Abstract This paper investigates an adaptive composite event‐triggered mechanism‐based‐tracking controller design of a nonlinear system under the comprehensive influence of deferred time‐varying state constraints and unknown nonlinear functions. First, a novel shifting function is constructed to ensure the initial value of state violating the constraint returns to constrained range within a preset time. It transforms the state constraint problem into a new state boundedness problem by combining the state transformation methods. Subsequently, to reduce the number of controller updates and energy consumption, a novel composite switching event‐triggered mechanism is designed. By integrating it and neural networks into backstepping control, an adaptive composite event‐triggered controller is developed. Furthermore, the Lyapunov theory is employed to demonstrate the bounded stability of the closed‐loop system, where all state constraints are satisfied. Ultimately, the effectiveness of the designed control strategy is validated through numerical simulation and practical application simulation.

Abstract In this paper, an event‐triggered adaptive optimized containment control strategy is proposed based on deep neural networks (DNNs) for nonlinear fractional‐order multi‐agent systems. DNNs, with their more complex multi‐layer nested architecture and exceptional function approximation capabilities, are employed to estimate unknown functions. Subsequently, an optimized containment control scheme based on DNNs is presented. Different from the traditional critic‐actor structure, the cost functions are approximated by DNNs, which reduces the control effort. At the same time, the event‐triggered mechanism is used to reduce the consumption of communication resources. The results can ensure that every signal of the system is bounded. Two simulation examples demonstrate the effectiveness of the proposed algorithm.

Abstract This paper investigates the finite‐time stability (FTS) and finite‐time contractive stability (FTCS) of the nonlinear time‐delay system with delayed impulses. By utilizing the Lyapunov–Krasovskii method and impulsive control theory, we derive some sufficient conditions for FTS and FTCS. In addition, we apply the derived theoretical results to chaotic neural networks. Finally, the effectiveness and applicability of the results are demonstrated through an example.

Abstract Differential‐drive wheeled mobile robot (DDWMR) offers excellent maneuverability, making it increasingly valuable to researchers in recent years, but the control poses major challenges due to the nonlinearity nature of the system. This study proposes an integrated scheme consisting of a backstepping controller and an adaptive sliding‐mode controller (ASMC) to address these challenges including the DDWMR being subject to external disturbances and uncertainties. The presented DDWMR design is lightweight, structurally robust, and mechanically simple with a total wight of about 10 kg. The new control scheme can achieve accurate trajectory tracking while ensuring dynamic stability of the DDWMR. Performance comparison of trajectory tracking was made on the approach with that of proportional‐integral‐derivative (PID) controller and sliding mode controller (SMC). Compared to a SMC + backstepping controller, simulation results revealed that performance improvements in trajectory tracking reached root‐mean‐square errors (RMSE) reduction by 3.7% in the X‐axis, and by 23.1% in the Y‐axis, respectively. Finally, experimental results verified that the proposed controller outperformed both traditional controllers by achieving a reduction of RMSE by 12.1% in the X‐axis and by 19.1% in the Y‐axis, respectively in comparison with the SMC + backstepping controller in a real‐world application.

Abstract Vision‐based localization is essential for UAV swarm traversal control in fields such as military reconnaissance and disaster relief. However, it presents challenges including collision risks and the precise localization and tracking of target areas. Therefore, this paper proposes a vision‐based collision‐free formation control method for UAV swarm traversal missions. Initially, LSD (line segment detector) algorithm is employed for target area detection, and a precise localization model is constructed using pinhole projection and triangle similarity principles. Subsequently, to ensure safe formation flight, a repulsive force function model based on Hooke's law is derived, incorporating both the relative position and velocity of the UAVs. Based on this, a collision‐free formation controller is designed within the consensus theory framework to meet formation transformation requirements. Finally, the experiment is conducted with 10 UAVs, including two with cameras. They are divided into two groups, each with five UAVs. In each group, the camera‐equipped UAVs detected and localized the target area, transmitting the center position to the other UAVs. Subsequently, both groups traversed the target area in formation. The results show that the UAV swarm successfully traversed two 2.5 m*2.5 m boxes, and the minimum distance from the boxes was kept at 0.5712 m, and there was no collision between UAVs during formation changes and traversal.

