International Journal of Robust and Nonlinear Control最新文献

Cascade control systems (CCSs) consist of at least two nested control loops. This type of control system is widely used in industrial processes, power plants, and chemical processes. A CCS in which the control loops are closed through a real-time communication network is called a networked cascade control system (NCCS). To reduce the load on communication channels in NCCSs, an event-triggered scheme (ETS) is proposed. In event-triggered control, control tasks are executed upon the occurrence of an event. Instead of executing control tasks after a specific time period, they are triggered by events. Certain environmental factors, including stochastic or deterministic cyber-attacks, cause changes in the dynamics of the closed-loop system. To model stochastic cyber-attacks, Bernoulli or Markov distributions are typically used. Moreover, deterministic cyber-attacks are described by periodic or aperiodic signals. Due to the presence of communication networks in both the inner and outer loops, time delays are inevitable. These delays, as well as packet loss, can lead to reduced system performance or even instability. The goal of this paper is to explore the latest challenges related to NCCSs to familiarize graduate students and researchers in the field of control engineering.

In this paper, we propose a family of discretization schemes that generalizes the Lyapunov-function-based scheme introduced in [49] for asymptotically stable homogeneous systems. We prove that if a convergent method with an order of consistency $��\sigma �$ is used in the auxiliary stages of the Lyapunov-function-based method, then the whole discretization method is convergent and has an order of consistency $��\sigma �$. Additionally, we prove (under some mild conditions) that those discretization schemes produce discrete-time systems that preserve both the properties of a Lyapunov function and the type of convergence (of the trajectories to the origin) associated with the discretized, continuous-time system.

With the vigorous development of the electric vehicle (EV) industry, the demand for charging has surged. However, the relative lag in the construction of charging infrastructure has led to a series of problems for drivers, such as difficulty in finding available charging stations and long waiting times for charging. To address this, this paper proposes an EV charging guidance framework based on an Informer network and Multi-Agent Reinforcement Learning (MARL), aiming at achieving efficient EV charging guidance. First, this paper regards charging stations as independent agents, integrating information from vehicles, charging stations, and traffic, transforming the multi-objective optimization problem of EV charging guidance into a multi-agent reinforcement learning task. Then, an Actor-Critic algorithm combined with the Informer is designed, utilizing the Informer in the Critic network to model the interactions between different charging stations, thereby reducing the complexity of policy learning and enhancing coordination among agents. Subsequently, after calculating the advantage function for the agents, the Actor network is updated to improve learning efficiency. The proposed algorithm was simulated and validated in two different EV charging scenarios. The simulation results show that compared with several state-of-the-art methods, our algorithm achieved the best results in multi-objective optimization, demonstrating its superiority and practicality of our proposed algorithm.

This paper introduces the concept of high-order convex approximation to address uncertainties in a class of nonlinear uncertain mechanical systems. Based on this concept, the homogeneous unit sliding mode control (HUSMC) is designed to handle multiplicative and additive perturbations (uncertainties and disturbances) of the plant. By integrating this design methodology within the framework of linear matrix inequalities (LMIs), we achieve precise adjustments to control parameters, thereby enhancing tracking accuracy and stability. Numerical simulations of an uncertain mechanical system demonstrate the effectiveness of the proposed method in mitigating uncertainties and ensuring the desired control accuracy.

This paper presents an adaptive control framework for dual-system VTOL UAVs capable of operating in both rotary-wing and fixed-wing modes. These aerial vehicles present considerable control challenges due to their nonlinear, time-varying dynamics and inherent instability during flight-mode transitions. The proposed approach addresses these issues by leveraging nonlinear system identification via Adaptive Sparse Identification of Nonlinear Dynamics (ASINDy) with a Lyapunov-based Model Predictive Control (LMPC) scheme. This integrated framework facilitates continuous model updating and guarantees stable trajectory tracking and robust performance. Compared to the GA-PID, the ASINDy–LMPC approach reduced tracking error by approximately 65%, maximum deviation by 67%, average deviation by 79%, and power consumption by 73% in simulation, while nearly halving the control effort. Preliminary hardware trials on a VTOL UAV prototype corroborate these trends, demonstrating consistent improvements during hovering and outdoor flights.

This work focuses on the trajectory tracking control of robot manipulators subject to model uncertainties and unknown additive disturbances. The controller design makes use of a self-adjusting adaptive fuzzy logic-based term, fused with a robust integral of the sign of the error feedback. In the proposed adaptive fuzzy logic framework, means and variances of the membership functions are updated dynamically during each iteration, allowing for a more precise estimation of the parametric uncertainties. The stability of the closed-loop system and the convergence properties of the states are established via Lyapunov-based arguments, where asymptotic stability of the joint tracking error is ensured. Numerical simulations have been conducted to further support the theoretical findings.

This paper mainly studies the finite-time group bipartite consensus problem for multi-agent systems (MASs) with input saturation, coexisting competition, and cooperation. In view of actuator saturation and reducing the frequency of information interaction, a novel event-triggered control protocol by employing the arctangent function is proposed. It is demonstrated that applying this protocol, the finite-time group bipartite consensus and group consensus can be obtained under the structurally balanced and unbalanced topologies, respectively. Furthermore, the Zeno behavior is proved to be excluded. Finally, numerical simulation results demonstrate the effectiveness of our control protocol.

This paper proposes an event-triggered finite-time adaptive nonlinear gradient descent neural network control strategy for a class of nonlinear system with nonlinear unmodeled dynamics. The proposed approach aims to address the challenges brought about by the system's unmodeled dynamics and external disturbances, while ensuring finite-time convergence and robust performance. By utilizing a nonlinear gradient descent-based adaptive neural network weight update law, the learning efficiency of the neural network is enhanced, and its approximation ability is guaranteed. The event-triggered mechanism is introduced to reduce communication and computational overheads, activating control updates only when necessary, thus improving the communication efficiency of the system. The stability of the closed-loop system is proven using Lyapunov theory, and the control law ensures that all closed-loop signals converge within a finite-time interval without Zeno behavior. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed control strategy.

In this paper, the problem of privacy-preserving average consensus (PPAC) is investigated for discrete multi-agent systems (MASs) with hybrid privacy-preserving mechanisms. To this end, a hybrid privacy mechanism consisting of noise injection and quantization-based encryption-decryption rules (EDR) is first constructed to protect the privacy of the initial state of the agent, where the utilization of a dynamic private key enhances the private capability. Then, a PPAC scheme is introduced by resorting to the constructed hybrid mechanism. By means of matrix properties, a sufficient condition is provided to determine the controller gain and the noise parameters, ensuring the performance of average consensus. Additionally, the size of the encrypted data is analyzed in the framework of the hybrid privacy mechanisms. Furthermore, the privacy of the initial state is demonstrated with respect to both honest-but-curious agents and external eavesdroppers. Finally, the effectiveness of the proposed PPAC scheme is illustrated through a numerical example.