Adaptive Control for Non-Linear Test Bench Dynamometer Systems

Fanesi, Marika; Scaradozzi, David

doi:10.1109/icstcc.2019.8885558

Cited by 3 publications

(6 citation statements)

References 21 publications

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“…To verify the proposed method, the DSM model and the controller is developed using the MathWorks Matlab/Simulink software. The preliminary outcomes obtained simulating the vehicle on the testbench is shown in [1]. Based on these previous results, the simulations are enhanced, adding the updated driving cycles presented in section 2.…”

Section: Simulations and Discussionmentioning

confidence: 99%

“…Lastly, as presented in the introduction, the validation tests assure that the results reflect real-world emission. This driving cycle is integrated into the system proposed in the following section to enhance the results on [1].…”

Section: Driving Cycles Developmentmentioning

confidence: 99%

“…Based on the virtual vehicle developed presented in [1], lateral and vertical dynamics are included. The analysis of the static and dynamic behaviour is necessary to have a stable model of the vehicle.…”

Section: Vehicle Modelmentioning

confidence: 99%

“…Therefore, the steering behaviour in the single-track road is included in the response of the control vehicle as to the static behaviour. The implemented control follows the same strategy explained in [1]. The Kalman filter inside is implemented as:…”

Section: Control Strategymentioning

confidence: 99%

“…However, to achieve these, the test benches are fundamentals. A novel data approach for advanced control strategies applied on a modelled test benches was introduced in a paper originally presented at the 23rd International Conference on System Theory, Control and Computing (ICSTCC) in 2019 [1], for which this work is an extension.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Advanced Control Strategies on Nonlinear Testbench Dynamometer System for Simulating the Fuel Consumption

Fanesi¹,

Scaradozzi²

2020

Adv. sci. technol. eng. syst. j.

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The adoption of Engine-in-the-loop technology shows real behaviour. This study presents a test runs simulation platform with real engine data. In addition, a test bench model is a demand approach that offers a significant potential to provide an excellent reproducibility of test runs. The platform includes the data integration to upgrade tests run and a comparison with previous results using the advancing control techniques designed. The dynamometer system presents significantly non-linearity. The adaptive control approach, integrated into the Model Predictive Control on the vehicle, allows increasing the tests run performance. The results show how the real data can improve performance and the validation of the system integrating the updated driving cycle and maintaining EiL approach. The conclusion showed the significant benefits regarding the control methods used.

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Section: Simulations and Discussionmentioning

confidence: 99%

Section: Driving Cycles Developmentmentioning

confidence: 99%

Section: Vehicle Modelmentioning

confidence: 99%

Section: Control Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced Control Strategies on Nonlinear Testbench Dynamometer System for Simulating the Fuel Consumption

Fanesi¹,

Scaradozzi²

2020

Adv. sci. technol. eng. syst. j.

Self Cite

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A Spline Kernel-Based Approach for Nonlinear System Identification with Dimensionality Reduction

Zhang

Zhu

2020

Electronics

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This paper proposes a novel approach for identification of nonlinear systems. By transforming the data space into a feature space, kernel methods can be used for modeling nonlinear systems. The spline kernel is adopted to produce a Hilbert space. However, a problem exists as the spline kernel-based identification method cannot deal with data with high dimensions well, resulting in huge computational cost and slow estimation speed. Additionally, owing to the large number of parameters to be estimated, the amount of training data required for accurate identification must be large enough to satisfy the persistence of excitation conditions. To solve the problem, a dimensionality reduction strategy is proposed. Transformation of coordinates is made with the tool of differential geometry. The purpose of the transformation is that no intersection of information with relevance to the output will exist between different new states, while the states with no impact on the output are extracted, which are then abandoned when constructing the model. Then, the dimension of the kernel-based model is reduced, and the number of parameters to be estimated is also reduced. Finally, the proposed identification approach was validated by simulations performed on experimental data from wind tunnel tests. The identification result turns out to be accurate and effective with lower dimensions.

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Test Cycle Simulation of Bench Based on Deep Reinforcement Learning

Gong,

Li,

et al. 2024

SAE Int. J. Passeng. Veh. Syst.

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<div>Test cycle simulation is an essential part of the vehicle-in-the-loop test, and the deep reinforcement learning algorithm model is able to accurately control the drastic change of speed during the simulated vehicle driving process. In order to conduct a simulated cycle test of the vehicle, a vehicle model including driver, battery, motor, transmission system, and vehicle dynamics is established in MATLAB/Simulink. Additionally, a bench load simulation system based on the speed-tracking algorithm of the forward model is established. Taking the driver model action as input and the vehicle gas/brake pedal opening as the action space, the deep deterministic policy gradient (DDPG) algorithm is used to update the entire model. This process yields the dynamic response of the output end of the bench model, ultimately producing the optimal intelligent driver model to simulate the vehicle’s completion of the World Light Vehicle Test Cycle (WLTC) on the bench. The results indicate that the algorithm exhibits good convergence in the simulation, throughout the WLTC simulation, the driver always kept the vehicle speed error within 1 km/h, and the response time is less than 0.5 s under the vehicle’s starting condition. In comparison to the PID control algorithm and the model predictive control (MPC) algorithm, it demonstrates smaller speed error and response time, ensuring accuracy, high efficiency, and safety during the indoor vehicle-in-the-loop test.</div>

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Adaptive Control for Non-Linear Test Bench Dynamometer Systems

Cited by 3 publications

References 21 publications

Advanced Control Strategies on Nonlinear Testbench Dynamometer System for Simulating the Fuel Consumption

Advanced Control Strategies on Nonlinear Testbench Dynamometer System for Simulating the Fuel Consumption

A Spline Kernel-Based Approach for Nonlinear System Identification with Dimensionality Reduction

Test Cycle Simulation of Bench Based on Deep Reinforcement Learning

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