stabilization is presented in term of linear programming. The static output-feedback fuzzy controller is constructed to guarantee that the closed-loop system is controlled positive, asymptotically stable and the L1-gains from the exogenous inputs to the regulated output is minimized, respectively. Moreover, the stabilization problem with optimal L∞-gain for positive T-S fuzzy systems is solved. Finally, three examples are presented to show the effectiveness of the theoretical results.

In this paper, for the stochastic nonlinear systems the adaptive fuzzy tracking controllers are constructed by using the fuzzy logic systems (FLS) and the classical quadratic functions. Compared with the existing results for adaptive fuzzy control, the stochastic nonlinear systems investigated in this paper are much more complex since the systems not only have distributed state time-varying delays in the noise jamming intensity terms but also have the time-varying delays in the input signals. During the controller design procedure, through appropriate assumptions and a state transformation the system with time-varying input delay can be easily transformed into a system without input delay. The other main advantage is that quadratic functions are used as Lyapunov functions to analyze the stability of systems, other than the fourth moment approach proposed by H. Deng and M. Krstic, and the hyperbolic tangent functions are introduced to deal with the Hessian terms. The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error can converge to a small residual set around the origin in the mean square sense.

This paper is concerned with the problem of observer-based fuzzy control design for discrete-time T-S fuzzy bilinear systems. Based on the piecewise quadratic Lyapunov function (PQLF), the piecewise fuzzy observer-based controllers are designed for T-S fuzzy bilinear systems. It is shown that the stability for discrete T-S fuzzy bilinear system can be established if there exists a PQLF can be constructed and the fuzzy observer-based controller can be obtained by solving a set of nonlinear minimization problem involving linear matrix inequalities(LMIs) constraints. An iterative algorithm making use of sequential linear programming matrix method (SLPMM) to derive a single-step LMI condition for fuzzy observer-based control design. Finally, an illustrative example is provided to demonstrate the effectiveness of the results proposed in this paper.

An adaptive fuzzy control scheme with only one adjusted parameter is developed for a class of nonlinear time-varying delays systems. Three kinds of uncertainties: time-varying delays, control directions, and nonlinear functions are all assumed to be completely unknown, which is different from the previous work. During the controller design procedure, appropriate Lyapunov-Krasovskii functionals are used to compensate the unknown time-varying delays terms and the Nussbaum-type function is used to detect the unknown control direction. It is proved that the proposed controller guarantees that all the signals in the closed-loop system are bounded and the tracking errors converge to a small neighborhood around zero. The two main advantages of the developed scheme are that (i) by combining the appropriate Lyapunov-Krasovskii functionals with the Nussbaum-gain technique, the control scheme is proposed for a class of nonlinear time-varying delays systems with unknown control directions, (ii) only one parameter needs to be adjusted online in controller design procedure, which reduces the computational burden greatly. Finally, two examples are used to show the effectiveness of the proposed approach.

For original paper see D. W. C. Ho et al., ibid., vol.16, no.3, p.625-35, (2005). For original paper see S. J. Yoo et al., ibid., vol.19, no.8, p.1496-8, (2008). This paper presents the reply to "Comments on ldquoAdaptive neural control for a class of nonlinearly parametric time-delay systemsrdquordquo.

An adaptive backstepping neural-network control approach is extended to a class of large-scale nonlinear output-feedback systems with completely unknown and mismatched interconnections. The novel contribution is to remove the common assumptions on interconnections such as matching condition, bounded by upper bounding functions. Differentiation of the interconnected signals in backstepping design is avoided by replacing the interconnected signals in neural inputs with the reference signals. Furthermore, two kinds of unknown modeling errors are handled by the adaptive technique. All the closed-loop signals are guaranteed to be semiglobally uniformly ultimately bounded, and the tracking errors are proved to converge to a small residual set around the origin. The simulation results illustrate the effectiveness of the control approach proposed in this correspondence.

In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.