This paper presents a differential evolution (DE)-optimized event-triggered sliding mode control framework for Markovian jump systems subject to nonlinear disturbances. A key innovation is the development of a DE-based optimization strategy for the dynamic event-triggering mechanism, where a novel hyperbolic-type objective function is formulated and optimized to achieve optimal bandwidth utilization. The control scheme integrates an observer-based sliding mode controller with an adaptive compensator designed to counteract unknown disturbances. Finite-time convergence to the sliding surface is guaranteed, while stochastic stability andH∞ performance are established through rigorous Lyapunov analysis. Practical simulations demonstrate that the DE-optimized approach significantly reduces communication frequency while maintaining control performance, outperforming conventional fixed-parameter and static event-triggering implementations. The results highlight DE’s effectiveness in solving complex event-triggered control optimization problems.

This work focuses on adaptive sliding mode control for networked Markov jump systems using a dynamic event-triggered strategy. Unlike previous studies, this mechanism employs a particle swarm optimization algorithm to compute the optimal triggering threshold based on the system’s continuous output values. First, this study designs an event-triggered state observer based on the system conditions, and the error dynamics can be obtained according to the definition of the error. Second, a hyperbolic-type cost function is designed for the event-triggered mechanism, and a dynamic event-triggered scheme is constructed using the particle swarm optimization algorithm. Third, an integral sliding surface is established to obtain the sliding mode dynamics. Following this, a sliding mode controller incorporating adaptive laws is designed, and the reachability is formally demonstrated, and the closed-loop system’s stochastic stability is examined through stochastic Lyapunov function method. Finally, the effectiveness and superiority of the proposed method are verified through RLC circuit.

The information interaction between agents in this paper is described using Markov switching topology, and an adaptive neural-network sliding mode (ANNSM) controller is proposed to address the admissible consensus problem for discrete-time nonlinear singular multi-agent systems (SMASs) with external disturbances. For the reason of approximating the nonlinear part online and overcoming the bounded difficulty, the radial basis function (RBF) based on neural network (NN) is introduced. When designing the state observer, a disturbance observer assisted by an adaptive update law is established for the purpose of estimating the external disturbance and the NN residual error. Next, an ANNSM control method related to the Markov switching topology is proposed based on the designed disturbance observer, and the Lyapunov stability theory demonstrates that the system achieves admissible bounded consensus. Finally, simulation studies on a numerical example and a distributed microgrid model to demonstrate the effectiveness of the proposed controller.

This article addresses the challenge of state observer design for sliding mode security control in Markov jump cyber-physical systems subjected to stochastic injection attacks. To enhance network efficiency, a dynamic event-triggered algorithm is introduced in the communication channel. First, the design begins with a Luenberger state observer featuring an adaptive compensator. This configuration aims to effectively counteract malicious attacks. Second, an integral sliding hyperplane is formulated within the estimation space, which serves as the foundation for deriving the sliding mode dynamics, ensuring robustness against disturbances. Recognizing the diversity of transition rates (TRs), an elastic sliding mode controller is designed to accommodate three distinct types of TRs, which is also strategically designed to guarantee reachability and maintain sliding motion. Third, stochastic stability with an$H_{\infty }$attenuation level is conducted separately for each type of TR. Correspondingly, the development of an algorithm for determining threshold parameters in triggered conditions is presented. Simultaneously, a proof of the nonexistence of Zeno behavior is provided, ensuring the stability and efficiency of the proposed system. Finally, a simulation study using a practical model is included to empirically demonstrate the validity of the proposed method in a real-world context.

This paper introduces an observer-based neural quadratic sliding mode control strategy for interconnected Markov jump systems faced with unknown interconnections, regardless of the high dimensionality of the systems. Firstly, a dynamic event-triggered scheme is constructed in the communication channel to the Lebesgue state observer, with which an integral quadratic sliding mode hyperplane is put forward; Secondly, a neural-based control method is put forward to make sure that predefined sliding hyperplane is attractive; In addition, the occurrence of Zeno phenomenon is also verified to be avoided with the implementation of the controller; Thirdly, linear matrix inequality technique and Lyapunov stochastic stability theory are proposed to check the stochastic stability of closed-loop systems, including sliding mode dynamics and error dynamics; Finally, simulation results on single-link robot arms are given to reveal the validity of the obtained results.

