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

Abstract

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.

Implementation of a C Library of Kalman Filters for Application on Embedded Systems

“…In this setup, the EKF-SR was successfully integrated in a small-scale production series ECU and employed in a real vehicle test drive under demanding real-time conditions, and has thus demonstrated its applicability. The complete use case, also containing a control system, is described in [28] and the tool chain and the results are briefly summarized in the following subsection.…”

“…Differences between the two scenarios relate to the underlying estimation problem, i.e., to the vehicle prediction models applied, the Kalman filter variant used, and the system on which the state estimation library is deployed. The first of these application examples is covered only briefly, since related results have already been published in another context in [28], whereas the second example is shown and discussed in more detail.…”

Intermediate-Variable-Based Distributed Fusion Estimation for Wind Turbine Systems

“…Under this case, the right term of inequality (24) can be viewed as an upper bound of e T zi (k + 1)e zi (k + 1) , and "min Tr{β i (k)} + Tr{γ i (k)}" can be treated as the optimization objective to solve the local intermediate observer gain L i (k). Now, the distributed weighting matrix Ω(k) is determined by solving the convex optimization problem (14). Define e F (k)…”

“…. The disturbance signal and measurement noises are given by 3,4,5,6,7,8,9,10,11,12,13,14) are random variables that can be generated by the function "rand" of MATLAB. The parameter µ of the intermediate observer is 1.…”

“…Particularly, the disturbance estimation problem based on the wind turbine transmission system was investigated in [13], where the estimation accuracy of the extended Kalman filter and the unknown input observer were presented, respectively. Though many state estimation algorithms have been developed and applied in various fields [14][15][16], the robustness, reliability and fault tolerance are not displayed well in the single sensor estimation. Thus, multi-sensor information fusion technology [17,18] was presented, which could overcome the limitations of single sensor estimation in order to integrate redundant information of multiple sensors and then to obtain a better estimation performance.…”