This paper studies the target-tracking problem of underactuated surface vessels with model uncertainties and external unknown disturbances. A composite robust adaptive self-structuring neural-network-bounded controller is proposed to improve system performance and avoid input saturation. An extended state observer is proposed to estimate the uncertain nonlinear term, including the unknown velocity of the tracking target, when only the measurement values of the line-of-sight range and angle can be obtained. An adaptive self-structuring neural network is developed to approximate model uncertainties and external unknown disturbances, which can effectively optimize the structure of the neural network to reduce the computational burden by adjusting the number of neurons online. The input-to-state stability of the total closed-loop system is analyzed by the cascade stability theorem. The simulation results verify the effectiveness of the proposed method.

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In this study, a robust fixed-time H∞ trajectory tracking controller for marine surface vessels (MSVs) is proposed based on self-structuring neural network (SSNN). First, a fixed-time H∞ Lyapunov stability theorem is proposed to guarantee that the MSV closed-loop system is fixed-time stable (FTS) and the L 2 gain is less than or equal to γ . This shows high accuracy and strong robustness to the approximation errors. Second, the SSNN is designed to compensate for the model uncertainties of the MSV system, marine environment disturbances, and lumped disturbances term constituted by the actuator faults (AFs). The SSNN can adjust the network structure in real time through elimination rules and split rules. This reduces the computational burden while ensuring the control performance. It is proven by Lyapunov stability that all signals in the MSV system are stable and bounded within a predetermined time. Finally, theoretical analysis and numerical simulation verify the feasibility and effectiveness of the control scheme.

In this study, a fixed‐time adaptive cooperative controller by a self‐structuring neural network is proposed, and actuator faults are considered for multi‐robot systems. First, a novel fixed‐time leader state observer is developed to estimate the state information of the leader and pass on to other followers without measuring the leader's velocity. Second, a fixed‐time cooperative controller is designed to achieve fast response and high precision. Third, a fixed‐time convergent self‐structuring neural network is designed to improve the approximation accuracy affected by system uncertainties and actuator faults. A new neuronal splitting strategy is designed to avoid excessive computational burden caused by too many neurons. Next, the Lyapunov stability theorem is employed to demonstrate that the whole error closed‐loop system can globally converge to a small region around zero in a fixed time. Finally, a simulation example on multi‐robot systems shows that the proposed fixed‐time adaptive cooperative controller is able to obtain satisfactory performances in the presence of uncertainties from external disturbances, actuator faults and other causes.