This paper examines the concept of the leader-follower formation for fractional-order multi-agent systems (FO-MASs) by utilizing dynamic event-triggered sliding mode control (SMC) method. Firstly, a dynamic event-triggered mechanism (DETM) is designed relying on the combined measurement vector, in which an auxiliary variable is introduced to dynamically adjust the threshold for each fractional-order agent system. Unlike most existing event-triggered communication mechanisms, whose threshold parameters are always fixed, the threshold parameters in our designed event-triggered conditions can be dynamically adjusted according to the fractional-order dynamic rule. Numerical results show that the proposed DETM can achieve a better system performance in reducing the sampling frequency and the expected formation performance. Secondly, the leader-follower formation control problem is transformed into checking the asymptotic stability problem of the closed-loop system. In addition, due to the memorability of fractional calculus operators, a distinctive condition is established to avoid the occurrence of Zeno behaviors reported in existing dynamic event-triggered schemes. Finally, a simulation example is presented to illustrate the effectiveness and feasibility of the dynamic event-triggered SMC method proposed in this paper.

The paper proposes an adaptive controller design for Markov jump systems with mixed mode transition information through a reduced-order sliding mode approach. The stability criteria and mode-dependent adaptive control law are achieved using linear matrix inequality technique. Firstly, a linear reduced-order sliding surface function is proposed to achieve the reduced-order sliding mode dynamics. Secondly, a feasible approach is presented to check the stochastic stability of resulting sliding motion corresponding to different mode transition information, and to solve the controller gains from stability criteria. Thirdly, an adaptive sliding mode controller is also designed to ensure the finite-time reachability of the predefined hyperplane even when no mode information is available. Finally, the application of the proposed control strategy to the RLC circuit is provided.

This paper is devoted to the issue of observer-based adaptive sliding mode control of distributed delay systems with deterministic switching rules and stochastic jumping process, simultaneously, through a neural network approach. Firstly, relying on the designed Lebesgue observer, a sliding mode hyperplane in the integral form is put forward, on which a desired sliding mode dynamic system is derived. Secondly, in consideration of complexity of real transition rates information, a novel adaptive dynamic controller that fits to universal mode information is designed to ensure the existence of sliding motion in finite-time, especially for the case that the mode information is totally unknown. In addition, an observer-based neural compensator is developed to attenuate the effectiveness of unknown system nonlinearity. Thirdly, an average dwell-time approach is utilized to check the mean-square exponential stability of the obtained sliding mode dynamics, particularly, the proposed criteria conditions are successfully unified with the designed controller in the type of mode information. Finally, a practical example is provided to verify the validity of the proposed method.

This paper deals with the issue of reliable sliding mode control for descriptor systems with semi-Markov parameters using the Takagi-Sugeno fuzzy model, in which an average dwell time approach is utilized to tackle generic uncertain transition rates (TRs). From the analysis of the inner mechanism of switching singular system, a continuous sliding surface function is proposed. Different from continuity with probability one for stochastic systems, the absolute continuity of system solution is a key point in this paper being ensured for application of the average dwell time approach. Then, the mean-square exponential stability of the obtained sliding mode is analyzed based on two types of uncertain TRs. Moreover, a sliding mode controller is constructed to ensure the reachability condition in finite time. Lastly, the method is verified numerically by a single-link robot arm model.

This paper is focused on the event-triggered fuzzy sliding-mode control of networked control systems regulated by semi-Markov process. First, through movement-decomposition method, the networked control system is transformed into two lower-order subsystems. Then, an event-triggered scheme based on a delay system model approach is proposed in designing the switching surface and obtaining the sliding mode dynamics. Furthermore, a fuzzy sliding-mode controller is developed to realize reachability of a predefined switching surface and desirable sliding motion. Moreover, in terms of linear matrix inequality method, sufficient conditions for stochastic stability of the obtained sliding mode dynamics is developed in the sense of generally uncertain transition rates. Finally, the applicability of the proposed results are verified numerically on the single-link robot arm system.

This paper is concerned with finite-time sliding mode control (SMC) of continuous-time semi-Markovian jump systems with immeasurable premise variables via fuzzy approach. First, an integral sliding surface is constructed based on fuzzy observer. Second, an observer-based SMC law is synthesized to guarantee finite-time reachability of the predefined sliding surface before the prescribed time. Third, through finite-time boundedness analysis, the required boundedness performance is conducted at the reaching phase first and then the sliding motion phase, respectively. Furthermore, sufficient conditions in terms of linear matrix inequalities (LMIs) are established to guarantee the required boundedness performance of the overall closed-loop controlled system during the two phases with generally uncertain transition rates (TRs) simultaneously. Finally, a practical example is given to show the validity of the established method numerically.