Large ports are seeking innovative logistical ways to improve their competitiveness world-wide. This article proposes waterborne AGVs, inspired by conventional automated guided vehicles and autonomous surface vessels, for transport over water. A predictive path following with arrival time awareness controller is proposed for such waterborne AGVs. The controller is able to achieve smooth tracking and energy efficiency with arrival time awareness for transport oriented applications. Tracking errors are conveniently formulated with vessel dynamics modeled in connected reference path coordinate systems and a coordinate transformation at switching coordinate systems. Binary decision variables and logic constraints based on an along-track state are proposed for modeling switches in the framework of Model Predictive Control (MPC) so that overshoots are avoided. Moreover, timing-aware along-track references are generated by a two-level double integrator scheme. The lower level is embedded in online MPC optimizations for smooth tracking. The higher level solves a mixed-integer quadratic programming problem considering distance-to-go and time-to-go before each MPC optimization. References over the next prediction horizon are generated being aware of the requirements on arrival time. Furthermore, successive linearizations of nonlinear vessel dynamics about a shifted previous optimal system trajectory are implemented to maintain a trade-off between computational complexity and optimality. Simulation results of two industrially relevant Inter Terminal Transport case studies illustrate the effectiveness of the proposed modeling and control design for waterborne AGVs@en

This brief proposes a distributed predictive path following controller with arrival time awareness for multiple waterborne automated guided vessels (waterborne AGVs) applied to interterminal transport (ITT). The goal is to design an efficient cooperative distributed algorithm that solves local problems in parallel and minimizes an overall objective. We model the ITT problem using waterborne AGVs with independent dynamics and objectives but coupling collision avoidance constraints. The problem is then solved by distributed model predictive control (DMPC) of which the parallelism is realized using the alternating direction method of multipliers (ADMM). Successive linearizations are utilized to maintain a tradeoff among computational complexity, optimality, and ease of decomposition. Moreover, we propose a fast ADMM by iteratively incorporating in local problems adaptive global information to improve convergence rates. Simulation results for an ITT case study illustrate the effectiveness of the proposed algorithms for DMPC of time-varying networks in general and cooperative distributed waterborne AGVs in particular.@en

Underactuated autonomous surface vehicles (ASVs) have stringent requirements on automatically tracking a predefined path. This paper proposes a model predictive control (MPC) approach based on adaptive line-of-sight (LOS) guidance for path following of ASVs. For the controller, a second-order nonlinear Nomoto model with disturbances is proposed as the vessel dynamic motion model after reviewing and comparing different ship motion models applied for path following control. For the guidance system, a novel adaptive LOS guidance with a variable acceptance circle radius is proposed to improve the precision of reference path tracking. Specifically, the acceptance circle radius is adapted with the angle between two adjacent straight segments of a reference path. Simulation experiments illustrate that the LOS guidance system with a variable acceptance circle radius results in smaller tracking errors compared with the fixed acceptance circle radius. The proposed path following method can track reference paths well even in the face of disturbances.@en

Underactuated autonomous surface vehicles (ASVs) have stringent requirements on automatically tracking a predefined path. This paper proposes a model predictive control (MPC) approach based on adaptive line-of-sight (LOS) guidance for path following of ASVs. For the controller, a second-order nonlinear Nomoto model with disturbances is proposed as the vessel dynamic motion model after reviewing and comparing different ship motion models applied for path following control. For the guidance system, a novel adaptive LOS guidance with a variable acceptance circle radius is proposed to improve the precision of reference path tracking. Specifically, the acceptance circle radius is adapted with the angle between two adjacent straight segments of a reference path. Simulation experiments illustrate that the LOS guidance system with a variable acceptance circle radius results in smaller tracking errors compared with the fixed acceptance circle radius. The proposed path following method can track reference paths well even in the face of disturbances.@en

Underactuated autonomous surface vehicles (ASVs) have stringent requirements on automatically tracking a predefined path. This paper proposes a model predictive control (MPC) approach based on adaptive line-of-sight (LOS) guidance for path following of ASVs. For the controller, a second-order nonlinear Nomoto model with disturbances is proposed as the vessel dynamic motion model after reviewing and comparing different ship motion models applied for path following control. For the guidance system, a novel adaptive LOS guidance with a variable acceptance circle radius is proposed to improve the precision of reference path tracking. Specifically, the acceptance circle radius is adapted with the angle between two adjacent straight segments of a reference path. Simulation experiments illustrate that the LOS guidance system with a variable acceptance circle radius results in smaller tracking errors compared with the fixed acceptance circle radius. The proposed path following method can track reference paths well even in the face of disturbances.@en

Underactuated autonomous surface vehicles (ASVs) have stringent requirements on automatically tracking a predefined path. This paper proposes a model predictive control (MPC) approach based on adaptive line-of-sight (LOS) guidance for path following of ASVs. For the controller, a second-order nonlinear Nomoto model with disturbances is proposed as the vessel dynamic motion model after reviewing and comparing different ship motion models applied for path following control. For the guidance system, a novel adaptive LOS guidance with a variable acceptance circle radius is proposed to improve the precision of reference path tracking. Specifically, the acceptance circle radius is adapted with the angle between two adjacent straight segments of a reference path. Simulation experiments illustrate that the LOS guidance system with a variable acceptance circle radius results in smaller tracking errors compared with the fixed acceptance circle radius. The proposed path following method can track reference paths well even in the face of disturbances.@en

