Safety-Certified Parallel Model Predictive Control of Autonomous Surface Vehicles via Neurodynamic Optimization

This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances, state constraints, and input constraints in complex ocean environments with multiple obstacles. A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control. Specifically, an extended state observer is designed by leveraging historical and real-time data for concur-rent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart. A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints. To ensure safety, high-order discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved. A receding-horizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks. The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.
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