Publisher: Norwegian Society of Automatic Control   (Total: 1 journals)   [Sort alphabetically]

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Modeling, Identification and Control     Open Access   (SJR: 0.234, CiteScore: 1)
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Modeling, Identification and Control
Journal Prestige (SJR): 0.234
Citation Impact (citeScore): 1
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 0332-7353 - ISSN (Online) 1890-1328
Published by Norwegian Society of Automatic Control Homepage  [1 journal]
  • Microevolutionary system identification and climate response predictions

    • Authors: Ergon; Rolf;
      Pages: 91 - 99
      Abstract: Microevolutionary system identification was introduced Ergon (2022), with the specific purpose to show that predictions of genetic adaptations to climate change require that environmental reference values are properly defined. The theoretical development was then limited to single-input single-output (SISO) systems, and the simulations used a toy example with spring temperature as input and mean breeding date as output. Generations were assumed to be non-overlapping. Here, the theory is extended to cover multiple-input multiple-output (MIMO) systems, while the simulation example uses two environmental inputs (spring temperature and rainfall) and two adaptive phenotypic outputs (breeding date and breeding habitat). These extended simulations reveal difficulties involved in predictions of genetic adaptations for complex systems based on short data, where the reference environment values are not included.
      PubDate: August 2022
      Issue No: Vol. 43, No. 3 (2022)
  • Parameter Estimation for a Gas Lifting Oil Well Model Using Bayes' Rule
           and the Metropolis–Hastings Algorithm

    • Authors: Ban; Zhe; Ghaderi, Ali; Janatian, Nima; Pfeiffer, Carlos F.;
      Pages: 39 - 53
      Abstract: Oil well models are frequently used in the oil production process. Estimation of unknown parameters of these models has long been a question of great interest in the oil industry field. Data collected from an oil well system can be useful for identifying and characterizing the parameters in the corresponding model. In this article, we present a solution to estimate the parameters and uncertainty of a gas lifting oil well model by designing Bayesian inference and using the Metropolis-Hastings algorithm. To present and evaluate the estimation, the performance of the chains and the distributions of the parameters were shown, followed by posterior predictive distributions and sensitivity analysis. Compared with the conventional maximum likelihood estimation methods that tried to identify one optimum value for each parameter, more information of the parameters is obtained by using the proposed model. The insights gained from this study can benefit the optimization and advanced control for the oil well operation.
      PubDate: May 2022
      Issue No: Vol. 43, No. 2 (2022)
  • Collision avoidance for ASVs through trajectory planning: MPC with
           COLREGs-compliant nonlinear constraints

    • Authors: Thyri; Emil H.; Breivik, Morten;
      Pages: 55 - 77
      Abstract: This article presents a trajectory planning method for autonomous surface vessels that is compliant with Rule 8 and rules 13-17 from the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). The method is suitable for operation in restricted waters, where it both handles collision avoidance with static obstacles, and also considers the available room to maneuver when determining the appropriate safe distance to other vessels. The trajectory planner is formulated as a finite-horizon nonlinear model predictive controller, minimizing the deviation from a reference trajectory and the acceleration. Collision avoidance with static obstacles is included through the use of convex free sets. Collision avoidance with other traffic is done by assigning so-called target ship domains to each vessel, and formulating constraints for that domain. COLREGs rules 13-15 and 17 are included by first classifying each vessel-to-vessel encounter to find which rule applies, and subsequently assigning an encounter-specific domain to the opposing vessel. The domain is designed so that if the trajectory does not violate the domain, compliance with COLREGs rules 13-15 and partial compliance with Rule 17 is ensured. Furthermore, compliance with COLREGs Rule 8 and Rule 16 is included through a novel method for calculating the objective function cost-gains. % in the planning horizon. By constructing windows of reduced tracking error and acceleration cost, the start time, duration and magnitude of a maneuver can be controlled, and hence readily apparent maneuvers made in ample time can be facilitated. The method's effectiveness and its completeness in terms of COLREGs compliance is demonstrated through an extensive set of simulations of vessel-to-vessel encounters in open waters. Furthermore, the robustness of the method is demonstrated through a set of complex simulations in confined areas with several maneuvering vessels. In all simulations, the method demonstrates compliance with COLREGs Rule 8 and rules 13-17.
      PubDate: August 2022
      Issue No: Vol. 43, No. 2 (2022)
  • Dynamical Pose Estimation with Graduated Non-Convexity for Outlier

