Cheng-Lin Liu

Coverage control in multimodal Gaussian environments remains challenging for distributed multi-agent systems with limited sensing and communication capabilities, as existing methods often lead to incomplete coverage, imbalanced agent allocation, and connectivity interruptions. To overcome these challenges, this paper proposes a novel coverage control strategy based on a leader-following framework. The strategy includes a three-role cooperative mechanism: (1) leaders move toward the preassigned Gaussian modes, (2) coverers optimize local coverage, and (3) connectivity maintainers follow leaders and connect coverers. All agents adopt a composite controller that employs role-specific formulations for the coverage term while using the common connectivity controller, with weights adjusted accordingly. The strategy operates in two phases: the leading phase and the coverage phase. Initially, agents whose number is equal to Gaussian modes are randomly selected as leaders and move toward the assigned Gaussian modes, while other agents follow leaders as connectivity maintainers. Upon meeting a global event-triggering condition, the strategy enters the second phase, and the dynamic role-switching strategy is activated. All leaders switch to coverers, while others are dynamically assigned as coverers or connectivity maintainers based on their distances to Gaussian modes. Numerical simulations demonstrate that the proposed strategy achieves coverage ratios exceeding 95% for all approximate high-density regions, while guaranteeing algebraic connectivity above the predefined lower bound. Compared with the previous coverage method, this strategy achieves more comprehensive and balanced coverage while consistently maintaining connectivity.

This paper investigates an adaptive predefined-time distance regulation control strategy for a two-dimensional (2-D) vehicle platoon with actuator faults and input saturation. First, a novel distance regulation scheme is introduced for scenarios where a ramp vehicle platoon merges into the main lane. After multi-lane fusion is completed, a designated vehicle in the main-lane platoon that receives the merging request actively increases its inter-vehicle distance to create a safe gap, while the distances of the other vehicles remain unchanged. This adjustment may temporarily enlarge tracking errors, which can potentially violate the prescribed performance function (PPF) constraints and threaten platoon stability. To address this issue, a modified prescribed performance function (MPPF) is developed to dynamically relax the error boundary within a specified time window and restore it after merging. Subsequently, an adaptive neural network-based dynamic surface control (DSC) approach is employed to design saturation and fault-tolerant throttle/brake inputs. A predefined-time angle controller is also designed based on an angle sliding mode surface. The resulting position and angle controllers enable the platoon to complete multi-lane fusion and achieve predefined-time stability despite ramp merging. Finally, simulation results validate the effectiveness of the proposed strategy.

This paper proposes a fixed‐time sliding mode prescribed performance control for robotic manipulators with actuator saturation, parametric uncertainties, and external disturbance. Taking the situation that the disturbance may surge temporarily into consideration, we propose a revised performance function capable of adapting according to the saturation status. It overcomes the inherent defect of the traditional prescribed performance function, that is, the singularity problem occurs when the constrained control input fails to maintain the tracking error within the performance funnel. Finally, simulation results are presented to validate and assess the effectiveness of the proposed method.

This article focuses on the 2-D plane finite-time multilane fusion control problem with velocity sensor faults and actuator faults. First, considering the velocity sensor faults, radial basis function neural networks (RBFNNs) are introduced to approximate sensor fault functions. A finite-time fault-tolerant position controller is designed by employing a hyperbolic tangent function to address the partial failure of the position actuator. Second, in case of complete actuator failure, the backup actuator is activated without altering the controller structure. The majority of prescribed performance functions (PPFs) currently in use are unsuitable for addressing complete actuator failure. As tracking errors may exceed PPF constraints before the backup actuator is activated upon such faults, this could result in vehicle platoon instability. To address this issue, this article designs a modified PPF (MPPF) that is independent of the initial conditions and can adjust the performance boundary according to the error change at specific times by introducing a shifting function. When a complete actuator failure occurs, the MPPF can sacrifice part of the transient performance to enclose the increased tracking error within the range of the MPPF, thus maintaining the stability of the vehicle platoon. When the actuator is working normally or has a partial failure, it can restore the user-specified performance. Then, by constructing a finite-time angle sliding-mode surface, an angle controller is designed. The designed position and angle of finite-time controllers can ensure that the vehicle platoon achieves multilane fusion within a finite time. Finally, through simulation and comparative results, the effectiveness of the proposed MPPF and finite-time fault-tolerant algorithm is demonstrated.

This paper addresses the average-tracking control problem for multi-agent systems subject to constant reference signals. By introducing auxiliary signals generated from the states and delayed states of agents, a novel privacy-preserving integral-type average-tracking algorithm is proposed. Leveraging the frequency-domain analysis approach, delay-dependent sufficient and necessary conditions for ensuring asymptotic average-tracking convergence are derived. Furthermore, the proposed algorithm is extended to tackle the average-tracking control problem with mismatched reference signals, and a corresponding delay-dependent sufficient condition is established to guarantee privacy-preserving average-tracking convergence. Numerical simulations are conducted to verify the effectiveness of the developed algorithms.

This paper proposes a finite-time prescribed performance control method to ensure effective two-dimensional (2-D) plane vehicle platoon multi-lane fusion and maintain platoon performance in the presence of unknown dynamic uncertainties and false data injection (FDI) attacks. Firstly, unknown dynamic uncertainties of the vehicle are approximated using radial basis function neural networks (RBFNNs). Building on this neural network approximation under FDI attacks, a novel state observer is developed to estimate the vehicle’s state, restore the communication protocol when the communication link is attacked, and address the complex coupling issues between vehicle states. Furthermore, finite-time prescribed performance control inputs are designed based on the constructed sliding surfaces to ensure practical finite-time stability of the 2-D plane vehicle platoon. This method facilitates vehicle multi-lane fusion within a finite time while guaranteeing platoon performance and preventing collisions. Finally, numerical simulations and comparative analyses are presented to demonstrate the effectiveness and superiority of the proposed control strategy, involving one leader vehicle and six followers.