A Novel Kalman Filter Design and Analysis Method Considering Observability and Dominance Properties of Measurands Applied to Vehicle State Estimation

Ruggaber, Julian; Brembeck, Jonathan

doi:10.3390/s21144750

Cited by 14 publications

(15 citation statements)

References 21 publications

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“…Furthermore, the parameterization of the Kalman filter algorithms represents a challenging issue and can be carried out with the help of various methods, see e.g., [24]. In this paper, the filter parameters (system and measurement noise covariance matrices) are determined using a reference trajectory and minimizing the error between the estimated and the true states.…”

Section: Integration Of the Efmu Into The Embedded Kalman Filter C-librarymentioning

confidence: 99%

Advanced Controller Development Based on eFMI with Applications to Automotive Vertical Dynamics Control

et al. 2021

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High-level modeling languages facilitate system modeling and the development of control systems. This is mainly achieved by the automated handling of differential algebraic equations which describe the dynamics of the modeled systems across different physical domains. A wide selection of model libraries provides additional support to the modeling process. Nevertheless, deployment on embedded targets poses a challenge and usually requires manual modification and reimplementation of the control system. The novel proposed eFMI Standard (Functional Mock-up Interface for embedded systems) introduces a workflow and an automated toolchain to simplify the deployment of model-based control systems on embedded targets. This contribution describes the application and verification of the eFMI workflow using a vertical dynamics control problem with an automotive application as an example. The workflow is exemplified by a control system design process which is supported by the a-causal, multi-physical, high-level modeling language Modelica. In this process, the eFMI toolchain is applied to a model-based controller for semi-active dampers and demonstrated using an eFMI-based nonlinear prediction model within a nonlinear Kalman filter. The generated code was successfully tested in different validation steps on the dedicated embedded system. Additionally, tests with a low-volume production electronic control unit (ECU) in a series-produced car demonstrated the correct execution of the controller code under real-world conditions. The novelty of our approach is that it automatically derives an embedded software solution from a high-level multi-physical model with standardized eFMI methodology and tooling. We present one of the first full application scenarios (covering all aspects ranging from multi-physical modeling up to embedded target deployment) of the new eFMI tooling.

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Section: Integration Of the Efmu Into The Embedded Kalman Filter C-librarymentioning

confidence: 99%

Advanced Controller Development Based on eFMI with Applications to Automotive Vertical Dynamics Control

et al. 2021

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“…where the ∆ k−j is the Kronecker delta function, when k = j ∆ = 1, and when k = j ∆= 0. Generally, as a simplification, Q k is set as a diagonal matrix [25].…”

Section: Vehicle System Equation and Measurement Equationmentioning

confidence: 99%

Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

Wenkang,

Jingan,

Bao

et al. 2021

Applied Sciences

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The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion state accurately and in real-time, while reducing the effect of uncertainty in noise statistical information, the vehicle state observer is designed based on interacting multiple model theory with square root cubature Kalman filter (IMM-SCKF). The IMM-SCKF algorithm sub-model considers different state noise and measurement noise, and the introduction of the square root filter reduces the complexity of the algorithm while ensuring accuracy and real-time performance. To estimate the vehicle longitudinal, lateral, and yaw motion states, the algorithm uses a three degree of freedom (3-DOF) vehicle dynamics model and a nonlinear brush tire model, which is then validated in a Carsim-Simulink co-simulation platform for multiple operating conditions. The results show that the IMM-SCKF algorithm’s fusion output results can effectively follow the sub-model with smaller output errors, and that the IMM-SCKF algorithm’s results are superior to the traditional SCKF algorithm’s results.

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“…This can be achieved by exporting the physical model to a scripting environment such as Matlab or Python using the Functional Mock-up Interface (FMI) standard. (Ruggaber and Brembeck, 2021;Gonzalez, et al, 2017;Andrén, et al, 2015) demonstrate how various variants of Kalman filters can be implemented for state and parameter estimation, also exploiting the directional derivatives defined by the FMI 2.0 standard. The combined usage of Modelica, FMI and the scripting environment has been proven to be successful for optimal start-up of power plants in offline mode (Dietl et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

[Industrial paper] Digital Twin Applications Using a Cloud Native Modelica Platform

Kumar¹,

Narasimhan²,

Rajendran³

et al. 2022

Linköping Electronic Conference Proceedings

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This paper showcases how Modelica technology can be leveraged for real-time applications using a cloud native simulation platform, Modelon Impact™. The platform allows for real-time, two-way communication of data, from the IoT connected plant to a physical model and, from the physical model to a dashboard for plant monitoring and control. The communication relies on open standards and REST-API, which makes it possible to implement digital twins for various applications, such as plant monitoring, predictive maintenance, fault isolation or controls. The paper describes a state estimation workflow where data is transmitted back and forth to the simulation platform via Message Queuing Telemetry Transport (MQTT) and where Node-Red is used for the end-user interface.

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A Novel Kalman Filter Design and Analysis Method Considering Observability and Dominance Properties of Measurands Applied to Vehicle State Estimation

Cited by 14 publications

References 21 publications

Advanced Controller Development Based on eFMI with Applications to Automotive Vertical Dynamics Control

Advanced Controller Development Based on eFMI with Applications to Automotive Vertical Dynamics Control

Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

[Industrial paper] Digital Twin Applications Using a Cloud Native Modelica Platform

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