State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Huazhong Institute of Electro-Optics, Wuhan National Laboratory for Optoelectronics, Wuhan 430223, China.
Sensors (Basel). 2023 Mar 16;23(6):3182. doi: 10.3390/s23063182.
To realize high-performance line of sight (LOS) stabilization control of the optronic mast under high oceanic conditions and big swaying movements of platforms, a composite control method based on an adaptive radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed. The adaptive RBFNN is used to approximate the nonlinear and parameter-varying ideal model of the optronic mast, so as to compensate for the uncertainties of the system and reduce the big-amplitude chattering phenomenon caused by excessive switching gain in SMC. The adaptive RBFNN is constructed and optimized online based on the state error information in the working process; therefore, no prior training data are required. At the same time, a saturation function is used to replace the sign function for the time-varying hydrodynamic disturbance torque and the friction disturbance torque, which further reduce the chattering phenomenon of the system. The asymptotic stability of the proposed control method has been proven by the Lyapunov stability theory. The applicability of the proposed control method is validated by a series of simulations and experiments.
为了实现海洋环境条件下和平台大幅摆动运动下光电桅杆的高性能视线（LOS）稳定控制，提出了一种基于自适应径向基函数神经网络（RBFNN）和滑模控制（SMC）的复合控制方法。自适应 RBFNN 用于逼近光电桅杆的非线性和时变理想模型，以补偿系统的不确定性，并减少 SMC 中过大切换增益引起的大振幅抖振现象。自适应 RBFNN 基于工作过程中的状态误差信息在线构建和优化，因此不需要先验训练数据。同时，采用饱和函数代替时变水动力干扰转矩和摩擦干扰转矩中的符号函数，进一步减少了系统的抖振现象。通过 Lyapunov 稳定性理论证明了所提出控制方法的渐近稳定性。通过一系列仿真和实验验证了所提出控制方法的适用性。
