International Journal of Robust and Nonlinear Control最新文献

For affine nonlinear systems with system uncertainties, a dual kernel fuzzy control scheme based on a multilateral learning mechanism is proposed in this paper. Firstly, based on the designed dual kernel fuzzy control structure, the nonlinear approximation error is effectively reduced, and the dynamic response time is shortened. Secondly, under the effect of a multilateral learning strategy, the multilateral parallel dual kernel fuzzy controller further approximates the nonlinear part of the system, so that the approximation error can converge to a small neighborhood of zero in finite time. In addition, the reference value of model uncertainty is constructed based on the relationship between the error differential and a second-order filter, and the parameter update law of the multilateral fuzzy controller is designed in combination with the estimated value, so as to improve the response capability and approximation accuracy of the control system to uncertainty. The saturation transfer function is introduced to solve the problem of parameter oscillation in the control process. The stability of the control system is proved by constructing a Lyapunov function. The effectiveness and advancement of the multilateral learning dual kernel fuzzy controller are verified in the inverted pendulum system and the two-degree-of-freedom manipulator by MATLAB simulation platform.

This paper presents a novel hybrid control strategy for force/position tracking in rigid-link electrically driven reconfigurable manipulators (RL-EDRM), explicitly considering actuator dynamics and time-varying environmental constraints—a combination not previously addressed in the literature. Existing force/position control methods often neglect actuator nonlinearities and dynamic constraint variations, limiting their applicability in real-world scenarios. In this work, the term “hybrid” refers to the integration of both model-based and model-free control schemes within a unified framework. Specifically, the controller combines a model-based backstepping design, which manages the known manipulator dynamics, with a model-free radial basis function (RBF) neural network that approximates unknown dynamics and unmodeled nonlinearities. Additionally, an adaptive compensator addresses external disturbances, friction, and approximation errors. The control scheme ensures asymptotic convergence of tracking errors and enforces constraint compliance by dynamically adjusting force and motion commands. Direct current (DC) motor models are incorporated to generate accurate current and torque profiles. System stability is analytically guaranteed using Lyapunov theory. Simulation studies on a 2-DOF reconfigurable manipulator under time-varying environmental conditions demonstrate the effectiveness of the proposed method. Quantitative results confirm the controller's robustness, adaptability, and enhanced performance compared to conventional model-based strategies.

This paper investigates the distributed secure state estimation problem for interconnected cyber-physical systems (CPSs) under false data injection (FDI) attacks. Firstly, we introduce intermediate variables and output estimation feedback terms for sensor attacks and actuator attacks, and propose two intermediate observer systems based on the proposed two intermediate variables of sensor attacks. The agent dynamic error system is derived based on the estimation error of the state and intermediate variables. Then, the $��{�H�}_{�\infty �}�$ performance index is utilized to suppress FDI attacks, and the robustness condition and error convergence boundedness of the system errors are proposed, by which observer gain matrices can be solved in terms of linear matrix inequalities. The proposed distributed secure state estimation approach, based on the original system matrix framework, offers a significant improvement over previous methods, which rely on augmented system models. It substantially reduces the communication burden, enhances the flexibility and adaptability of the design, and ensures robustness and uniform boundedness against system errors. Finally, the effectiveness of the proposed method is validated through numerical simulations.

This article studies the optimal cooperative tracking control problem of nonlinear multi-agent systems (MASs) in the presence of unknown dynamics and denial-of-service (DoS) attacks. It is more challenging to achieve the desired optimized performance under DoS attacks, especially when neural networks are utilized to identify the unknown dynamics online. Considering two types of DoS attacks (connectivity-maintained attacks and connectivity-broken attacks), the article presents a novel defense control strategy consisting of a resilient distributed observer and a neuro-adaptive optimal cooperative tracking controller. In specific, by introducing a label information algorithm, the resilient distributed observer for each follower agent is presented to reconstruct the leader's states under two types of DoS attacks. Subsequently, a neuro-adaptive optimal cooperative tracking controller is proposed under an identifier-critic-actor architecture in the presence of DoS attacks. It is proven that the output of all follower agents can synchronize with that of the leader under DoS attacks, while the local performance indexes reach the Nash equilibrium simultaneously. Finally, the simulation results show the effectiveness of the presented optimal cooperative tracking control methodology.

