Abstract
This paper proposes an adaptive learning control scheme to solve high-precision velocity tracking problem for tank gun control servo systems. Lyapunov approach is used to design the learning controller, with alignment condition used to cope with initial problem of iterative learning control. Robust control technique and adaptive learning control technique are synthesized to handle nonlinear uncertainties and external disturbances. The unknown parameters are estimated according to the full saturation difference learning strategy. As the iteration number increases, the system state can accurately track the reference signal over the whole time interval, and all signal are guaranteed to be bounded.
Highlights
As a kind of useful weapons in battle fields, tanks can both improve the efficiency of artillery firepower and strengthen the surviving ability
In [2], a variable structure control scheme is proposed to solve the position tracking for tank guns with large uncertainties
In [5], sliding mode control based on optimization was reported to cope with the motion control of tank guns
Summary
As a kind of useful weapons in battle fields, tanks can both improve the efficiency of artillery firepower and strengthen the surviving ability. The ILC results on the trajectory-tracking problem for gun control servo systems of tank is few. In this paper, referring to the ILC algorithms design for PMLSMs and PMSMs, we want to solve the trajectorytracking problem for gun control servo systems of tank by using ILC approaches. (1) Difference from those existed results, in this work, robust adaptive iterative learning control technique is introduced to develop the control algorithm for tank gun control servo systems, which is helpful to get better tracking performance for the corresponding systems. (2) In the process of ILC design for tank gun servo systems, alignment condition is used to remove/relax the zero initial error condition, which should be observed in most traditional ILC algorithms. On the basis of (11), we propose the following learning control law for system (1) as uq,k
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