Dynamic Positioning of Vessels Using a UKF-Based Observer and an NMPC-Based Controller

Jayasiri, Awantha; Nandan, Anirudh; Imtiaz, Syed; Spencer, Don; Islam, Mohammed; Ahmed, Salim

doi:10.1109/tase.2017.2698923

Cited by 32 publications

(18 citation statements)

References 12 publications

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“…The x coordinate of Ship A's midship is 20, and the y coordinates of its starboard side is 3. Therefore, to ensure the safe distance, the reference points for path planning are set as (20,11), (20,15) and (20,19) accordingly in Cases 5, 6 and 7. The collision avoidance task is to ensure that the ship's y coordinates should be larger than 11, 15 and 19 when it passes x = 20 in Cases 5-7.…”

Section: A Scenario 1: Without Collision Avoidancementioning

confidence: 99%

Automatic Docking for Underactuated Ships Based on Multi-Objective Nonlinear Model Predictive Control

Liu

Negenborn

et al. 2020

IEEE Access

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Autonomous shipping refers to the ability of a ship to independently control its own actions while transporting cargo from one port to another, which places higher requirements on ship motion control methods. When a ship enters a port, it is important to ensure that the ship sails from the fairway area to the assigned position at the berth with a desirable speed and that it finally stops at the desired position. Ship docking is known as one of the most challenging tasks due to the non-linearity of low-speed ship movements and the high requirements on collision avoidance with the quayside. This paper proposes a nonlinear model predictive control (NMPC)-based approach for underactuated ships, providing optimal ship rudder angles and propeller revolution rate to automate the ship docking process. At each sampling instant, a finite horizon optimal control problem is formulated based on a nonlinear ship maneuverability model. A lexicographic multi-objective optimization strategy is proposed in the design of the NMPC controller, saving the efforts on control parameters tuning. Simulation experiments are carried out to evaluate the effectiveness of the proposed approach. INDEX TERMS Automatic docking, motion control, nonlinear model predictive control.

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Section: A Scenario 1: Without Collision Avoidancementioning

confidence: 99%

Automatic Docking for Underactuated Ships Based on Multi-Objective Nonlinear Model Predictive Control

Liu

Negenborn

et al. 2020

IEEE Access

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“…In the positioning and tracking system, the Bayesian filter is employed to fuse sensory data from all different sources to gain better positioning accuracy. Among Bayesian filtering methods, the Kalman filter (KF) and its variants, such as unscented Kalman filter (UKF), have been widely used for the navigation systems, since they are efficient in terms of the computational cost while providing a high accuracy in localization [ 9 , 28 ]. Recently, many indoor positioning approaches that fuse the user’s motion estimated by DR method and the location data obtained by RSS fingerprinting through PF have been proposed to achieve higher positioning accuracy than KF and its variants in indoor environments [ 12 , 29 ].…”

Section: Related Workmentioning

confidence: 99%

“…To achieve better indoor localization results, many smartphone-based localization approaches that integrate both DR and fingerprinting method through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed [ 7 , 8 , 9 ]. Among Bayesian filters, PF is the most popular integrating scheme and can provide the best localization performance.…”

Section: Introductionmentioning

confidence: 99%

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter

Sung

Lee

Kim

2018

Sensors

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The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user’s motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency.

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“…To achieve higher path-following performance, sliding mode, reinforcement learning, and object recognition approaches have been extensively studied. 3,[18][19][20][21] Regarding the problem of system nonparametric uncertainties, it is a common phenomenon in an industrial control environment. 22 Fortunately, universal function approximator was developed to estimate the unknown system dynamics caused by nonparametric uncertainties via nueral networks (NNs) or fuzzy systems, owing to their learning and adaptation abilities.…”

Section: Introductionmentioning

confidence: 99%

Finite-time predictor line-of-sight–based adaptive neural network path following for unmanned surface vessels with unknown dynamics and input saturation

Guo

2018

International Journal of Advanced Robotic Systems

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In the presence of unknown dynamics and input saturation, a finite-time predictor line-of-sight-based adaptive neural network scheme is presented for the path following of unmanned surface vessels. The proposed scheme merges with the guidance and the control subsystem of unmanned surface vessels together. A finite-time predictor-based line-of-sight guidance law is developed to ensure unmanned surface vessels effectively converging to and following the referenced path. Then, the pathfollowing control laws are designed by combining neural network-based minimal learning parameter technique with backstepping method, where minimal learning parameter is applied to account for system nonparametric uncertainties. The key features of this scheme, first, the finite-time predictor errors are guaranteed; second, designed controllers are independent of the system model; and third, only required two parameters update online for each control law. The rigorous theory analysis verifies that all signals in the path-following guidance-control system are semi-globally uniformly ultimately bounded via Lyapunov stability theory. Simulation results illustrate the effectiveness and performance of the presented scheme.

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Dynamic Positioning of Vessels Using a UKF-Based Observer and an NMPC-Based Controller

Cited by 32 publications

References 12 publications

Automatic Docking for Underactuated Ships Based on Multi-Objective Nonlinear Model Predictive Control

Automatic Docking for Underactuated Ships Based on Multi-Objective Nonlinear Model Predictive Control

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter

Finite-time predictor line-of-sight–based adaptive neural network path following for unmanned surface vessels with unknown dynamics and input saturation

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