Safety-Critical Automated Surface Vessels MIMO Control With Adaptive Control Barrier Functions Under Model Uncertainties

Ensuring the side-by-side configuration of the automated surface vessels under the effect of the uncertainties of the environment attracted continued efforts on this challenging issue. This paper investigates the learning-based safety-enhanced adaptive side-by-side control algorithm that suitably defines and uses the modified adaptive control barrier functions to tackle the constrained control satisfied under the uncertainty environment by ensuring the control barriers on state-variables constraints. In addition to the inherent adaptivity of the adaptive control barrier functions, the finite-time auxiliary system is enabled to modify the adaptive control barrier functions, which considers the unknown part of the nominal model to ensure the satisfaction of system constraints, especially under uncertain situations. For the formulated quadratic programs with adaptive control barrier functions and control Lyapunov functions, the operator splitting quadratic program is employed in problem-solving, which effectively enlarges the robustness of problem-solving, making it particularly efficient and reliable for real-time applications. The effectiveness of the proposed method is demonstrated via comparative simulations under uncertain cases to show the superior adaptive ability under the model uncertainties, which can be employed in marine industry applications, e.g., carbon emissions estimation modeling and optimization.
Note to Practitioners
—This paper was motivated by enlarging the adaptive ability of optimization-based safe operation for safety-critical automated surface vessels control under the side-by-side configuration under the effect of the uncertainty of the environment. The proposed learning-based safety-enhanced adaptive side-by-side control algorithm promotes adaptivity and control performance with the usage of the finite-time auxiliary system to the refined adaptive control barrier functions in the optimization problem formulation. The problem-solving is accelerated with the usage of the operator-splitting quadratic program. These implements effectively enlarge the robustness of the proposed method, making it particularly efficient and reliable for industrial real-time applications. The proposed method employed a complicated system design with the outcome of a simple algorithm implemented with promoted adaptivity and robustness, which can be employed in industrial applications in future research to promote current control system performance.