This article investigates secure proportional-integral-derivative (PID) control for cyber-physical systems (CPSs) subject to joint deception attacks that randomly occur in both sensor-observer (SO) and controller-actuator (CA) channels. First, the attack model is established by two steps. The deception attack randomly occurring in the CA channel is treated as an additional unknown input (UI) and merged with the original UI to form a multiple UI. An output transformation technique is then applied to derive a new system output that can completely eliminate the deception attack in the SO channel. Next, an algebraic equation is developed to connect the system state with the multiple UI by using an interval observer. On this basis, an unknown input observer (UIO) is constructed to provide asymptotic estimates of both the system state and multiple UI. Subsequently, by employing the orthogonal decomposition approach, a compensation PID controller is developed, which can ensure the stability in the mean-square sense of the considered CPS by defending against deception attacks in both channels. Finally, a practical quadruple-tank system is used to demonstrate the effectiveness of the proposed approach.

This article investigates the event-triggered impulsive control of nonlinear systems with external disturbance by using a dynamic gain method. In the existing works, the dynamic gain method is often utilized to handle unknown parameters of nonlinear systems without external disturbances. In this article, the disturbance rejection problem is solved by introducing a sliding-variable-based method to the designs of the event-triggered impulsive controller and adaptive dynamic gain laws. Under the proposed control method: (i) the systems states converge to a region around the origin; (ii) the system states are globally asymptotically convergent. It is proved that the Zeno phenomenon can be avoided. Finally, the proposed control schemes are applied to the electromechanical system, which shows the effectiveness and advantages of the results.

This paper addresses the integrated attitude and position tracking control for servicing spacecraft in proximity operations with non-cooperative targets. The concerned servicing spacecraft is subject to flexible vibrations, asymmetric actuator saturation, and unknown external disturbances. First, the attitude-position-vibration coupling dynamic model of the servicing spacecraft is established using dual quaternion. Second, an adaptive fixed-time disturbance observer (AFTDO) is designed to compensate for unknown external disturbances and flexible coupling terms. Third, a faster fixed-time tracking controller (FFTC) is proposed based on a novel unwinding-free nonsingular sliding mode surface. In addition, an anti-windup compensator is developed to mitigate the adverse effects of asymmetric actuator saturation. It is demonstrated through Lyapunov stability analysis that the practical fixed-time convergence of the attitude and position tracking errors is guaranteed, while the unwinding phenomenon caused by dual quaternion description is avoided. Numerical simulation validates the effectiveness of the proposed control scheme.

This paper proposes a distributed practical predefined-time robust time-varying formation control (DPPTRTVFC) scheme for multiple Euler-Lagrange systems (ELSs) subject to uncertain dynamics, input saturations, and unknown external disturbances. A new practical predefined-time stability criterion (PPTSC) is created for nonlinear systems, where the condition inequality of our created PPTSC only contains a single fractional power term of a Lyapunov function by employing a piecewise constant function. In addition, we also create a corollary of the PPTSC. Utilizing the corollary of the PPTSC, a predefined-time sliding-mode disturbance observer (PTSMDO) is constructed to estimate the compound disturbances lumped by uncertain dynamics and unknown external disturbances within a predefined settling time (ST). Based on the PPTSC, a predefined-time auxiliary dynamic system (PTADS) is constructed, which not only can be used to handle the input saturation problem but also allows the ST of the PTADS to be flexibly predefined. Further, a DPPTRTVFC law is designed by incorporating the PTSMDO and the PTADS into the dynamic surface control method. Theoretical analysis indicates that the designed control scheme can implement the time-varying formation control within a predefined ST. Simulation results illustrate the effectiveness of the proposed DPPTRTVFC scheme.

In this paper, we design novel types of smooth time-varying controllers/observers for lower triangular nonlinear systems with prescribed time convergence. We prove the results under very mild conditions: No global restrictions or bounds on the nonlinearities are required, but only locally Lipschitz conditions or the existence of solutions over finite intervals. Our smooth time-varying controllers/observers have completely different features in comparison with traditional homogenous or sliding-mode controllers/observers and existing smooth time-varying controllers/observers, being designed with a symmetry-based strategy.

This paper presents a generalized three-dimensional (3D) intelligent impact time control guidance (ITCG) law for intercepting maneuvering targets under the field-of-view (FOV) constraint in the relative virtual framework (RVF). The proposed method addresses key limitations of existing guidance approaches by eliminating the need for time-to-go estimation, model linearization, and decoupling. First, a generalized ITCG law with the FOV constraint is derived by employing two newly defined impact errors and the backstepping control technique. Second, a future average speed prediction algorithm is subsequently introduced. Third, the proximal policy optimization (PPO) algorithm is employed to optimize the controller gain $��\tau �$, which minimizes both guidance errors and energy consumption. Finally, a novel implementation case further illustrates the key advantage of the proposed ITCG law, whose impact time feasible region can be derived analytically. Unlike other existing guidance laws, the proposed approach does not rely on restrictive assumptions or approximations, which makes it more applicable for actual combat scenarios. Numerical simulations validate the superiority of the proposed ITCG law over existing methods in terms of accuracy, robustness, and energy efficiency.