Physics

Control engineering

Nonlinear system

Mathematics

Mathematical optimization

Engineering

Artificial intelligence

Control (management)

Computer science

Quantum mechanics

Quadratic programming

Control theory (sociology)

Lyapunov function

IEEE Transactions on Intelligent Vehicles

This paper considers cooperative control of interconnected intelligent surface vehicles (ISV) moving in a complex water surface containing multiple static/dynamic obstacles. Each ISV is subject to control force and moment constraints, in addition to internal model uncertainties and external disturbances induced by wind, waves and currents. A general safety-certified cooperative control architecture capable of achieving various collective behaviors such as consensus, containment, enclosing, and flocking, is proposed. Specifically, a distributed motion generator is used to generate desired reference signals for each ISV. Robust-exact-differentiators-based (RED-based) extended state observers (ESOs) are designed for recovering unknown total disturbances in finite time. With the aid of control Lyapunov functions, input-to-state safe high order control barrier functions and RED-based ESOs, constrained quadratic optimization problems are formulated to generate optimal surge force and yaw moment without violating the input, stability, safety constraints. In order to facilitate real-time implementations, a one-layer recurrent neural network is employed to solve the constrained quadratic optimization problem on board. It is proved that all tracking errors of the closed-loop system are uniformly ultimately bounded and the multi-ISV system is input-to-state safe. An example is given to substantiate the effectiveness of the proposed general safety-certified cooperative control architecture.

A General Safety-Certified Cooperative Control Architecture for Interconnected Intelligent Surface Vehicles With Applications to Vessel Train