Jonas Linder

A closed-loop instrumental variable approach to mass and center of mass estimation using IMU data

In this paper, an instrumental variable (IV) method for estimating the mass and center of mass (CM) of a ship using IMU data has been further investigated. Here, this IV method, which was proposed in an earlier paper, has been analyzed from a closed-loop point of view. This new perspective reveals the properties of the system and dependencies of the signals used in the estimation procedure. Due to similarities with closed-loop identification, previous results in the closed-loop identification field have been used as an inspiration to improve the IV estimator. Since the roll dynamics of a ship is well described by a pendulum model, a pendulum experiment has been carried out to validate the performance both of the original and the improved IV estimators. The experiments gave good results for the improved IV estimator with significantly lower variances and relative errors than the previous IV estimator.

Identifiability of physical parameters in systems with limited sensors

In this paper, a method for estimating physical parameters using limited sensors is investigated. As a case study, measurements from an IMU are used for estimating the change in mass and the change in center of mass of a ship. The roll motion is studied and an instrumental variable method estimating the parameters of a transfer function from the tangential acceleration to the angular velocity is presented. It is shown that only a subset of the unknown parameters are identifiable simultaneously. A multi-stage identification approach is presented as a remedy for this. A limited simulation study is also presented to show the properties of the estimator. This shows that the method is indeed promising but that more work is needed to reduce the variance of the estimator.

Mass estimation of a quadcopter using IMU data

In this paper, an approach to estimate the mass of a quadcopter using only inertial measurements and pilot commands is presented. For this purpose, a lateral dynamic model describing the relation between the roll rate and the lateral acceleration is formulated. Due to the quadcopters inherent instability, a controller is used to stabilize the system and the data is collected in closed loop. Under the effect of feedback and disturbances, the inertial measurements used as input and output are correlated with the disturbances, which complicates the parameter estimation. The parameters of the model are estimated using several methods. The simulation and experimental results show that the instrumental-variable method has the best potential to estimate the mass of the quadcopter in this setup.

Identification of systems with unknown inputs using indirect input measurements

ABSTRACT A common issue with many system identification problems is that the true input to the system is unknown. This paper extends a previously presented indirect modelling framework that deals with identification of systems where the input is partially or fully unknown. In this framework, unknown inputs are eliminated by using additional measurements that directly or indirectly contain information about the unknown inputs. The resulting indirect predictor model is only dependent on known and measured signals and can be used to estimate the desired dynamics or properties. Since the input of the indirect model contains both known inputs and measurements that could all be correlated with the same disturbances as the output, estimation of the indirect model has similar challenges as a closed-loop estimation problem. In fact, due to the generality of the indirect modelling framework, it unifies a number of already existing system identification problems that are contained as special cases. For completeness, the paper is concluded with one method that can be used to estimate the indirect model as well as an experimental verification to show the applicability of the framework.

Vertical modeling of a quadcopter for mass estimation and diagnosis purposes

In this work, we estimate a model of the vertical dynamics of a quadcopter and explain how this model can be used for mass estimation and diagnosis of system changes. First, a standard thrust model describing the relation between the calculated control signals of the rotors and the thrust that is commonly used in literature is estimated. The estimation results are compared to those using a refined thrust model and it turns out that the refined model gives a significant improvement. The combination of a nonlinear model and closed-loop data poses some challenges and it is shown that an instrumental variables approach can be used to obtain accurate estimates. Furthermore, we show that the refined model opens up for fault detection of the quadcopter. More specifically, this model can be used for mass estimation and also for diagnosis of other parameters that might vary between and during missions.

Interactive Multiobjective Optimization for a Grab-Shift Unloader Crane

Abstract In this paper, multiobjective optimization (MOO) is applied to an optimal control problem for a grab-shift unloader crane. The crane is modeled as a cart-pendulum system with varying rope length and the trajectory of the grab is limited by the ship, the quay, and the crane structure. The objectives to minimize are chosen as time, energy and maximal instantaneous power. The optimal control problem is solved using a direct simultaneous optimal control method. The study shows that MOO can be an efficient tool when choosing a good compromise between conflicting objectives such as time and energy. Furthermore, navigation among the Pareto optimal solutions has proven to be very useful when a user wants to learn how the control variables interact with the process.

Interactive multiobjective optimization for the hot rolling process

In this paper, multi-objective optimization is applied to the hot rolling process. It is modeled mostly using first principle models considering, for instance, the mass balance (or mass flow rate), the tensions in the material, the power requirements, the thermal field, and the microstructure of the material.