ABSTRACT Inventory equilibrium in supply chains can be formulated as a time‐varying multi‐agent competitive resource‐allocation game between supply and demand agents. Its real‐time solution is challenging due to heterogeneous, coupled inequality constraints, and time‐varying parameters. This paper develops a zeroing neural network (ZNN)‐based computational framework for this class of problems. The key feature of the framework is an exponential auxiliary‐variable transformation, through which the original time‐varying matrix inequalities are reformulated as equivalent time‐varying matrix equations suitable for ZNN computation. A finite‐time convergence mechanism based on a sign‐bi‐power activation function is then employed to obtain finite‐time tracking of the transformed system. Lyapunov analysis establishes finite‐time convergence and provides an explicit upper bound on the settling time. Numerical simulations and comparative studies are conducted to evaluate convergence accuracy and computational performance, while application experiments demonstrate its capability to compute feasible competitive decisions online under time‐varying operational constraints and examine robustness to disturbances and problem scale. The study therefore contributes a unified inequality‐to‐equation ZNN framework and its application to real‐time competitive resource allocation in supply chain systems.
ABSTRACT The cybersecurity of autonomous vehicles, particularly under stealthy data‐aware attacks, presents a critical challenge that conventional control systems are ill‐equipped to handle. While existing attack schemes lack efficiency or precision, this paper proposes a novel integrated offensive‐defensive framework to address this gap. First, a sophisticated data‐aware attack scheme is designed, which intelligently selects and corrupts the most critical measurement components in realtime based on a dynamic importance index and a dual‐threshold mechanism. This strategy ensures high destructiveness while maintaining strong concealment. Second, to counter such sophisticated attacks, an attack‐target‐related switching controller is developed that dynamically adjusts its gains from eight pre‐designed modes based on real‐time attack identification. The exponential stability and a prescribed disturbance attenuation performance of the closed‐loop system are rigorously guaranteed via Lyapunov theory. Finally, extensive simulations demonstrate that the proposed attack strategy can significantly degrade system performance and robustness, and more importantly, the proposed defense controller effectively maintains stability and ensures safe operation under malicious attacks, outperforming conventional controllers. This work establishes a foundational offensive‐defensive co‐design paradigm for securing autonomous vehicles against sophisticated cyber‐physical threats.
ABSTRACT This study proposes an intelligent fractional‐order super‐twisting sliding mode control (IFSTSMC) for a 3‐PRP planar parallel robot (PPR). Initially, a standard super‐twisting sliding mode control (STSMC) and a fractional‐order super‐twisting sliding mode control (FSTSMC) are developed for the trajectory tracking of a 3‐PRP PPR. However, theoretical analysis indicates that both methods yield only uniformly ultimately bounded (UUB) stability because fixed control gains cannot fully compensate for state‐dependent uncertainties. To address this limitation, the proposed IFSTSMC incorporates a recurrent neural network (RNN) observer for online estimation of complex system dynamics, alongside a state‐dependent robust compensator to neutralise neural approximation residuals. This hierarchical strategy suppresses residual perturbations, and the system transitions from UUB stability to asymptotic trajectory regulation. Experimental validations on a custom PPR indicate that the IFSTSMC improves the contour tracking performances compared to the STSMC and FSTSMC and achieves enhanced robustness against payload variations.
ABSTRACT With the presence of external disturbance and moving targets, how to establish new predefined time stability criteria to achieve prescribed performance under varying sea states is a challenging problem. A new predefined‐time stability lemma is introduced through employing an exponentially decaying function. It has more adjustable parameters for the size of residual set, and can enter a smaller residual set. Subsequently, according to this lemma, a practical predefined‐time prescribed performance control for cooperative target enclosing of unmanned surface vehicles is proposed. A predefined‐time observer is designed to compensate for uncertain parameters and disturbances. Finally, experimental results validate that this control scheme reduces tracking errors when compared to existing methods, and demonstrates robustness against external disturbances.
ABSTRACT This paper addresses the adaptive tracking control problem for a class of uncertain nonlinear systems. Conventional approximation‐based control methods usually employ fuzzy logic systems (FLSs) or neural networks to directly approximate unknown nonlinear functions. However, the resulting approximation error is highly dependent on the selection of basis‐function parameters, which are often empirically chosen and difficult to characterize precisely. This limitation may degrade the approximation capability and consequently affect the tracking performance. To overcome this issue, the states of the original system are augmented, and a fuzzy state observer is developed to construct an indirect fuzzy approximator for the unknown nonlinear functions. In the proposed framework, the approximation error is equivalently represented by the fuzzy observation error, which can be explicitly regulated through the observer gains. Based on the proposed indirect fuzzy approximator and an adaptive design strategy, an indirect‐approximator‐based adaptive tracking controller is developed. Compared with traditional direct approximation‐based methods, the proposed controller can achieve improved tracking performance by driving the tracking error into a smaller residual set. The stability of the closed‐loop system and the convergence property of the tracking error are rigorously analysed. Finally, simulation and experimental results are provided to demonstrate the effectiveness and superiority of the proposed method.
