Guozeng Cui

The generalized homogenization-based protocol is proposed, which provides a novel direction for the challenging problem of finite-time consensus tracking (FTCT) control in singular multiagent systems (SMASs) in this paper. The proposed FTCT protocol is continuous and can be regarded as an upgrade of the distributed protocol for asymptotically stable analysis. Then, the homogeneity conditions of the closed-loop systems under the proposed protocol are derived, providing an important prerequisites for FTCT analysis. The FTCT problem for SMASs is successfully addressed by combining the construction of canonical homogeneous norm Lyapunov function and impulse free analysis. Finally, the simulation results are provided to validate the effectiveness of the proposed method.

This paper investigates the predefined-time (PT) formation control for heterogeneous vehicle systems with general narrow tunnel constraints and non-affine actuator faults. Unlike conventional barrier Lyapunov function-based approaches that rely on specific constraint forms, a novel funnel-based control framework is developed to directly accommodate general curvilinear constraint boundaries. To address the explosion of complexity issue, an enhanced nonlinear filter is proposed, which reduces parameter dependence while improving filtering accuracy. By combining funnel control, coordinate transformations, and Butterworth low-pass filter, an adaptive broad fuzzy neural network control strategy is constructed to ensure PT stability of the closed-loop system. Besides, the proposed approach guarantees safe vehicle operation under narrow-tunnel constraints while keeping formation errors within prescribed performance funnels. Real-time experiments on a Speedgoat platform validate the effectiveness and feasibility of the proposed method.

This paper proposes a predefined-time adaptive optimal control strategy to address the trajectory tracking problem for a quadrotor unmanned aerial vehicle (QUAV). By employing the command filter and nonsmooth error compensation mechanism, the issue of “explosion of complexity” (EOC) and the effect of filtered errors are successfully addressed, respectively. By integrating the actor-critic structure from reinforcement learning into command filtered backstepping design method, both feedback and feedforward signals are optimized to minimize the performance index. It is proved that all signals of the closed-loop system are predefined-time bounded, and the tracking errors converge to a neighborhood of the origin within a predefined time. Finally, simulation examples are conducted to verify the effectiveness and superiority of the developed predefined-time adaptive optimal control strategy.

Traditional Lyapunov stability theory cannot directly apply to constrained stochastic nonlinear systems when using barrier Lyapunov functions due to their inherent lack of radial unboundedness. This article presents a novel approach to establishing finite-time stability for such systems by employing Lyapunov functions. The proposed stability analysis relies on a time-varying gain function that remains uniformly bounded. This approach ensures that the system achieves finite-time stability with an arbitrarily prescribed upper bound on the settling time, thereby effectively avoiding the unbounded controller gain problem. The resulting stability is further extended to the finite-time state-feedback control for strict-feedback stochastic nonlinear systems with output constraints. Simulation studies validate the effectiveness of the proposed control scheme.

In practical control systems, limitations such as binary sensing, sensor saturation, and inherent nonlinearities often restrict the available feedback to only the sign of the measured variables. These constraints, however, lead to the adoption of sign-based control strategies, which offer benefits such as reduced sensor cost, lower computational requirements, and improved robustness. In response to such challenges, this article introduces a novel twisting-like second-order sliding mode (SOSM) algorithm using only signs of the state variables. The proposed SOSM controller can achieve exact output regulation for uncertain nonlinear systems within a prescribed convergence time bound, which is independent of the initial conditions and other design parameters. On the one hand, based on a finite time-varying gain function, the proposed SOSM algorithm offers a more straightforward way to adjust the convergence time. Moreover, it has the potential to substantially reduce the demands on both sensing and computational resources. Finally, simulation comparison and experimental validation on permanent magnet synchronous motors (PMSMs) are presented to demonstrate the effectiveness of the proposed controller.

This article investigates the low-complexity predefined-time (PT) optimal formation control (FC) of multinonholonomic mobile robots (MNMRs) with collision avoidance safety requirements. First, by applying the funnel technique, the collision avoidance and communication range constraints between adjacent robots are uniformly transformed into formation error constraints, thereby eliminating the complex barrier Lyapunov functions in existing methods and reducing the complexity of controller design. Then, to mitigate the complexity explosion problem, an enhanced nonlinear filter with adaptive parameters is designed, which reduces the computational load while improving the convergence performance of the system. On this basis, the low-complexity safe FC scheme is constructed using broad fuzzy neural networks (BFNNs) and reinforcement learning techniques, achieving PT stability, optimality, avoiding collisions, and no overshoot behavior in formation errors. Finally, the effectiveness and feasibility of the proposed control method are verified on the Speedgoat real-time target machine.