    • Authors: Smith; Torbjørn; Egeland, Olav;
      Pages: 79 - 89
      Abstract: In this paper we develop a method for relative pose estimation for two sets of corresponding geometric primitives in 3D with a significant outlier fraction. This is done by using dynamical pose estimation as a solver in registration problems formulated with graduated non-convexity for truncated least squares (GNC-TLS). Dynamical pose estimation provides a unifying solver that can be used for point cloud registration, primitive registration, and absolute pose estimation. The solver is straightforward to implement, and it does not require specialized software for optimization. The main contribution of this paper is to show how the dynamical pose estimation method can be extended to fit into the GNC-TLS framework so that high outlier fractions can be handled. The proposed method is validated for point cloud registration, primitive registration, and absolute pose estimation. The accuracy and robustness to outliers is shown to be on the level of existing GNC-TLS methods.
      PubDate: August 2022
      Issue No: Vol. 43, No. 2 (2022)
  • Model-free Control of Partially Observable Underactuated Systems by
           pairing Reinforcement Learning with Delay Embeddings

    • Authors: Knudsen; Martinius; Hendseth, Sverre; Tufte, Gunnar; Sandvig, Axel;
      Pages: 1 - 8
      Abstract: Partial observability is a problem in control design where the measured states are insufficient in describing the systems trajectory. Interesting real-world systems often exhibit nonlinear behavior and noisy, continuous-valued states that are poorly described by first principles, and which are only partially observable. If partial observability can be overcome, these conditions suggest the use of reinforcement learning (RL). In this paper we tackle the problem of controlling highly nonlinear underactuated dynamical systems, without a model, and with insufficient observations to infer the systems internal states. We approach the problem by creating a time-delay embedding from a subset of the observed state and apply RL on this embedding rather than the original state manifold. We find that delay embeddings work well with learning based methods, as such methods do not require a precise description of the systems state. Instead, RL learns to map any observation to appropriate action (determined by a reward function), even if these observations do not lie on the original geometric state manifold.
      PubDate: January 2022
      Issue No: Vol. 43, No. 1 (2022)
  • Elimination of Reflections in Laser Scanning Systems with Convolutional
           Neural Networks

    • Authors: Alstad; Ola; Egeland, Olav;
      Pages: 9 - 20
      Abstract: This paper presents a machine learning approach for eliminating reflections in line laser scanning of aluminium workpieces to be welded. The elimination of reflections is important to obtain accurate laser scanning of workpiece geometry for highly reflective materials like aluminium. The proposed solution is to use a convolutional neural network (CNN) which is trained to eliminate the reflections. The training of the network is done by simulating the laser line of the scanner in ray-tracing software using aluminium surfaces with appropriate reflection properties. This CNN then recovers the reflected laser line by removing the reflections. The CNN is used with two different camera configurations. In the first configuration one camera and one laser scanner are used. In the second configuration two cameras are used in a stereo arrangement in combination with the line laser. In this case, the planar homography of the laser plane is used to detect possible points on the laser line in a preprocessing step. The high performance of the solution is demonstrated for simulated data.
      PubDate: February 2022
      Issue No: Vol. 43, No. 1 (2022)
  • RMS Based Health Indicators for Remaining Useful Lifetime Estimation of

    • Authors: Klausen; Andreas; van Khang, Hyunh; Robbersmyr, Kjell G.;
      Pages: 21 - 38
      Abstract: Estimating the remaining useful life (RUL) of bearings from healthy to faulty is important for predictive maintenance. The bearing fault severity can be estimated based on the energy or root mean square (RMS) of vibration signals, and a stopping criterion can be set based on a threshold given by an ISO standard. However, the vibration RMS is often not monotonically increasing with damage, which renders a challenge for predicting the RUL. This study proposes a novel method for splitting the vibration signal into multiple frequency bands before RMS calculations to generate multiple health indicators. Monotonic health indicators are identified using the Spearman coefficient, and the RUL is afterward estimated for each indicator using a suitable model and parameter update scheme. Historical failure data is not required to set any parameters. The proposed method is tested with the Paris' law, where parameters are updated by particle filters. Experimental results from two test rigs validate the performance of the proposed method.
      PubDate: March 2022
      Issue No: Vol. 43, No. 1 (2022)
  • Editorial: Volume 32, No 2

  • Editorial: Volume 31, No 3

  • Editorial: Volume 31, No 1

  • Editorial: Volume 30, No 3

  • Editorial: Volume 30, No 2

  • Editorial: Volume 30, No 1

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