Abstract Robust control invariance (RCI) is crucial for ensuring that the state of the system remains within the predefined set under disturbances. In this paper, we study the robust control invariance issues of probabilistic Boolean control networks using state‐flipped control (SFC). Employing the semi‐tensor product method, we propose a matrix‐based testing condition to determine whether a given set can become a robust SFC invariance set under a specified SFC scheme. Additionally, we derive a necessary and sufficient condition to test the existence of feasible SFC schemes. To optimize the process, we present an algorithm that calculates feasible SFC schemes with the minimal flip set, ensuring the given set becomes a robust SFC invariance set. The idea of the algorithm is to identify all beneficial states for achieving RCI and direct the system towards these states, thereby avoiding the need to explore all possible SFC schemes and significantly reducing time complexity. Compared with the event‐triggered approach, our method reduces the time complexity of designing feasible control schemes from to . Finally, a biological simulation is presented to demonstrate the effectiveness of the theoretical results.

Abstract This study presents a non‐iterative tuning technique for a linear fractional‐order (FO) controller, based on the integral of the time‐weighted absolute error (ITAE) criterion. Minimizing the ITAE is a traditional approach for tuning FO controllers. This technique reduces the over/undershoot and suppresses the steady‐state error. In contrast to conventional approaches of ITAE‐based controller tuning, the proposed approach does not require multiple closed‐loop experiments or model‐based simulations to evaluate the ITAE. The one‐shot input/output data is collected from the controlled plant. A fictitious reference signal is defined on the basis of the collected input and output signal, which enables us to evaluate the closed‐loop response provided by the arbitrary controller parameters. To avoid repeated experiments that are necessary in the conventional approach, we reformulate the ITAE minimization problem using the fictitious reference signal. The desired FO controller parameters minimizing the ITAE are obtained by solving the optimization problem that is based on the fictitious reference signal. The validity of the proposed approach is demonstrated by a numerical study. The avoidance of repeated experiments significantly reduces the development cost of linear FO controllers, thereby facilitating their practical application.

Abstract Addressing financial risk models is essential to maintaining the sustainability and stability of economic setups in a world economy. The advantage of using fractional discrete‐time difference equations to model financial risk management and economic processes is their capacity to detect random fluctuations in dynamic behaviors. This novel research introduces and analyzes the complex dynamics of a discrete‐time financial risk model with fractional orders. The fundamental dynamics of this proposed model are examined, including the bifurcation analysis, chaotic attractor, and Lyapunov exponents. Furthermore, sequence complexity variations when fractional order and parameter of bifurcation risk varies are observed using the approximate entropy (ApEn) and 0‐1 test. The findings highlight the sensitivity of the discrete‐time financial risk model to fractional derivative orders, leading to the emergence of various rich, dynamic patterns such as chaos. Additionally, the suggested fractional‐order discrete‐time model is successfully stabilized and synchronized through the design of an active controller. Finally, MATLAB R2024b simulations was used to validate the research findings.

Abstract In this paper, the optimal regulation performance of multi‐input multi‐output (MIMO) systems with packet dropouts and logarithmic quantization is studied. A model is developed that retains the characteristics of the networked control system, a model is established incorporating packet dropouts, logarithmic quantization, encoding‐decoding, bandwidth and Gaussian white noise. The optimal regulation performance is derived using factorization and partial factorization. The results indicate that optimal regulation performance is significantly influenced by fundamental system properties (unstable poles, non‐minimum phase zeros, and their directions) in addition to network constraints (packet dropouts, logarithmic quantization, encoding‐decoding, bandwidth, and noise). Finally, we conduct a numerical simulation to validate our findings.The results indicate that optimal regulation performance is significantly influenced not only by fundamental system properties (unstable poles, non‐minimum phase zeros, and their directions) but also by network constraints (packet dropouts, logarithmic quantization, encoding‐decoding, bandwidth, and noise). Finally, we conduct a numerical simulation to validate our findings.

Abstract This paper investigates the fully distributed secure consensus for multi‐agent systems under rotation‐scale attacks, which combine both the rotational and scaling effects on system dynamics. Rotation‐scale attacks provide a new form of DoS attack, which can bypass traditional DoS detection mechanisms and cause control signal saturation. To enhance communication efficiency, a fully distributed event‐triggered control protocol with a double‐damping adaptive mechanism is proposed, which guarantees control signal unsaturation. Rigorous theoretical analysis proves the system's security under this approach. The effectiveness of the strategy is validated through a simulation with 20 single‐link robotic manipulators.