In this paper, an observer-based adaptive fuzzy robust controller is proposed for trajectory tracking control of a bionic mechanical leg (BML) with unmeasured system states, dynamic uncertainties and external disturbances. A high gain observer (HGO) is constructed to estimate the unavailable joint velocities using the joint position feedback signals, while an adaptive fuzzy logic system (AFLS) is employed to address the lumped uncertainties. The nonlinear robust controller is then synthesized via backstepping method to improve the position tracking performance. The stability of the closed loop system is mathematically demonstrated via the Lyapunov’s stability theory. It is proven that under the proposed controller all the closed-loop signals are bounded and the trajectory tracking errors converge to a small neighborhood of the origin with appropriate design parameters. The effectiveness of the proposed control scheme is illustrated by simulation studies.

High dynamic performance of a deep-sea electric oil-filled joint actuator is an important premise to guarantee the working performance of an electric underwater manipulator. However, the unfavorable factors (i.e., extremely high water pressure, near freezing temperature) brought by the deep-sea working environment seriously affect the characteristic and dynamic performance of the electric oil-filled joint actuator, which mainly includes oil stirring viscos loss, output shaft dynamic seal loss, and core loss. In this paper, a novel observer-based robust control method named prescribed performance non-singular fast-terminal sliding-mode control (PP-NFTSMC-ESO) was synthesized for improving the dynamic performance of a deep-sea electric oil-filled joint actuator. The extended state observer (ESO) was employed to observe the unmeasured joint velocity signal and estimate the lumped uncertainties, while the prescribed performance function (PPF) was applied to constrain the instantaneous and steady-state performance of the trajectory-tracking error. The robust NFTSMC control method was then established by integrating the function of ESO and PPF through backstepping methodology. The stability of the proposed PP-NFTSMC-ESO strategy was analyzed and proved by the Lyapunov’s stability theory. It was proven that under the proposed controller, all the closed-loop signals are bounded and the trajectory tracking errors will converge to a small neighborhood of the origin with appropriate design parameters. The effectiveness of the proposed control scheme was illustrated by comparative simulation studies.

The performance of underwater crawling and adjustment of the body posture for underwater manipulating of the deep-sea crawling and swimming robot (DCSR) is directly influenced by the dynamic performance of the underwater mechanical legs (UWML), as it serves as the executive mechanism of the DCSR. Compared with the mechanical legs of legged robots working on land, the UWML of the DCSR not only possesses the characteristics of the land used mechanical legs, but is also affected by the influence of the deep-sea underwater working environment (i.e., the hydrodynamic force, viscous resistance and dynamic seal resistance). To reduce these influence, firstly, the hydrodynamic force of the UWML were researched based on theory and experiment, and the hydrodynamic model was established with the fitted hydrodynamic parameters. Secondly, the oil viscous resistance and the dynamic seal resistance were studied experimentally, and the change laws of both with respect to the joint speed and the ambient pressure (depth of operation) were obtained. The results provide a basis for the subsequent research on the structure optimization and high performance control of the UWML and DCSR.