We propose closed-loop energy-efficient scheduling and control of an autonomous Inter Terminal Transport (ITT) system using waterborne Autonomous Guided Vessels (waterborne AGVs). A novel pick-up and delivery problem considering safety time intervals between berthing time slots of different waterborne AGVs is proposed. Waterborne AGVs are controlled in a cooperative distributed way to carry out the assigned schedules. Real-time scheduling and control loop is closed by a partial scheduling model and an interaction model with feedback reflecting neglected lower level factors. Simulation results demonstrate the effectiveness of the proposed methodology and the potential of applying waterborne AGVs towards an autonomous ITT system.@en

Motion control in absence of human involvement is difficult to realize for autonomous vessels because there usually exist environmental disturbances and unmeasurable states at the same time. A discrete-time model predictive control (MPC) approach based on a state-compensation extended state observer (SCESO) is proposed to achieve more precise control performance with state estimations and disturbance rejections simultaneously. The main idea is that lumped disturbances encompassing nonlinear dynamics and external disturbances are handled as two parts, unlike the standard extended state observer (ESO). Particularly, the nonlinear terms are compensated by estimated states and the external disturbances are considered as extended states and attenuated by the traditional ESO strategy. Assuming that the lumped disturbances are constant over the prediction horizon, the prediction model is linearized to save computational time since after linearization the online MPC optimization problems are solved as quadratic programming problems instead of nonlinear programming problems. The convergence of the proposed SCESO estimation errors to zero is proved even when the disturbances keep variable. Two case studies involving a numerical example and ship heading control have been conducted to verify the effectiveness of the proposed control method.@en

Motion control in absence of human involvement is difficult to realize for autonomous vessels because there usually exist environmental disturbances and unmeasurable states at the same time. A discrete-time model predictive control (MPC) approach based on a state-compensation extended state observer (SCESO) is proposed to achieve more precise control performance with state estimations and disturbance rejections simultaneously. The main idea is that lumped disturbances encompassing nonlinear dynamics and external disturbances are handled as two parts, unlike the standard extended state observer (ESO). Particularly, the nonlinear terms are compensated by estimated states and the external disturbances are considered as extended states and attenuated by the traditional ESO strategy. Assuming that the lumped disturbances are constant over the prediction horizon, the prediction model is linearized to save computational time since after linearization the online MPC optimization problems are solved as quadratic programming problems instead of nonlinear programming problems. The convergence of the proposed SCESO estimation errors to zero is proved even when the disturbances keep variable. Two case studies involving a numerical example and ship heading control have been conducted to verify the effectiveness of the proposed control method.@en

The possible larger amount of container throughput and the limited handling capacities of existing infrastructures impose increasingly high pressure on large ports in improving competitiveness. Inside container terminals, land-side automated guided vehicles have been used extensively for decades to improve terminal operational efficiency and sustainability. Transport between terminals, i.e., Inter Terminal Transport (ITT), is currently mainly realized by manned trucks. However, road traffic has already been heavy in port areas with limited land. For geographically complex ports, e.g., the port of Rotterdam, travel distances by land can be much longer than by water between some terminals. Expanding the existing physical transportation infrastructure might be an option, at extremely high costs nonetheless. As an alternative, more efficient and sustainable ways for port logistics need to be investigated.@en

This paper proposes a novel cost-effective robust Model Predictive Control (RMPC) approach that can handle stochastic uncertainties with infinite support. The robust controller is developed by explicitly considering system and uncertainty properties and is applied to waterborne AGVs against environmental disturbances due to wind, waves, and currents. Specifically, probabilistic distributions are modeled and integrated in tube-based RMPC with optimized uncertainty bounds. Furthermore, successive linearizations of nonlinear system dynamics and non-convex constraints are implemented for ease of computational complexity and the robust design. Simulation results are presented to demonstrate the effectiveness of the proposed approach.@en

This paper proposes a novel cost-effective robust Model Predictive Control (RMPC) approach that can handle stochastic uncertainties with infinite support. The robust controller is developed by explicitly considering system and uncertainty properties and is applied to waterborne AGVs against environmental disturbances due to wind, waves, and currents. Specifically, probabilistic distributions are modeled and integrated in tube-based RMPC with optimized uncertainty bounds. Furthermore, successive linearizations of nonlinear system dynamics and non-convex constraints are implemented for ease of computational complexity and the robust design. Simulation results are presented to demonstrate the effectiveness of the proposed approach.@en

Waterborne autonomous guided vessels (waterborne AGVs) moving over open waters experience environmental uncertainties. This paper proposes a novel cost-effective robust distributed control approach for waterborne AGVs. The overall system is uncertain and has independent subsystem dynamics but coupling objectives and state constraints. Waterborne AGVs determine their actions in a parallel way, while still minimizing an overall cost function and respecting coupling constraints robustly by communicating within a neighborhood. Our first contribution is the proposal of the system robustness level for the costeffective robust distributed model predictive control (RDMPC) for waterborne AGVs. Cost-effective RDMPC models the price of robustness by explicitly considering uncertainty and system characteristics in a tube-based robust control framework. The second contribution is an efficient integrated branch & bound (B&B) and the alternating direction method of multipliers (ADMMs) algorithm for solving the cost-effective RDMPC problem. The algorithm exploits special ordered variable sets and combining branching criteria with intermediate ADMM results conducting smart search in B&B. Simulation results demonstrate the effectiveness of the proposed approach for cooperative distributed waterborne AGVs with cost-effective robustness.@en