International Journal of Robust and Nonlinear Control最新文献

This paper considers the problem of global asymptotic stabilization in mean square (GAS-in-MS) by sampled-data output feedback for a class of stochastic nonlinear systems with state delays. Under the forward completeness condition, we show that global asymptotic stabilization in mean square of the stochastic nonlinear systems with arbitrary large state delays is possible by sampled-data output feedback. By using the Lyapunov–Krasovskii functional method together with the robust control technique, we prove that the proposed sampled-data output feedback controller makes the hybrid closed-loop system with state delays globally asymptotically stable in mean square, as long as the sampling period is limited. Finally, the sampled-data control scheme is applied to a numerical example and the underactuated, weakly coupled, unstable mechanical system.

In this article, a fixed-time event-triggered fuzzy adaptive control strategy is investigated for underactuated unmanned surface vehicles (USVs) formation. Initially, the underactuated issue is tackled by transforming the mathematical model of USVs. Then, the preset formation is achieved by the leader-follower approach, and a fixed-time differentiator is employed to obtain the real-time differential signals of the virtual control law, which reduces the complexity of the controller design. Fuzzy logic systems (FLSs) are employed to approximate model uncertainties and external disturbances, and fixed-time convergence is obtained. Furthermore, a fixed-time relative threshold event-triggered controller is designed based on an asymmetric differentiable saturation function, and an auxiliary saturation system is introduced to attenuate the effect of input saturation. Finally, the boundedness of the signal in the closed-loop system is proved by the Lyapunov theory. Simulation experimental results verify the effectiveness of the proposed strategy.

This paper investigates the predefined-time consensus tracking problem for second-order nonlinear multi-agent systems (MAS) under disturbance. Firstly, a predefined-time observer is designed to estimate the state of the entire system, ensuring that the observed state and the leader state achieve consensus within a predefined time. Secondly, a nonsingular terminal sliding mode (NTSM) controller is designed to drive the tracking errors to zero exactly within a predefined time, effectively handling nonlinearities and disturbances without singularity. In this context, the convergence time is directly determined by tunable parameters, enabling the control protocol to satisfy specific design requirements. Using Lyapunov stability theory and predefined-time stability theory, we rigorously derive sufficient conditions for predefined-time consensus in disturbed second-order nonlinear MAS. Furthermore, a numerical simulation is provided to demonstrate the validity and effectiveness of the proposed theoretical results.

Despite many years of discrete sliding mode controller (DSMC) development, non-affine nonlinear systems remain fundamentally intractable due to nonlinear control coupling that violates the DSMC's affine structure requirement. This limitation excludes vast classes of real-world systems, including chemical reactors, aerospace vehicles, and biological processes, from benefiting from DSMC's proven robustness. This work successfully addresses the long-standing challenge of applying DSMC to non-affine systems, motivated by the critical need to extend robust discrete control methodology to nonlinear non-affine systems. Unlike previous NN-based DSMC models, our method provides global convergence and robustness without requiring offline training or fixed-gain design. This work introduces three novel contributions: (1) first-ever integration of least-squares support vector regression (LSSVR) with DSMC for non-affine systems, (2) novel NARMA-L2 decomposition enabling exact affine transformation for DSMC, and (3) dual-layer adaptive framework with proven global convergence. First, the nonlinear autoregressive model with exogenous inputs (NARX) framework is employed, with online LSSVR applied to identify system dynamics accurately. This NARX model is then transformed into a NARMA-L2 structure, which allows the original non-affine system representation to be reformulated as an affine system. Using this affine transformation, a discrete-time sliding mode control law is derived and formulated based on system dynamics to ensure robust trajectory tracking under parametric uncertainties, external disturbances, and measurement noise. The approach includes adaptive tuning of DSMC parameters using the Levenberg–Marquardt algorithm, allowing real-time updates to controller gains based on observed errors and model discrepancies. Simulation results demonstrate that the proposed control architecture provides superior tracking performance and robustness across a range of challenging conditions, showcasing its potential for application in high-precision, digital control systems. This integration of adaptive DMSC and LSSVR within a NARMA-L2 framework represents a significant advancement in sliding mode control for non-affine systems in discrete time.

