Design of Particle Swarm Optimization Adaptive Sliding Mode Controller Based on an Extended State Observer for the Longitudinal Motion of a Supercavitating Vehicle with Input Saturation

Abstract

The aim of this paper is to present a novel sliding mode control scheme for the supercavitating vehicle trajectory tracking problem that subjects to external disturbances and actuator saturation with two symmetric elevators and a cavitator as actuators by analytical methods and computer simulations. Firstly, the nonlinear and highly coupled dynamic and kinematic models of a supercavitating vehicle are presented in a comprehensive way by taking the cavity memory effect and time-variant planing force into consideration. The PSO algorithm is employed to optimize the control parameters for achieving better and more practical tracking performance by minimizing the objective function. A second-order extended state observer (ESO) is utilized to estimate the unknown external and state-dependent disturbances and compensates for control inputs. In addition, an antiwindup compensator is adopted to cope with actuator saturation. Finally, the proposed control scheme is employed for complex trajectory tracking of a supercavitating vehicle under various conditions by conducting comparative numerical simulations. Rigorous theoretical analysis and simulation results indicate that the proposed control scheme can achieve satisfactory tracking performance and have a good capability of robustness.

Research on Encounter Probability Between a Supercavitating Vehicle and a Target Based on Path Planning

“…The supercavitating vehicle model adopted in this paper is illustrated in Fig. 1, which is widely adopted in the study of motion control [12,40,41]. The encounter models between the supercavitating vehicle and the target in two modes are derived, and the encounter situation is depicted in Fig.…”

“…. Based on the law of error propagation, the variance of B X can be represented by (18). According to (3) and ( 10), the distances , 1, 2, 3, 4 Bmin Bnax X X is obtained according to (17).…”

“…Literature [17] proposed estimation methods of the minimum turning radius of a supercavitating vehicle and the maximum deflection angle of cavitation. Motivated by the previous research on lateral rotation of the supercavitating vehicle, we adopted the adaptive sliding mode control method based on neural networks to obtain the lateral maneuvering ability of a supercavitating vehicle on the premise that the actuators did not fail [18].…”

Research on Lateral Maneuverability of a Supercavitating Vehicle Based on RBFNN Adaptive Sliding Mode Control with Rolling Restriction and Planing Force Avoidance

“…However, the simulation results showed the existence of chattering [12]. The paper [13] proposed a particle swarm optimization adaptive sliding mode controller to control the longitudinal motion of a supercavitating vehicle considering time-delay effects. External disturbances were estimated by an extended state observer.…”