Based on an integral backstepping approach, a trajectorytracking control algorithm is proposed for an underactuated unmanned marine vehicle (UMV) sailing in the presence of ocean-current disturbance. Taking into consideration the UMV model's fore/aft asymmetry, a nonlinear threedegree-of-freedom (3DOF) underactuated dynamic model is established for the horizontal plane. First, trajectorytracking differences between controllers designed based on symmetric and asymmetric models of the UMV are discussed. In order to explicitly study the effect of oceancurrent interference on the trajectory-tracking controller, the ocean current is integrated into the kinematic and dynamic models of the UMV. Detailed descriptions of distinct trajectory-tracking control performances in the presence of different ocean-current velocities and direction angles are presented. The well-known persistent exciting (PE) condition is completely released in the designed trajectory-tracking controller. A mild integral item of trajectory tracking error is merged into the control law, and global stability analysis of the UMV system is carried out using Lyapunov theory and Barbalat's Lemma. Simulation experiments in the semi-physical simulation platform are implemented to confirm the effectiveness and superiority of the excogitated control algorithm.
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. INDEX TERMS Unmanned marine vehicle (UMV), heading control, robust adaptive fuzzy neural network control (RAFNNC), generalized dynamic fuzzy neural network (GDFNN), bacterial foraging optimization (BFO).
This paper presents a state-feedback-based backstepping control algorithm to address the point stabilization (or setpoint regulation) control problem for an underactuated autonomous underwater vehicle (AUV) in the presence of constant and irrotational ocean current disturbance. A nonlinear three degree of freedom dynamic model in the horizontal plane for an AUV without symmetry fore/aft is considered. The expression of the relationship between the desired heading angle of the AUV and direction angle of the ocean current, which is a necessary condition for precise point stabilization control of an underactuated AUV in the presence of ocean current disturbance is firstly discussed in this paper. The proposed backstepping control law for point stabilization has further been enriched by incorporating an additional integral action for enhancing the steady state performance of the AUV control system, while practical asymptotic stability analysis of the system is carried out using Lyapunov theory and Barbalat's Lemma. Simulation experiments of an underactuated AUV verify the theorem proposed and demonstrate the effectiveness of the controller.
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