A robust adaptive fuzzy neural network control (RAFNNC) algorithm is proposed based on a generalized dynamic fuzzy neural network (GDFNN), proportion-integral-differential (PID), and improved bacterial foraging optimization (BFO) algorithm, for heading the control of the unmanned marine vehicle (UMV) in the presence of a complex environment disturbance. First, the inverse dynamic model of the motion control of UMV is established based on the GDFNN for the uncertain disturbance caused by the complex environment disturbance. Then, the adaptive rate of the fuzzy neural network is designed based on the error between the real UMV heading angle and designed reference heading angle, so as to further adjust the weight parameter of the GDFNN, and then, the output control value of the neural network is obtained. In order to further reduce the computation amount and computation time of the RAFNNC, the parameters of the PID control algorithm were optimized in advance by using the improved BFO algorithm. The fractal dimension step size and the intelligent probe operation are integrated into the BFO algorithm, in order to optimize the operation time and accuracy of the algorithm. Stability of the designed RAFNNC algorithm for the heading control of the UMV in the presence of complex marine environment disturbance is proved by the Lyapunov stability theory, and the effectiveness and accuracy of the control algorithm proposed are verified by semi-physical simulation experiment carried out in our laboratory.

To predict the manoeuvrability of unmanned marine vehicle and improve its manoeuvrability, the parameters of the manoeuvring model of unmanned marine vehicle need to be obtained. Aiming at the inconvenience of obtaining model parameters under the traditional experimental method, this article studies the parameter identification of unmanned marine vehicle’s manoeuvring model based on extended Kalman filter and support vector machine. Firstly, the second-order nonlinear manoeuvring response model of unmanned marine vehicle is discretized by the difference method, and the corresponding data are collected by the manoeuvring motion simulation of the response model. Secondly, the discrete response model is transformed into an augmented state vector based on extended Kalman filter, and the optimal estimation of the state vector is calculated to identify the parameters. And then, the discrete response model is transformed into a support vector machine-based regression model, the collected data are processed and a set of support vectors are obtained to further identify the parameters of the response model. Finally, by comparing the simulation experiments’ results from the original model and the identification model, the recognition results-based extended Kalman filter and support vector machine are analysed and some research results are obtained. The results of this article will provide a powerful reference for the design of unmanned marine vehicle’s motion control algorithm.

In this paper, a novel dynamic surface sliding mode control (DSSMC) method, combined with a lateral velocity tracking differentiator (LVTD), is proposed for the cooperative formation control of underactuated unmanned surface vehicles (USVs) exposed to complex marine environment disturbances. Firstly, in view of the kinematic and dynamic models of USVs and the design idea of a virtual control law in a backstepping approach, the trajectory tracking control problem of USVs’ cooperative formation is transformed into a stabilisation problem of the virtual control law of longitudinal and lateral velocities. Then, aiming at the problem of differential explosion caused by repeated derivation in the process of backstepping design, the first-order low-pass filter about the virtual longitudinal velocity and intermediate state quantity of position is constructed to replace differential calculations during the design of the control law, respectively. In order to reduce the steady-state error when stabilising the virtual lateral velocity control law, the integral term is introduced into the design of the sliding mode surface with a lateral velocity error, and then the second-order sliding mode surface with an integral is structured. In addition, due to the problem of controller oscillation and the role of the tracking differentiator (TD) in active disturbance rejection control (ADRC), the LVTD is designed to smooth the state quantity of lateral velocity. Subsequently, based on the dynamic model of USV under complex marine environment disturbances, the nonlinear disturbance observer is designed to observe the disturbances and compensate the control law. Finally, the whole cooperative formation system is proved to be uniformly and ultimately bounded, according to the Lyapunov stability theory, and the stability and validity of the method is also verified by the simulation results.

In this paper, a bio-inspired sliding mode control (bio-SMC) and minimal learning parameter (MLP) are proposed to achieve the cooperative formation control of underactuated unmanned surface vehicles (USVs) with external environmental disturbances and model uncertainties. Firstly, the desired trajectory of the follower USV is generated by the leader USV’s position information based on the leader–follower framework, and the problem of cooperative formation control is transformed into a trajectory tracking error stabilization problem. Besides, the USV position errors are stabilized by a backstepping approach, then the virtual longitudinal and virtual lateral velocities can be designed. To alleviate the system oscillation and reduce the computational complexity of the controller, a sliding mode control with a bio-inspired model is designed to avoid the problem of differential explosion caused by repeated derivation. A radial basis function neural network (RBFNN) is adopted for estimating and compensating for the environmental disturbances and model uncertainties, where the MLP algorithm is utilized to substitute for online weight learning in a single-parameter form. Finally, the proposed method is proved to be uniformly and ultimately bounded through the Lyapunov stability theory, and the validity of the method is also verified by simulation experiments.