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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.

In this article, we propose a polynomial chaos expansion (PCE) approach to interval estimation for uncertain Takagi–Sugeno fuzzy systems by considering time-invariant parameter uncertainties. First, we design the observer to get the point-valued estimates, where the accuracy of interval estimation is ensured by optimizing the gain matrices of the designed observer. Second, to obtain the interval estimation results of real states, the error dynamics is split into a random process with stochastic uncertainties and a bounded process with set-based uncertainties. Different from the interval estimation methods presented in the literature, which use only zonotopic analysis for interval estimation, we use the PCE approach in conjunction with zonotopic analysis for interval estimation, where the random process is analyzed using PCE in conjunction with the zonotopic method, and the bounded process is studied using the zonotopic analysis method. Moreover, a numerical simulation is proposed, and its results illustrate that the presented approach can decrease the conservatism of interval estimation and facilitate more interval estimation accuracy compared to an advanced interval observer design approach. Finally, interval estimation is performed for a vehicle lateral dynamic model to illustrate the applicability and superiority of the presented approach.

This study explored the issue of decentralized adaptive event-triggered neural network (NN) control for nonlinear interconnected large-scale systems (LSSs) subjected to unknown measurement sensitivity and nonconstant control gains. Due to the impact of unknown measurement sensitivity, the real states of LSSs cannot be directly utilized. To overcome this difficulty, an effective adaptive feedback control scheme was developed. Subsequently, NNs were exploited to address the nonlinear terms and unknown nonconstant control gains. A modified first-order compensation system was developed to enhance the control performance in the presence of saturation nonlinearity. Furthermore, a significant dynamic event-triggered control (DETC) protocol was developed based on the saturation controller and measurement error, which reduced the number of controller updates. According to the Lyapunov stability theory, the proposed DETC-based decentralized adaptive protocol demonstrated that all signals were semiglobally uniformly ultimately bounded. The simulation examples illustrate the validity of the presented control protocol.

This article designs a filter-based output-feedback system to regulate the speed of permanent magnet synchronous motors (PMSMs), which structures a simple single-loop form compensated by feed-forward terms. The proposed controller design framework considerably reduces the dependence level of the PMSM model by requiring partial nominal parameter values for control law and removing the model structure and whole parameter information for the filter. The main advantages consist of two parts. First, the proposed observer employs the second-order pole-zero cancelation (PZC) technique to continuously extract the speed and acceleration from noisy position measurements by the rotary encoder, independent from the PMSM model. Second, the PZC filter-based proportional–integral control forms a single-loop feedback system including the active damping injection and disturbance observer, which assigns the critically damped performance to the closed-loop system by specially structuring the feedback gains. The performance improvement derived by the closed-loop analysis is validated through an experimental comparison study using a prototype 700-W PMSM controlled by the 32-b microprocessor.