Vahid Hassani

Multiple model adaptive wave filtering for dynamic positioning of marine vessels

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. Its key contribution is twofold: i) it introduces an improved model for filter design, and ii) and it exploits the structure of a multiple model adaptive wave filter that relies on measurements of the vessels position and heading only. Namely, an improvement in the control plant model is proposed that better captures the physics of the problem at hand and a bank of Kalman filters is designed for a finite number of parameter values, each corresponding to a different peak frequency of the assumed wave spectrum model. Tools from multiple model adaptive estimation (MMAE) theory are exploited to blend the information provided by the different observers, yielding position and velocity estimates of the marine vessel. These estimates are then to be used in an appropriately designed feedback control law. Simulations illustrate the efficacy of the MMAE techniques proposed and the improvement in performance that is obtained when compared with other approaches.

Stability Analysis of Robust Multiple Model Adaptive Control

Abstract The Robust Multiple Model Adaptive Control (RMMAC) methodology was first introduced in Fekri et al. [2006] for open-loop stable plants with parametric uncertainty and unmodeled dynamics subjected to external disturbances and measurement noise. This paper addresses the stability of RMMAC systems. We show, using concepts and analysis tools that borrow from Supervisory Control, that all closed-loop signals in a RMMAC system are bounded. It is further shown that robust performance is recovered in steady state.

Multiple Model Adaptive Estimation and model identification usign a Minimum Energy criterion

This paper addresses the problem of Multiple Model Adaptive Estimation (MMAE) for discrete-time, linear, time-invariant MIMO plants with parameter uncertainty and unmodeled dynamics. Model identification is analyzed in a deterministic setting by adopting a Minimum Energy selection criterion. The MMAE system relies on a finite number of local observers, each designed using a selected model (SM) from the original set of possibly infinite plant models. Results akin to those previously obtained in a stochastic setting are derived in a far simpler manner, in a deterministic framework. We show, under suitable distinguishability conditions, that the SM identified is the one that corresponds to the observer with smallest output prediction error energy. We also develop a procedure to analyze the behavior of MMAE when the true plant is not one of the SMs. This leads to an algorithm that computes, for each SM, the set of equivalently identified plants, that is, the set of plants that will be identified as that particular SM. The impact of unmodeled dynamics on model identification is discussed. Simulation results with a model of a motor coupled to a load via an elastic shaft illustrate the performance of the methodology derived.

A novel methodology for robust dynamic positioning of marine vessels: Theory and experiments

The paper describes a novel robust adaptive controller for Dynamic Positioning (DP) of marine vessels. The proposed Robust Multiple Model Adaptive Dynamic Positioning (RMMADP) structure consists of a bank of robust controllers designed using the Mixed-μ methodology and an identification unit. The latter is composed by a bank of (steady-state) Kalman filters (KFs) that generate online the output estimation errors (residuals) that are used to generate appropriate monitoring signals. At each sampling time, the monitoring signals are assessed to decide which controller should be selected from the bank of the controllers. The proposed adaptive structure of the RMMADP enables the DP system to operate in different operational conditions and hence, it is a step forward to a so-called all-year marine DP system. Numerical simulations, carried out with a high fidelity nonlinear DP simulator, illustrate the efficacy of the RMMADP techniques proposed. To bridge the gap between theory and practice, 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.

A novel methodology for adaptive Wave Filtering of marine vessels: Theory and experiments?

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 vessels dynamic model adopted captures the sea state as an uncertain parameter. The proposed adaptive Wave Filtering (WF) consists of a recursive optimization procedure which seeks to identify the dominant wave frequency (the uncertain parameter) by minimizing an appropriate defined performance index. The estimated dominant wave frequency is used to identify the sea condition, based on which adaptive wave filtering (using a Kalman filter) is performed for dynamic positioning purposes. The adaptive WF enables the DP system to operate in different operational conditions and hence, it is a step forward to a so-called all-year marine DP 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.

Robust Dynamic Positioning of Offshore Vessels using Mixed-μ Synthesis Part II: Simulation and Experimental Results

Abstract This paper is a follow-up of a companion paper by Hassani et al. (2012b) on a control design methodology for Dynamic Positioning (DP) of marine vessels and offshore rigs subjected to the influence of sea waves, currents, and wind loads using mixed-μ synthesis. The present paper describes the results of a design exercise in which robust controllers were designed for a representative vessel. Its main focus is on the discussion of the results of numerical simulations and experimental model-testing of a set of robust DP controllers operating under different sea conditions: calm, moderate, high, and extreme seas. The robust DP controllers were first evaluated in a high fidelity nonlinear DP simulator, illustrating the efficiency of the design. To bridge the gap between theory and practice, the results were experimentally verified by model testing of a DP operated ship, the Cybership III, under different simulated sea conditions in a towing tank equipped with a hydraulic wave maker.

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

Abstract 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.

A Multiple Model Adaptive Wave Filter for Dynamic Ship Positioning

Abstract This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships subjected to the influence of sea waves. Its key contribution is the use of a multiple model adaptive wave filter that relies on measurements of the ships position and heading only. To this effect, a bank of Kalman filters is designed for a finite number of parameter values, each corresponding to a different peak frequency of the assumed wave spectrum model. Tools from multiple model adaptive estimation (MMAE) theory are exploited to blend the information provided by the different observers, yielding position and velocity estimates of the ship. These estimates are then be used in an appropriately designed feedback control law. Simulations illustrate the efficacy of the MMAE techniques proposed.

Wave Filtering and Dynamic Positioning of Marine Vessels Using a Linear Design Model: Theory and Experiments

This chapter describes a procedure to obtain an improved design model of ships subjected to the influence of currents and sea waves. The model structure is at the heart of the application of new techniques in control and estimation theory to the problem of Dynamic Positioning (DP) and wave filtering of marine vessels. The model proposed captures the physics of the problem at hand in an effective manner and includes the sea state as an uncertain parameter. This allows for the design of advanced control and estimation algorithms to solve the DP and wave filtering problems under different sea conditions. Numerical simulations, carried out using a high fidelity nonlinear DP system simulator, illustrate the performance improvement in wave filtering as a result of the use of the proposed model. Furthermore, using Monte-Carlo simulations the performance of three DP controllers, designed based on the plant model developed, is evaluated for different sea conditions. The first controller is a nonlinear multivariable PID controller with a passive observer. The second controller is of the Linear Quadratic Gaussian type and the third controller builds on \(\mathcal{H}_{\infty }\) control techniques using the mixed-μ synthesis methodology. The theoretical results are experimentally verified and the performance of wave filtering in DP systems operated with the controllers designed for different sea conditions are further examined by model testing a DP operated ship, the Cybership III, in a towing tank equipped with a hydraulic wavemaker.

Evaluation of three dynamic ship positioning controllers: From calm to extreme conditions

Abstract Using Monte-Carlo simulations and experimental model tests, the performance of three controllers designed for dynamic positioning (DP) of marine vessels and offshore rigs subjected to the influence of sea waves, currents, and wind loads is compared for different sea conditions: calm, moderate, high, and extreme seas. The first controller is a nonlinear multivariable PID with a passive observer. The second controller is of the Linear Quadratic Gaussian (LQG) type and the third controller builds on ℋ ∞ control techniques using the mixed-μ synthesis methodology. We further examine the performance of wave filtering of the controllers operating in different sea conditions. The Monte-Carlo simulations highlight the strength of each method as well as its shortcomings and drawbacks.