Multivariable adaptive super-twisting guidance law based on barrier function

For tactical missiles, sliding mode control and super-twisting algorithms have been widely studied in the area of guidance law design. However, these methods require the information of the target accelerations and the target acceleration derivatives, which is always unknown in practice. In addition, guidance laws utilizing these tools always have chattering phenomena and large acceleration commands. To solve these problems, this article introduces a barrier function based super twisting controller and expands the controller to a multivariable adaptive form. Consequently, a multivariable adaptive super-twisting guidance law based on barrier function is proposed. Moreover, the stability of the guidance law is analyzed, and the effectiveness and the robustness are demon-strated by three simulation examples. Compared with previous guidance laws using sliding mode control or super-twisting algorithm, the one proposed in this paper does not require the information of target accelerations, nor target acceleration derivatives; it has smaller super-twisting gains so that has smaller acceleration commands; it can increase and decrease the gains to follow the target accelerations and maintain the sliding mode, and it does not chatter.