1.The Institute of Microelectronic of the Chinese Academy of Sciences, Beijing 100049, China;
2.Beijing Institute of Aerospace Control Devices, Beijing, 100854, China
Abstract：The photoelectric inertially stabilized platform (ISP) is widely used on mobile carriers of unmanned systems. Due to the nonlinearity and parameters uncertainty of the ISP dynamic system, a terminal sliding mode controller combined with radial basis function neural network (TSMCNN) is presented for ISP that has external disturbance and friction. The radial basis function neural network is used to estimate the unknown nonlinear function in the platform dynamics system. Moreover, considering the output saturation characteristics of the brushless DC torque motor in practical applications, an auxiliary function is introduced to compensate for the difference between the ideal control torque and the actual output torque, which improves the stability of the payload image and the rapidity of dynamic target tracking. Finally, the stability and asymptotic convergence of the closed-loop system are verified by Lyapunov's principle. Experimental results indicate that the TSMCNN proposed in this paper has a vibration reduction effect and the RMS value of the gyro is 4.7mrad/s, which improves by 8% compared with the traditional SMC, and increases by 13.3% compared with PID. the proposed control method can better suppress the disturbance of the moving carrier to the photoelectric equipment, and have strong anti-interference ability.
Key words: Inertially stabilization; Terminal sliding mode; Actuator saturation; Neural network control
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