International Journal of Robust and Nonlinear Control最新文献

This paper solves the problem of predefined-time stabilization for low-order nonlinear systems with time-varying orders and the asymmetric output constraint. We construct a tangent-type barrier Lyapunov function to handle the output constraint. By using the presented barrier Lyapunov function and the adding a power integrator technique, a novel continuous state-feedback controller is designed to guarantee that the solution of the closed-loop system is predefined-time stable while the output constraint is not transgressed.

In this paper, we study the exponential tracking and disturbance rejection problem of a class of Euler–Lagrange (EL) systems with high-order actuator dynamics. This type of EL system takes into account not only the dynamics of the rigid components of the plant but also the dynamics of the actuators and it includes the elastic joint robot manipulator as a special case. Assuming the reference signal is bounded with bounded derivatives and both the output and the input of the actuator are subject to multi-tone sinusoidal disturbances, we first establish nonlinear observers for the unknown disturbances based on the internal model principle. Then, we further construct a control law through a backstepping-like design procedure to achieve the exponential tracking and disturbance rejection for the class of EL systems with high-order actuator dynamics. A numerical example is provided to illustrate the proposed approach.

This paper introduces an innovative adaptive fixed-time sliding mode control (SMC) method that eliminates chattering, enhancing trajectory tracking accuracy and robustness in nonlinear dynamical systems facing uncertainties and external disturbances. The proposed control scheme ensures fixed-time convergence of system states to a predefined neighborhood around the origin, independent of initial conditions. An innovative adaptive tuning law is proposed to estimate the unknown upper bounds of synthetic uncertainties and disturbances without requiring prior knowledge. This law forces system states to trend to the sliding surface within a fixed time, achieving stabilization of tracking errors at the origin without undesirable chattering and while avoiding singularities. Comprehensive simulations and comparisons among four control strategies (C1, C2, C3, and C4) demonstrate that the proposed C1 control strategy outperforms others in terms of faster convergence speed, higher tracking accuracy, less chattering, and stronger robustness.

In this paper, fault estimation (FE) and fault-tolerant control (FTC) are studied for switched stochastic nonlinear systems with unknown input and faults. The dynamic unknown input observer (DUIO) and disturbance observer are designed, and the system state, faults, and disturbances can be reconstructed. The DUIO designed in this paper is the extension of the traditional unknown input observer (UIO) and the proportional-integral observer. The unknown input, which contains unmodeled disturbance, uncertainty, can be decoupled by the estimation error system. A dynamic output feedback controller based on observers is designed to compensate for actuator fault and modelable disturbance. The integration scheme of FE and FTC is designed, and the one-step linear matrix inequalities (LMIs) conditions are constructed, which avoids the coupling of observer and controller parameters. The root-mean-square (RMS) gain is used to attenuate the unknown input and unmodelable disturbances in the closed-loop systems, which can remove some common assumptions. Finally, the feasibility of the proposed method is demonstrated through simulations.

This paper presents a decentralized robust dynamic event-sampled tracking (EST) control law for interconnected nonlinear-constrained systems. The core of developing such a control law is to convert the original EST control problem into the event-sampled decentralized stabilization problem of augmented interconnected systems. To address the transformed decentralized stabilization problem, an indirect approach relying on the optimal control methodology is proposed. Initially, a group of cost functions are constructed for the nominal subsystems related to the augmented interconnected systems. Then, the dynamic event-sampling mechanisms are introduced for lessening the computational burden. Meanwhile, the event-sampled Hamilton–Jacobi–Bellman equations (ES-HJBEs) are proposed for the augmented interconnected systems. To approximately solve the ES-HJBEs, the critic approximators are used with their parameters tuned under the reinforcement learning framework. After that, the uniform ultimate boundedness of the tracking errors and the approximators' parameter estimation errors are assured based on the Lyapunov theorem. Finally, a nonlinear plant is provided to validate the decentralized robust dynamic EST control law.

In this article, a hierarchical distributed model-free adaptive fault-tolerant vehicular platooning control scheme for nonlinear vehicular platooning systems (VPSs) is studied. First of all, the dynamic linearization technique (DLT) is utilized to acquire an equivalent linear data model for nonlinear VPSs. Secondly, a hierarchical control structure is developed to realize the vehicular platooning tracking control task, which consists of the upper-layer adaptive distributed observer and the under-layer decentralized model-free adaptive vehicular platooning tracking controller. In order to make each follower vehicle get the leader's information, the adaptive distributed observer is employed to get the estimation value of leader's information. In addition, for the purpose of ensuring the safety of driving, the radial basis function neural network (RBFNN) algorithm is utilized to address the problem of sensor failures. Based on this, a novel hierarchical distributed model-free adaptive fault-tolerant vehicular platooning control scheme is designed to achieve simultaneous tracking of vehicular position and speed. Lastly, the validity of the theoretical control scheme is demonstrated through a realistic and detailed simulation example of a VPS.

The trade off analysis between the fixed-time stabilization in probability and energy consumption of nonlinear stochastic system is studied in this paper. By constructing a switching controller and using inequality techniques, sufficient conditions for fixed-time stabilization in probability in the Lyapunov sense are given, and the upper bounds of the settling time function and energy consumption are estimated. Then, by analyzing the relationship between control parameters, control time and energy consumption, the existence of trade off between control time and energy consumption is proposed, and the corresponding optimal parameter values are given. Finally, a numerical example is used to verify the validity of the theoretical results.

Aiming at resolving trajectory tracking control challenges during high-speed lane changes in intelligent driving vehicles, an innovative fractional-order sliding mode control approach is introduced in the present study. The control strategy comprises upper and lower-level controls. First, the upper-level control designs the vehicle trajectory tracking controller, integrating a non-singular terminal sliding mode (NTSM) surface with a fractional-order fast super-twisted sliding mode control (FOF-STSMC) algorithm. The NTSM surface properties ensure rapid convergence of the system tracking error to zero within a finite time, while the fractional-order control extends the control system's regulation range and enhances algorithm flexibility. Additionally, the integration with the super-twisting algorithm effectively mitigates oscillation issues in the control input, achieving a smooth input. Second, the lower-level control aims to enhance vehicle driving stability. Utilizing the reference yaw rate, and sideslip angle and accounting for tire force saturation, a fractional-order sliding mode control (FOSMC) algorithm is developed to compute the external yaw moment. Through dynamic load allocation, considering the vertical load for each tire, intelligent external yaw moment distribution significantly improves vehicle stability. Finally, the results of the Carsim–Simulink co-simulation demonstrate that, compared to the STSMC strategy, the FOSMC strategy with front-wheel-only steering, and the linear quadratic regulator (LQR) control strategy, the proposed control strategy in this paper reduces the tracking error by 77%, 61%, and 58%, respectively, achieving more precise and stable trajectory tracking under high-speed conditions.

Intermittent random denial-of-service attacks (IRDoS) and deception attacks in multi-UAV systems (MUAVs) can present significant security challenges. The intrusions of IRDoS attacks and deception attacks, respectively, can interrupt the network communication among followers and manipulate the received neighbor information with a certain probability, failing MUAVs to complete the formation task. A secure formation controller is developed for MUAVs in the presence of IRDoS and deception attacks, utilizing a distributed dynamic event-triggered mechanism (DETM). Unlike the static event-triggered mechanism, the triggering threshold of the DETM is adaptively adjustable, which can reduce data transmission and save network resources. The stability of the system is analyzed, and sufficient conditions are derived. Additionally, the duration and probability of successful attacks are examined. Ultimately, the simulation results showcase the efficacy and superiority of the suggested approach.