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This work presents an advanced decentralized multiloop output-feedback solution enabling closed-loop systems to reduce the position synchronization errors via feedback-loop intelligentization and a model-free observer while handling the challenges of the practical concerns, system model and load information dependence, imperfect output measurement, complicated feedback system structure. The contributions of this article fall into three parts: 1) the model-free observer for estimating the speed and acceleration specifies its gain for two design parameters, resulting in diagonalized estimation error dynamics; 2) the observer-based proportional-integral-derivative inner loop control exponentially stabilizes speed errors according to the desired first-order convergent system through the order reduction technique; and 3) the outer loop control injects the desired critically damped position dynamics by incorporating a simple feedback-loop intelligence mechanism. The effectiveness of the proposal is validated through an experimental study using a hardware platform comprising QUBE-servo2 and a MyRIO1900 processor.

This article proposes a finite-memory-based front-wheel angle estimation (FMFWAE) strategy for robust and accurate performance in a steer-by-wire (SBW) system. The SBW system integrates the steering actuator and vehicle models, considering both the steering angle and vehicle motion. Our proposed FMFWAE, designed based on the SBW system, operates with the finite-memory property, using only a limited number of recent measurements and control inputs. This finite-memory property provides robustness to the proposed FMFWAE against linearization errors, model uncertainties, and fault signals. Furthermore, by developing a Frobenius norm minimization problem, we obtained the gain matrices of FMFWAE to minimize the impact of linearization errors and model uncertainties on the SBW system. The robust and accurate performance of FMFWAE was validated through real-time experiments.

Communication connectivity is the main factor that affects networked unmanned surface vehicles’ (NUSVs) formation maneuverability. When vehicles are maneuvered for obstacle avoidance, distances between neighbors may exceed the limits of allowable communication ranges, causing communication interruption. Within this context, this paper proposes an event-driven fully distributed affine formation maneuver control (FDAFMC) scheme for NUSVs undergoing intermittent network connectivity. Firstly, a distributed observer is developed to divide the whole system into the cooperative localization subsystem and the tracking subsystem. A node-based adaptive gain is then developed in the cooperative localization layer to synthesize the intermittent event-triggered mechanism. Based on this framework, communication interruption occurring both in position and yaw channels can be considered integrally. Furthermore, by resorting to a preset ${\mathcal { L}}_{2}$ gain in the tracking layer, system stability, and robustness are tactfully ensured. The prominent feature of the proposed method lies in ensuring maneuver flexibility without assuming a constantly maintained network connectivity. Simulation results show the efficacy of the FDAFMC strategy.

In this article, we address the distributed optimal coordinated control (DOCC) problem for unmanned surface vehicles (USVs) suffering from limited network channels and communication interruptions. For networked USVs, ignoring network limitations can cause serious sailing incidents. Therefore, we developed a DOCC design with a limited number of communication channels and a restricted communication range. The proposed optimal control system includes two subsystems: coordinated trajectory optimization and local trajectory tracking control. The first employs a delicately designed auxiliary system to determine the optimal solution for each USV. To address the scarcity and intermittency of communication channels, we also develop an intermittent interleaved periodic event-triggered mechanism (IIPETM) in the auxiliary system. The second subsystem adopts a neural network-based backstepping approach to achieve the tracking objective. Compared with the existing methods, the developed algorithm can interleave the triggering instants for different USVs and ensure system stability, even under communication interruptions. We demonstrate the effectiveness and superiority of the proposed scheme through numerical simulations of USVs.