Novel Two-stage Nonlinear Interconnected Unknown Input Observer Design: Hardware & Experimental Vehicle Validation

FOUKA, Majda; SENTOUH, Chouki; POPIEUL, Jean-Christophe · 2021 · Crossref

DOI: 10.21203/rs.3.rs-220514/v1

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Summary

This paper addresses the challenge of estimating both lateral and longitudinal nonlinear dynamics in vehicles, a problem complicated by the interdependency of these motion modes and the presence of unknown inputs. The authors aim to overcome coupling features and observability issues associated with immeasurable real-time variations in forward speed and tire slip velocities. To achieve this, they propose a Novel Nonlinear Interconnected Unknown Input Observer (NI-UIO) framework. This approach extends lateral motion estimation to include longitudinal dynamics, accounting for unknown accelerator, brake pedal, and driver steering torque inputs, as well as tire-ground pneumatic efforts. By incorporating these factors, the design seeks to reduce conservatism and improve estimation accuracy compared to traditional methods that treat these dynamics separately or ignore specific unknown inputs. The methodology employs a Takagi-Sugeno (TS) fuzzy model to handle the nonlinearities inherent in the vehicle dynamics during observer synthesis. The stability of the estimation errors is ensured through Input to State Stability (ISS) analysis, utilizing Lyapunov stability arguments. This theoretical framework provides robustness guarantees against disturbances caused by immeasurable nonlinearities. The sufficient conditions for the ISS property are formulated as an optimization problem expressed in terms of Linear Matrix Inequalities (LMIs). This mathematical formulation allows for the systematic design of the observer while ensuring stability and performance under varying conditions. The proposed observer was validated through both hardware-in-the-loop simulations and experimental vehicle tests. The hardware validation utilized the SHERPA dynamic interactive driving simulator, while the experimental validation was conducted on a TWINGO vehicle prototype at the LAMIH experimental test track. The results demonstrated the observer's ability to accurately estimate states and reconstruct unknown inputs. Figures comparing real measurements with observer estimations showed close alignment, indicating high performance. Robustness tests further confirmed the observer's reliability under various driving maneuvers, including braking, traction, and steering inputs. The experimental data highlighted the effectiveness of the NI-UIO in handling the complex, interconnected nature of vehicle dynamics. The significance of this work lies in its contribution to vehicle safety and control systems by providing a robust method for state estimation in nonlinear, interconnected systems. By successfully integrating lateral and longitudinal dynamics estimation within a single observer framework, the study offers a more comprehensive solution for monitoring vehicle behavior. The use of ISS properties and LMI-based design ensures that the observer remains stable and accurate despite uncertainties and unknown inputs. This approach has potential applications in advanced driver assistance systems and autonomous vehicle control, where precise and reliable state estimation is critical for safe operation. The successful experimental validation underscores the practical applicability of the proposed method.

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