Tele‐aiming control design for reconnaissance robot using a strong tracking multi‐model extended super‐twisting observer

Abstract

In this study, a tele‐aiming control system for reconnaissance robots is proposed, capable of enabling operators to quickly control the robot to aim at and track the manoeuvring target with random motion characteristics under measurement noise. To track such manoeuvring targets, a cascaded multi‐model super‐twisting observer is designed. First, the system uncertainty and the inertial impact force disturbance generated during rapid tele‐aiming are expanded into a lumped disturbance state. Subsequently, a strong tracking filter is employed to filter and estimate the system output and the lumped disturbance, and the output of the filter is input into the multi‐model extended state super‐twisting observer. The final second‐order state estimation and lumped disturbance estimation of the augmented system are conducted in accordance with the weighted sum of all super‐twisting observers. Lastly, a non‐singular terminal sliding mode controller is designed to compensate for the lumped disturbance and achieve the fast trajectory tracking. To verify the effectiveness of the proposed observer‐based controller, a series of manoeuvring target tracking experiments was performed. As revealed by the experimental results, the proposed controller can quickly control the reconnaissance robot to aim at and track the manoeuvring target with random abrupt motion characteristics under measurement noise disturbance.

Finite-Time Super Twisting Disturbance Observer-Based Backstepping Control for Body-Flap Hypersonic Vehicle

“…The step command is used as the attitude angle command to analyze the effect of command parameters on the performance of the system, without considering the influence of deviations in aerodynamic parameters and disturbances. In addition, the controller parameters are given as k 1 = diag (15,5), k 2 = diag (20,10) and the command parameters are given as K p , K r = 10, 20, 35, 60, respectively.…”

“…The parameters of the two controllers are listed in Table 3. (20,10) The desired attitude commands are given by: The tracking curves of the attitude angles and their tracking error curves are shown in Figures 5 and 6, respectively. It can be seen that both control schemes exhibit satisfactory performance in the presence of disturbances, and the control scheme proposed in this paper can guarantee the tracking results with a faster convergence time.…”

“…Another realistic problem is disturbance and uncertainty. Due to its excellent performance in anti-disturbance and convergence time, sliding mode control (SMC) is a popular choice for designing controllers of nonlinear systems [18][19][20][21]. In a study [22], SMC combined with learning controllers was designed for a T-S fuzzy system of HSVs, and a sliding mode-based extended state observer (ESO) was employed to estimate unmodeled dynamics.…”