This paper studies the compound learning control of disturbed uncertain strict-feedback systems. The design is using the dynamic surface control equipped with a novel learning scheme. This paper integrates the recently developed online recorded data-based neural learning with the nonlinear disturbance observer (DOB) to achieve good ``understanding'' of the system uncertainty including unknown dynamics and time-varying disturbance. With the proposed method to show how the neural networks and DOB are cooperating with each other, one indicator is constructed and included into the update law. The closed-loop system stability analysis is rigorously presented. Different kinds of disturbances are considered in a third-order system as simulation examples and the results confirm that the proposed method achieves higher tracking accuracy while the compound estimation is much more precise. The design is applied to the flexible hypersonic flight dynamics and a better tracking performance is obtained.

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The finite-time formation tracking control is investigated for a multi-agent system (MAS) with obstacle avoidance. For the collision and obstacle avoidance problem in the formation process, the artificial potential field is used as the formation planning design, and the virtual structure is adopted to improve the organizational ability of the formation. The trajectory tracking control follows the back-stepping scheme, and the finite-time technique is developed in the control design. Considering the dynamics uncertainty of the agent system, a neural network is applied for estimating and the prediction error-based adaptive law is established to achieve the precise estimation performance. Moreover, the predefined performance function is embedded to satisfy the output constraint. The uniformly ultimate boundedness of the system error signals and the finite-time convergence of the MAS are guaranteed. The simulation study is performed to validate the proposed control for multiple autonomous underwater vehicles system, while the results manifest that the obstacle avoidance with high-precision tracking and formation performance will be achieved under the formation trajectory tracking controller.

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