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Depth estimation problem for the perspective vision systems has been extensively studied in the literature and the depth estimation can be achieved under the assumption that the exact camera motion parameters are available. However, in practice, it is hard to obtain the camera motion parameters exactly. For instance, the exact camera velocity is always unavailable and instead only the roughly estimated one can be obtained. This introduces the significant difficulties to achieve the depth estimation with partially uncertain camera motion parameters. In this article, we consider the depth estimation problem for the perspective vision system in the case that the camera angular velocities are accurate, while partial camera linear velocities are contaminated by some disturbances. This problem is theoretically formulated and solved for the first time in this article by proposing a new depth observer in the presence of the partially uncertain camera linear velocities. Local exponential convergence of the depth and disturbance estimates is achieved in presence of the partially uncertain camera linear velocities such that the estimation errors of states and disturbances converge to zero for constant disturbances or to a small error bound for bound time-varying disturbances. Simulations and experiments are carried out to verify the performance of the proposed observer.

The high-precision alignment of spacecrafts subject to multiple composite disturbances [e.g., axle frictions and back electromotive force (EMF)] and mandatory pointing constraints is an extremely challenging issue for both theoretically and practically. To address this challenge, a refined deep coupled alignment control scheme is proposed in this article. First, a deep coupled alignment model of spacecraft based on the dynamics of the reaction wheel actuator is established, which reveals the influence of the composite disturbances, such as the axle frictions and the back EMF on the system, and separates the composite disturbances into mismatched and matched disturbance according to the affected channels. Second, in view of the characteristics of the composite disturbances, heterogeneous sliding mode disturbance observers (SMDOs) are designed to estimate them accordingly. Then, in order to cope with the mandatory pointing constrains, barrier functions are introduced to convert the alignment errors to limit their variation range. Finally, combined with heterogeneous SMDOs and conversion errors, a composite refined controller is constructed to finely compensate the composite disturbances in the alignment system. High-precision control performance is achieved and the mandatory pointing requirement is satisfied. Simulations and experiments verify the effectiveness of the proposed scheme.

Underwater polarization (POL) fields contain spatial information about the Sun. It offers underwater POL fields the capability of navigation and positioning. Benefiting from the advantages of autonomy, the POL positioning is an effective complement for the global navigation satellite system (GNSS)-denied underwater environment. The current POL-based positioning strategies always exploit the Sun's position directly. However, the solution of geolocalization is susceptible to the constant deviations in Sun measurement. In an effort to address this issue, a two-step positioning model is designed. The Sun's positions over time are calculated by exploiting the POL field inside Snell's window, in which the underwater disturbance can be shielded. Subsequently, the first step in the two-step model adopts time-sequence Suns for coarse positioning. Around the coarse positioning solution, a local region is determined as the precondition for the second step positioning. A time differential Sun (TDS) positioning strategy is proposed for a refined geolocation. TDS exploits the Sun's dynamic movement for positioning, and thereby the position errors arising from the constant deviations are eliminated. An underwater POL positioning system is developed. The experimental results demonstrate that the positioning accuracy is improved distinctly by the TDS strategy.

We propose a reliable state feedback control method for uncertain linear systems with a terminal reliability constraint. By modeling the uncertainties from system parameters and disturbances with multiple probabilistic intervals whose probabilities sum to 1, multiple corresponding probabilistic intervals of control responses are approximately estimated through the system dynamics as the uncertainty propagation bridge. This not only mitigates the need for large sample data required by probabilistic methods for robust control, but also avoids the large error caused by the first-order Taylor expansion required by nonprobabilistic methods in the case of large radiuses of uncertain parameters. Considering the different characteristics of transient and steady-state phases, a prescribed performance threshold function is established for the concerned control response. Based on probabilistic intervals and threshold, the failure rate model and reliability model for the concerned control response are formulated. Further, an optimization problem for the reliable state feedback control with the terminal reliability constraint is formulated and solved by the simulated annealing algorithm. An illustrative example is presented to demonstrate the feasibility and effectiveness of the reliable state feedback control.

High-precision and safety control in face of disturbances and uncertainties is a challenging issue of both theoretical and practical importance. In this article, new adaptive anti-disturbance control schemes are proposed for a class of uncertain nonlinear systems with composite disturbances, including additive disturbances, multiplicative actuator faults, and implicit disturbances deeply coupled with system states. Both the cases with known and unknown control/fault directions are investigated. By properly fusing the techniques of disturbance observers and adaptive compensation, it is shown that all closed-loop signals are globally uniformly bounded and the tracking error converges to zero asymptotically, no matter the control/fault directions are known or not. In the case of known directions, the proposed control scheme, for the first time, guarantees asymptotic tracking and ${L}_{\infty }$ tracking performance simultaneously in face of disturbances and actuator faults. Moreover, novel Nussbaum functions and a contradiction argument are introduced, which allow the system to have multiple unknown nonidentical control directions and unknown time-varying fault direction. Simulation results illustrate the effectiveness of the proposed control schemes.