This paper deals with the design of an adaptive and robust control scheme to achieve the rotor speed stability enhancement and the terminal voltage regulation through the excitation control of a single synchronous generator connected to an infinite bus through a transmission line. A comprehensive 10th-order plant model of the plant is provided, including the exciter system dynamics, under the condition that all model parameters are completely unknown. The scheme design is developed based on a combination of modified adaptive backstepping, second-order constrained SM, and fuzzy logic methods. Based on the stator currents and the rotor angle-speed sensors data and the plant model structure, an adaptive sliding mode observer is designed to estimate the unmeasured rotor flux. Using the estimated rotor flux to overcome the uncertainty in the plant model, two sliding surfaces on which the rotor speed and terminal voltage errors asymptotically tend to zero are formulated. With only one excitation control input, a discontinuous control algorithm is proposed in combination with fuzzy logic to achieve finite-time convergence of the closed-loop system state to the intersection of the two designed sliding surfaces. The effectiveness of the proposed control scheme is confirmed by simulation.

The fixed-time leader-follower consensus issue in second-order multi-agent systems (MASs) with uncertain external disturbances will be investigated in this study. The multi-agent systems are constituted of $��N�$ followers and a leader. First, a distributed fixed-time observer is developed to estimate the leader's states for each follower, taking into account that only a subset of followers has access to the leader's information. Subsequently, a sliding mode adaptive controller is designed to achieve fixed-time leader-follower consensus while enhancing the robustness of the system. In particular, convergence time bounds are determined by tunable parameters, which provide flexibility in designing consensus protocols to satisfy the desired convergence time requirements. Finally, a simulation example is presented to confirm the efficacy of the proposed theoretical results.

In this paper, an interval observer for continuous linear time-invariant systems with unknown input and bounded disturbances is proposed. Usually, the design of such observers is based on the cooperative property of the considered system dynamics, which is hard to satisfy in many cases. To overcome this issue, in a recent work, it has been shown that under some assumptions, the cooperativity of the studied system can be ensured by means of a time-varying change of coordinates. However, a constructive method for the design of the observer gain, making the observation error dynamics simultaneously positive and stable, is still missing and remains an open problem, especially in the context of unknown input. In this paper, a constructive approach is provided to obtain not only the observer gain but also a new gain that cancels the unknown input of the system and reduces the effect of the bounded disturbances. The proposed approach also allows for estimating the bounds of the unknown input. The efficiency of the proposed methodology is shown through numerical simulations and is compared with simulations resulting from a new method proposed in a recent work.

This study addresses the problem of identifying unknown parameters in fractional-order state-space (FOSS) systems, which more accurately capture the dynamic behavior of actual systems compared to their integer-order counterparts. The parameter identification of the FOSS systems is an important research topic in control theory. As the dimension of the FOSS systems increases, the computational complexity of the identification algorithm also increases. In addition, the process of identifying unknown states further complicates the problem. We adopt a hierarchical identification method to achieve joint estimation of states and parameters. The interactive estimation of parameters and states is achieved by using the hierarchical multi-innovation least squares algorithm for parameter estimation and the Kalman filtering for estimating unknown states. Finally, two simulation examples and an application example are employed to verify the effectiveness of the proposed algorithm. The simulation results show that this method has high identification accuracy and fast convergence speed for FOSS systems.

In this paper, the digital control problem of trajectory tracking is investigated for a random Lagrange system. Firstly, for a general random differential equation, it is shown that the “equivalent output” signal of the sigma-delta modulation can almost match the input signal generated by a continuous feedback controller. Then, considering the random Lagrange system driven by DC motors, a continuous bounded controller is systematically synthesized by the vectorial backstepping technique and random quadratic programming. On this basis, a digital controller is designed via sigma-delta modulation, which enables the tracking error of the closed-loop system can be as small as possible by adjusting the parameters. Finally, a simulation experiment of a 3-link robotic arm is given.