Complementary Constrained Model Predictive Depth Control of a Hybridly-Actuated Submarine Drifter

The submarine drifter is a novel Lagrangian-based observation platform to explore the ocean, but its precise and rapid depth control system design is still an open issue. The major challenge would be the complex hybrid actuation system, which contains anisotropic characteristics and switching issues. In this paper, we proposed an modified complementary constrained model predictive control (MCC-MPC) scheme to meet the metrics. The scheme reformulates complex drifter and hybrid actuation system dynamics into a solvable system with complementary constraints. The nonlinear component inside the system is approximated by applying a sgn-sigmoid approximation function for the sake of linearization and computation. Then the customized online optimizer predicts the system dynamics with complementary constraints and computes the optimal control outputs in the finite horizon in an iterative loop. The validation results prove that the proposed controller can effectively control the submarine drifter to achieve the desired depth and the key metrics are 10×, 4×, and 2× better than conventional PID control, disturbance observer-based control, and conventional MPC methods, respectively.