Pilot Vehicle System Modeling Using Sub-Scale Flight Experiments

The sheer complexity of engineering propulsion systems for hybrid electric vehicles (HEV) demands the use of model-based development processes supported by comprehensive, robust vehicle system models. A Vehicle System Modeling (VSM) process has been developed to provide high-quality, application-appropriate vehicle system models in time to support critical HEV engineering activities. The process seeks to manage the complexity of the large number of model variants that are required to support a vehicle program. Additionally, it drives model development and aligns modeling activities with program timing. This paper describes the key elements of the VSM process and presents an application example. The application example illustrates the process by which a highly detailed HEV system model is created from an initial, base conventional vehicle system model via integration of high fidelity component models into a re-usable vehicle system modeling framework. The component models come from a variety of modeling tools and environments, which introduces additional complexity that must be managed. Results generated from the model show the complex system interactions that must be addressed by the vehicle control strategy. This re-enforces the notion that such modeling is required to achieve robust system designs.

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A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty

Complexity ◽

10.1155/2017/6034786 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Chuanfeng Li ◽

Hao Wu ◽

Zhile Yang ◽

Yongji Wang ◽

Zeyu Sun

Keyword(s):

System Modeling ◽

External Disturbance ◽

Uncertain System ◽

Hypersonic Vehicle ◽

State Feedback Control ◽

Linear Matrix ◽

Optimization Approach ◽

Combined System ◽

Vehicle System ◽

Guaranteed Cost

Hypersonic vehicle is a typical parameter uncertain system with significant characteristics of strong coupling, nonlinearity, and external disturbance. In this paper, a combined system modeling approach is proposed to approximate the actual vehicle system. The state feedback control strategy is adopted based on the robust guaranteed cost control (RGCC) theory, where the Lyapunov function is applied to get control law for nonlinear system and the problem is transformed into a feasible solution by linear matrix inequalities (LMI) method. In addition, a nonfragile guaranteed cost controller solved by LMI optimization approach is employed to the linear error system, where a single hidden layer neural network (SHLNN) is employed as an additive gain compensator to reduce excessive performance caused by perturbations and uncertainties. Simulation results show the stability and well tracking performance for the proposed strategy in controlling the vehicle system.

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