Research on the Line of Sight Stabilization Control Technology of Optronic Mast under High Oceanic Condition and Big Swaying Movement of Platform

Abstract

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.

Design and Analysis of the Composite Stability Control of the Reflective Optoelectronic Platform

“…To validate the proposed compensation method for the composite stability mechanism, its effectiveness was compared with existing research methods. Stability precision was evaluated using error range under constant frequency disturbance of 1 degree and 1 Hz, with methods such as ZPETC [21], XPC real-time simulation [22], optimized adaptive Kalman filtering (OAK) [23], high-speed target tracking (HT) [24], LuGre and ADRC (LA) [18], back-stepping control (BS) [19], and RBFNN and SMC (RS) [20] used as comparison indices.…”

“…Nevertheless, the effectiveness of these research improvements is constrained by the low-frequency phase lag issue caused by filters lacking a zero point. Some other researchers have used adaptive algorithms to enhance the stability error compensation process for optoelectronic platforms and validated their approach through simulations [15,[17][18][19][20]. However, developing an adaptive algorithm model requires extensive empirical data and can be challenging to implement, raising questions about its practical utility in solving engineering problems.…”

“…Furthermore, performance of the proposed method is compared with traditional reflector stabilization methods and other existing classic methods, such as methods of the disturbance observer combined with zero phase error tracker (ZPETC) [21], XPC real-time simulation [22], optimized adaptive Kalman filtering [23], high-speed target tracking [24], LuGre and ADRC [18], back-stepping control [19], and RBFNN and SMC [20]. The stability precision, calculated by measuring the error range under constant frequency disturbance, is used as the comparison index.…”