A Dynamic Positioning System Based on Kalman Filtering and Optimal Control

Balchen, Jens G.; Jenssen, N.A.; Mathisen, Eldar; Sælid, Steinar

doi:10.4173/mic.1980.3.1

Cited by 228 publications

(118 citation statements)

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“…Hence, from a practical point of view, wave filtering has the potential to reduce wear of mechanical equipment, such as thrusters and engines, together with reducing the fuel consumption and emissions from the vessel's engines. Observer-based wave filtering was first introduced by Balchen et al [2] and later extended by Saelid et al [3] utilizing the vessel model and the Extended Kalman Filter (EKF) algorithm. This wave-filtering technique makes use of the knowledge of the encounter frequency of the waves, that is the peak frequency of the wave spectra shifted due to the speed of the vessel, to separate the LF and WF motion.…”

Section: Introductionmentioning

confidence: 99%

Design of inertial navigation systems for marine craft with adaptive wave filtering aided by triple‐redundant sensor packages

Bryne

Fossen

Johansen

2015

Adaptive Control & Signal

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SUMMARYMarine craft feedback control systems typically require estimates of position, velocity and heading where the wave-induced motions should be suppressed. This paper presents a strapdown inertial navigation system with adaptive wave filtering. Wave filtering based on inertial navigation systems differ from previous vesselmodel-based designs which require knowledge of vessel parameters and mathematical models for estimation of thruster and wind forces and moments based on auxiliary sensors. The origin of the inertial navigation system error states is proven to be uniformly semiglobally exponentially stable (USGES). The wave-filtering scheme uses the estimated states of the inertial navigation system to separate the low-frequency motion of the craft from the wave-frequency motions. The observer structure also allows for estimation of the timevarying encounter frequency by using a signal-based frequency tracker or an adaptive observer. Finally, the structural properties of the triple-redundant sensors packages have been utilized to obtain optimal and robust sensor fusion with respect to sensor performance and faults.

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Section: Introductionmentioning

confidence: 99%

Design of inertial navigation systems for marine craft with adaptive wave filtering aided by triple‐redundant sensor packages

Bryne

Fossen

Johansen

2015

Adaptive Control & Signal

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“…In 1976, the first Norwegian DP paper was presented at the IFAC/IFIP Symposium on Automation in Offshore Oil Field Operations in Bergen, Norway (Balchen et al, 1976). This paper is probably also the first to investigate Kalman filtering for dynamic positioning purposes.…”

Section: Albatross Dpmentioning

confidence: 99%

“…In the first volume of MIC, a paper detailing the cybernetic algorithms behind the Albatross DP was published (Balchen et al, 1980). The paper starts with a rather technology-optimistic explanation for KV's DP initiative: "The known disadvantages of conventional DPsystems led the mechanical engineering and data technology company Kongsberg Våpenfabrik A/S of Norway to initiate the development of a DP-system based on the concept of modern control theory, such as Kalman filtering and optimal control.…”

Section: A Model-based Dp Systemmentioning

confidence: 99%

From Eureka to K-Pos: Dynamic Positioning as a Highly Successful and Important Marine Control Technology

Breivik¹,

Kvaal

Østby

2015

IFAC-PapersOnLine

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Dynamic positioning (DP) started out as an exotic control technology for geological core sampling in deep waters in the early 1960s. The technology gradually became more advanced during the 1970s, especially after it was introduced and applied in the offshore petroleum industry. Several British, French and US companies delivered DP systems during this period, and DP controllers were typically based on the PID principle. In 1977, the first DP system applying modern control theory in the form of a Kalman filter was delivered. This system was a collaborative effort between key companies, research institutes and universities in Norway, motivated by the discovery of oil and gas on the Norwegian continental shelf in 1969. A small group called Albatross had been established as part of the state-owned "modernization locomotive" Kongsberg Våpenfabrikk to industrialize and deliver the new DP system. Albatross DP rapidly gained popularity, and in 1980 all DP systems in the world were delivered by this small group situated in the small Norwegian inland town of Kongsberg. Since then, what has later become known as the Kongsberg DP has been able to maintain a majority share of an increasingly large DP market. Today, dynamic positioning technology has developed from scientific to industrial to massmarket applications, and has been a cornerstone of the offshore petroleum industry for more than four decades. This paper will present DP as an example of a highly successful and important marine control technology, and use the Kongsberg DP as a case study to illustrate key factors required to achieve commercial success for control applications. The paper is based on the book "The Jewel in the Crown: Kongsberg Dynamic Positioning Systems 1975 -2015", which was released in June 2015.

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“…However, notch filters restrict the performance of closedloop systems because they introduce phase lag around the crossover frequency, which in turn tends to decrease phase margin. An improvement in performance was achieved by exploiting more advanced control techniques based on optimal control and Kalman filter (KF) theory, see Balchen et al (1976). These techniques were later modified and extended in Saelid et al (1983); Sørensen et al (1996); Grøvlen and Fossen (1996); Torsetnes et al (2004); Nguyen et al (2007); Hassani et al (2012), and Hassani et al (2013c).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Wave Filtering for Dynamic Positioning of Marine Vessels using Maximum Likelihood Identification: Theory and Experiments

Hassani

Sørensen

Pascoal

2013

IFAC Proceedings Volumes

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This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships and offshore rigs subjected to the influence of sea waves. The dynamic model of the vessel captures explicitly the sea state as an uncertain parameter. The proposed adaptive wave filter borrows from maximum likelihood identification techniques. The general form of the logarithmic likelihood function is derived and the dominant wave frequency (the uncertain parameter) is identified by maximizing this function. To this effect, a bank of Kalman filters is used to evaluate the log-likelihood function for different values of the uncertain parameter. After each identification step a new set of Kalman filters is designed to estimate the dominant wave frequency with better accuracy. The proposed sea state identification technique enables adaptive Wave Filtering (WF) and Dynamic Positioning (DP) systems to operate in different operational conditions and hence, it is a step forward to the development of a so-called all-year marine estimation and control system. The results are experimentally verified by model testing a DP operated ship, the Cybership III, under different sea conditions in a towing tank equipped with a hydraulic wave maker.

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A Dynamic Positioning System Based on Kalman Filtering and Optimal Control

Cited by 228 publications

References 1 publication

Design of inertial navigation systems for marine craft with adaptive wave filtering aided by triple‐redundant sensor packages

Design of inertial navigation systems for marine craft with adaptive wave filtering aided by triple‐redundant sensor packages

From Eureka to K-Pos: Dynamic Positioning as a Highly Successful and Important Marine Control Technology

Adaptive Wave Filtering for Dynamic Positioning of Marine Vessels using Maximum Likelihood Identification: Theory and Experiments

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