Abstract This paper investigates the integral sliding‐mode‐based almost output regulation (SMBAOR) issue of switched T‐S fuzzy systems (STSFSs) with matched uncertainty. First, a switched fuzzy integral sliding mode (SM) surface is introduced. Based on the controller derived from the sliding mode surface and the multiple Lyapunov functions method, a solvability condition constrained by average dwell‐time (ADT) relevant switching index is established to achieve OR characteristic and performance. Then, the form of the SM control law that can guarantee the finite time reachability of the switched fuzzy integral SM surface is given. Further, the efficacy of the control tactic is verified by adjusting the total temperature increment of the low‐pressure turbine outlet of the aero engine model.

Abstract This article investigates the problem of quantized state feedback tracking control for discrete‐time nonlinear systems subject to privacy protection and hybrid attacks. To capture the inherent nonlinear dynamics of the plant and improve modeling accuracy, an interval type‐2 (IT2) Takagi‐Sugeno (T‐S) fuzzy model is adopted. To mitigate the communication burden in networked transmission, three uniform dynamic quantizers are incorporated into the communication links to reduce the number of transmitted packets. The primary objective of this study is to design an IT2 fuzzy state feedback tracking controller that guarantees both the mean‐square asymptotic stability and tracking performance of the closed‐loop system, even in the presence of privacy protection constraints and hybrid cyber‐attacks. Moreover, all design parameters can be obtained by solving a set of linear matrix inequalities. Finally, two simulation examples are presented to verify the effectiveness of the proposed method.

Abstract This paper investigates a sliding mode control strategy under the event‐triggered mechanism for a class of stochastic Markov switching systems subject to Lévy noise and time‐varying delays. Firstly, a sliding mode surface function is designed with an integral term. Then both the generalized Itô formula and the Gronwall inequality are used to verify the exponential stability of the system on this sliding mode surface in the mean‐square sense. Secondly, an event‐triggered sliding mode control strategy is designed to guarantee the sliding mode surface's reachability. Thirdly, the positive lower bound on the triggering interval is also given to exclude Zeno behavior. In the end, the validity of the theoretical derivation is confirmed through a numerical simulation.

Abstract This paper studies the state estimation problem for nonlinear systems with missing measurements and filter‐and‐forward relays. By introducing stochastic variables obeying the Bernoulli distribution, the phenomenon of randomly occurring missing measurements is taken into consideration. The filter‐and‐forward relay is implemented in the channel from the sensor to the remote estimator, enhancing the quality of signal transmission. A matrix‐inequality‐based approach is proposed to design the filter in the filter‐and‐forward relay under which the filtering error in the relay is ensured to be ultimately bounded. Subsequently, a recursive estimator is built by employing the extended Kalman filter method. An upper bound on the estimation error covariance is derived by solving Riccati‐like difference equations and is then minimized by choosing an appropriate estimator gain. Finally, the validity of the estimation method is verified through two examples.

Abstract While learning‐based controllers demonstrate impressive performance, a significant challenge remains the lack of safety guarantees. Existing safety approaches often involve separating the optimization of the objective and constraint certification, which introduces a Model Predictive Safety Certification (MPSC) scheme. Unfortunately, this isolation frequently compromises performance, even though it ensures constraint satisfaction. This paper introduces a novel Multi‐Step MPSC scheme that combines Predictive Functional Control to track the reference output generated by the learning‐based controller, along with the utilization of a robust optimizer. The goal of this innovation is to introduce a method that confirms the system's safety while enhancing the performance of the MPSC. A numerical case study is provided and compared with the studies conducted by other researchers. The simulation results validate the efficiency and robustness of the proposed approach.

ABSTRACT This paper presents a new dynamic motion control method using a fast nonsingular finite time control approach based on an adaptive disturbance observer to solve the trajectory tracking problem of nonholonomic autonomous mobile robots (AMR) with uncertain disturbances. The proposed method combines fast nonsingular terminal sliding mode control (FNTSMC) with an adaptive sliding mode observer (ASMO), referred to as FNTSMC+ASMO. The ASMO estimates disturbances in finite time, and its output is used by FNTSMC to enhance system robustness and ensure fixed‐time convergence of tracking errors under practically bounded initial conditions via Lyapunov stability theory. Additionally, an artificial potential field (APF) technique is incorporated to achieve effective obstacle avoidance, further improving its practical applicability. The validity and superiority of the proposed method are well exemplified through extensive simulations and experiments, demonstrating superior tracking accuracy and excellent disturbance rejection compared to other sliding‐mode control methods.

Abstract This paper revisits the group controllability of two‐time scales multi‐agent systems, aiming to achieve innovation from the perspective of heterogeneity and switching topology. To accomplish this objective, the singular perturbation system theory and boundary layer theory are introduced to model the systems. Then, Wonham's Geometric Methods alongside concepts, such as invariant subspaces and controllable state sets, are employed to establish criteria for group controllability. Furthermore, we propose a method to construct switching sequences aimed at ensuring group controllability. Numerical simulations validate the effectiveness of the derived conclusions.

Abstract In this paper, a flexible distributed fencing controller based on Euler–Lagrange dynamics is proposed, which could cooperatively fence a moving target with unknown but constant velocity within the convex hull formed by the agents while ensuring collision avoidance and stable formation. It is worth noting that we consider the nonlinear characteristics of complex systems and adopt Euler–Lagrangian dynamics instead of linear systems for modeling, while no label is predetermined for the agent. To enforce velocity matching between the agents and the target, a flexible distributed fencing control strategy accounting for unknown dynamic parameters is designed using only the relative position information of the target and the agents. The effectiveness of the controller is verified by rigorous theoretical analysis and extensive numerical simulations.

Abstract This paper addresses the trajectory tracking problem of the 2‐DOF helicopter system by proposing an adaptive finite‐time control method based on a composite learning and switching mechanism. Unlike traditional learning control approaches, the composite learning method in this paper improves learning efficiency by introducing a prediction error term within the neural domain. Outside the neural domain, the switching mechanism ensures rapid convergence of the system state. Additionally, a barrier Lyapunov function (BLF) is employed to impose state constraints, ensuring that the system state remains within a safe range during the learning process. Through Lyapunov stability analysis, the finite‐time convergence and uniform ultimate boundedness of the system are proven. Finally, simulations and experiments validate that the proposed method effectively overcomes the challenges posed by model uncertainties and external disturbances.

Abstract This paper proposes an incentive Stackelberg strategy for the mean‐field type Itô stochastic systems with Poisson jumps in finite horizon and infinite horizon, respectively. Unlike the regular Stackelberg strategy, the obtained incentive Stackelberg strategy allows the leader to achieve his desired goal, that is, attaining his global optimum. Also, the mean‐square stability of the system is investigated, and it can be achieved by the follower. In addition, an algorithm procedure is introduced to derive the incentive feedback Stackelberg strategy in an infinite horizon. Finally, two examples are provided to highlight the effectiveness of the proposed results.