model-in-the-loop (MiL) testing is a method in which the test object is split into a physical part and a simulated part, and these are connected with interfaces to form a combined physical-numerical system. The challe...
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ISBN:
(纸本)9781467398916
model-in-the-loop (MiL) testing is a method in which the test object is split into a physical part and a simulated part, and these are connected with interfaces to form a combined physical-numerical system. The challenge of generating a MiL test is that, firstly, because of the limited dynamic response of the actuators, the test results may be inaccurate, and secondly, because of the high frequency noise introduced by the sensors to the closed-loop system, it may be difficult to design a compensator for the actuator response, while stabilizing the closed-loop system at the same time. In this paper, a MiL system is designed using a small hydraulic robot arm. The problems with the MiL test without any compensator is shown with experimental results. The effectiveness of a 1st order phase lead compensator and an inverse model compensator are validated in the experiment. For systems which can be approximated by linear time-invariant models, it is proposed that compensator design is a linear optimization problem balancing emulation error with noise amplification. Thus, a new method of designing the compensator for MiL testing based on H-infinity optimization is presented.
The testing of the intelligent driving systems is faced with the challenges of efficiency because real traffic scenarios are infinite, uncontrollable and difficult to be precisely defined. Based on the complexity inde...
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The testing of the intelligent driving systems is faced with the challenges of efficiency because real traffic scenarios are infinite, uncontrollable and difficult to be precisely defined. Based on the complexity index of scenario that designed to measure the test effect indirectly, a new combinational testing algorithm of test cases generation is proposed to make a balance among multiple objects including test coverage, the number of test cases and test effect. Then a joint simulation platform based on Matlab, PreScan and Carsim is built up to realize the construction of 3D test environment, execution of test scenarios and evaluation of test results automatically and seamlessly. The strategy proposed in this paper is validated by applying it to a traffic jam pilot system. The result shows that the proposed strategy can improve the overall complexity of the designed test scenarios effectively, which can help us detect system faults faster and easier. And the time required to conduct tests is reduced obviously by means of automation.
Context: testing and verification of automotive embedded software is a major challenge. Software production in automotive domain comprises three stages: Developing automotive functions as Simulink models, generating c...
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Context: testing and verification of automotive embedded software is a major challenge. Software production in automotive domain comprises three stages: Developing automotive functions as Simulink models, generating code from the models, and deploying the resulting code on hardware devices. Automotive software artifacts are subject to three rounds of testing corresponding to the three production stages: model-in-the-loop (MiL), Software-in-the-loop (SiL) and Hardware-in-the-loop (HiL) testing. Objective: We study testing of continuous controllers at the model-in-loop (MiL) level where both the controller and the environment are represented by models and connected in a closed loop system. These controllers make up a large part of automotive functions, and monitor and control the operating conditions of physical devices. Method: We identify a set of requirements characterizing the behavior of continuous controllers, and develop a search-based technique based on random search, adaptive random search, hill climbing and simulated annealing algorithms to automatically identify worst-case test scenarios which are utilized to generate test cases for these requirements. Results: We evaluated our approach by applying it to an industrial automotive controller (with 443 Simulink blocks) and to a publicly available controller (with 21 Simulink blocks). Our experience shows that automatically generated test cases lead to MiL level simulations indicating potential violations of the system requirements. Further, not only does our approach generate significantly better test cases faster than random test case generation, but it also achieves better results than test scenarios devised by domain experts. Finally, our generated test cases uncover discrepancies between environment models and the real world when they are applied at the Hardware-in-the-loop (HiL) level. Conclusion: We propose an automated approach to MiL testing of continuous controllers using search. The approach is imple
Context: testing and verification of automotive embedded software is a major challenge. Software production in automotive domain comprises three stages: Developing automotive functions as Simulink models, generating c...
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Context: testing and verification of automotive embedded software is a major challenge. Software production in automotive domain comprises three stages: Developing automotive functions as Simulink models, generating code from the models, and deploying the resulting code on hardware devices. Automotive software artifacts are subject to three rounds of testing corresponding to the three production stages: model-in-the-loop (MiL), Software-in-the-loop (SiL) and Hardware-in-the-loop (HiL) testing. Objective: We study testing of continuous controllers at the model-in-loop (MiL) level where both the controller and the environment are represented by models and connected in a closed loop system. These controllers make up a large part of automotive functions, and monitor and control the operating conditions of physical devices. Method: We identify a set of requirements characterizing the behavior of continuous controllers, and develop a search-based technique based on random search, adaptive random search, hill climbing and simulated annealing algorithms to automatically identify worst-case test scenarios which are utilized to generate test cases for these requirements. Results: We evaluated our approach by applying it to an industrial automotive controller (with 443 Simulink blocks) and to a publicly available controller (with 21 Simulink blocks). Our experience shows that automatically generated test cases lead to MiL level simulations indicating potential violations of the system requirements. Further, not only does our approach generate significantly better test cases faster than random test case generation, but it also achieves better results than test scenarios devised by domain experts. Finally, our generated test cases uncover discrepancies between environment models and the real world when they are applied at the Hardware-in-the-loop (HiL) level. Conclusion: We propose an automated approach to MiL testing of continuous controllers using search. The approach is imple
Real-time hybrid testing combines the reliability of experimental testing with the convenience of numerical simulation. The system to be tested is split into a physical substructure and a real-time numerical simulatio...
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Real-time hybrid testing combines the reliability of experimental testing with the convenience of numerical simulation. The system to be tested is split into a physical substructure and a real-time numerical simulation which are coupled using actuators and sensors to transfer data at the interface in real-time. In order to achieve stable and accurate hybrid testing representative of the true system, high fidelity control is required at the substructure interface. However, actuators have a response lag which results in tracking errors and potential instability in hybrid tests. This paper investigates the effectiveness of a combined compensation strategy based on passivity control and adaptive feedforward filtering to improve stability, robustness and tracking performance in real-time hybrid testing. The combined strategy is adaptive and requires no prior information of the actuator dynamics unlike conventional transfer dynamics compensators in real-time hybrid testing. Moreover, the scheme requires no extra hardware making it inexpensive and applicable to a wide range of systems. Experimental results on a single degree of freedom nonlinear real-time hybrid test show the potency of the scheme in synchronizing substructure displacements while improving stability. The scheme was also found to restore stability of hybrid tests inherently unstable due to actuator delay whilst phase lags of up to 58 degrees have been successfully mitigated in a lumped parameter mechanical oscillator system. (c) 2020 Elsevier Ltd. All rights reserved.
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