Adjustment and Control of a Certain Top-mounted Gun Based on a Novel Fractional Order Neural Sliding Mode Strategy.

A novel fractional order neural sliding mode control (FNSMC) strategy is proposed for the nonlinearities of a gun control system (GCS) which is used to control the elevation of a certain top-mounted gun. A fractional order PID type sliding surface is especially designed by introducing the fractional order calculus, and an equivalent control discipline with fractional order dynamics is induced. The saturation function is employed as switch function. To achieve the best control performances, a dynamic adjustment approach of the switch gain is introduced based on RBF neural network. The dynamic and static characteristics of the fractional order sliding mode control (FSMC) system are analyzed by numerical simulation, demonstrating that FSMC can reach up to the steady state more smoothly, which significantly suppresses the chatter effects and enhances the response rate of the control system. Finally, a series of experiments on a semi-physics simulation platform are conducted to investigate the performances of control system. The results show that the proposed FNSMC is of more excellence than the conventional integer order neural SMC (CNSMC). The FNSMC-based control system is of better tracking accuracy as well as high robustness, and the fast, smooth and accurate adjustments of the gun can be achieved. [ABSTRACT FROM AUTHOR]