Dynamic modeling is essential for comprehending physical mechanisms and devising control strategies in bionic robot research. This letter introduces a novel dynamic modeling method that combines Lagrangian dynamics and data-assisted techniques for robotic fish with bionic morphology, multi-joint structures, and a flexible caudal fin. Firstly, a nonlinear continuous hydrodynamic model has been developed using extensive data derived from computational fluid dynamics (CFD), thereby capturing the high-fidelity locomotion of robotic fish. Secondly, based on mathematical derivation, a stability analysis method and controller design approach for biomimetic systems with periodic behaviors have been proposed. Furthermore, to demonstrate the model's efficacy, we designed a model reference adaptive controller for speed control. Both simulation and experimental results validate the model's accuracy, effectiveness, and potential for improving control consistency in tracking time-varying speeds of robotic fish.

This letter aims to enhance the autonomy of bionic robotic fish formation in executing underwater tasks by integrating visual recognition and control systems. Firstly, we design an adaptive image enhancement (AIE) module that integrates a hyperparameter neural network (HPNN) into the YOLOv8 framework, which improves recognition performance under low-light and high-interference underwater conditions, enhancing the obstacle perception capability of underwater robots. Secondly, considering the underactuated and nonlinear hydrodynamic characteristics of the robotic fish system, a formation heading and speed controller with constrained control freedom is designed. This involves establishing a side-slip dynamics model for the robotic fish, analyzing its nonlinear hydrodynamics, and proving the Lyapunov stability of the controller. Finally, the synergistic efficacy of the visual and control systems is validated through a series of experiments, including target tracking, target frame traversal, formation maintenance and reconstruction, and formation obstacle avoidance. These experiments demonstrate that the collaboration of the proposed perception and control modules significantly enhances the capability of the robotic fish formation to autonomously undertake complex underwater tasks.

The current paper verifies the asynchronous $H_{\infty }$ control and optimization problem for flight vehicles with a time-varying delay. The nonlinear dynamic model and Jacobian linearization establish the flight vehicle’s switched model. An asynchronous $H_{\infty }$ tracking controller is designed, considering the existing asynchronous switching between the controllers and corresponding subsystems. In order to promote transient efficiency, the tracking controller comprises the model-based part and the learning-based part. The model-based part guarantees stability and prescribed efficiency, and the learning-based part compensates for undesirable uncertainties. The multiple Lyapunov function (MLF) and mode-dependent average dwell time (MDADT) methods are utilized to guarantee stability and the specified attenuation efficiency. The existing conditions and the solutions of model-based sub-controllers are represented by linear matrix inequalities (LMIs). The deep Q learning (DQL) algorithm provides the learning-based part. Different from the conventional method, the controller parameters are scheduled online. Therefore, robustness, stability, and dynamic efficiency can be met simultaneously. A numerical example illustrates the efficiency and advantage of the presented approach.

In complex underwater acoustic environments, biases in the effective sound speed and propagation-delay estimation errors may introduce skewness and heavy tails into measurement noise, thereby degrading maneuvering target tracking accuracy. Under these conditions, noise parameter estimates obtained through per-time-step variational updates are susceptible to measurement outliers, while coupling among parameters may hinder effective parameter updates. To exploit recent observations and improve parameter estimation stability, this paper proposes an adaptive interacting multiple model sliding-window variational Bayesian unscented filtering algorithm. Within the interacting multiple-model framework, variational inference and the unscented transform are combined to estimate the target state and noise-related variables at each time step. Sliding-window parameter fusion is applied to the location parameter and mixing variable, while sliding-window maximum a posteriori estimation is employed to update the shape parameter. Furthermore, an adaptive process noise covariance adjustment strategy based on changes in model probabilities is developed to balance rapid response to target maneuvers with robust tracking during steady motion. Simulation and lake-trial results demonstrate that the proposed algorithm outperforms the benchmark methods under skewed heavy-tailed measurement noise and target maneuvers. In the lake trial, the mean position and velocity tracking errors are reduced by 54.5% and 10.7%, respectively.

Longitudinal oscillations are commonly observed in depth control of low-speed underwater platforms such as bio-inspired robotic fish. To investigate this issue, this letter conducts a quantitative analysis of the vertical-plane dynamics. It reveals the speed-dependent coupling between the depth and pitch channels as the swimming speed decreases, and clarifies the underdamped mechanism induced by restoring-moment dominance. A critical speed condition for the onset of low-speed oscillations is also derived. Based on the dynamic analysis, a hierarchical GVF–MPC depth control framework is proposed, which structurally decouples geometric guidance from dynamic execution. The guidance layer employs a guiding vector field (GVF) to generate naturally smooth attitude references from geometric information, with reduced sensitivity to model degradation and pitch disturbances. The inner loop applies model predictive control (MPC) to track the guidance commands while explicitly accounting for dynamic constraints, balancing convergence speed and control-input smoothness to improve stability and safety under low-speed conditions. Simulation and experiments are conducted to evaluate the effectiveness and robustness of the proposed method. Simulations consider sinusoidal and V-shaped depth path and compare the proposed approach with LOS, PID, and SMC based controllers. Experiments are performed in still water and under simulated Sea-State 1 disturbances to assess steady state accuracy, disturbance recovery, and overall robustness.