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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Virtual Sensor: Simultaneous State and Input Estimation for Nonlinear Interconnected Ground Vehicle System Dynamics.

Chouki Sentouh1,2, Majda Fouka1, Jean-Christophe Popieul1,2

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Summary
This summary is machine-generated.

This study introduces a novel observer for estimating vehicle dynamics and driver inputs. The approach enhances vehicle state estimation and road condition analysis for improved safety and performance.

Keywords:
interconnected observersinterlinked vehicle dynamicsstate estimationunknown inputs estimationvehicle dynamicsvehicle safety

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Area of Science:

  • Automotive Engineering
  • Control Systems
  • Nonlinear Dynamics

Background:

  • Accurate estimation of vehicle dynamics and external inputs is crucial for advanced driver-assistance systems (ADAS).
  • Existing methods face challenges with nonlinearities, coupling effects, and unmeasurable parameters like tire slip and road curvature.

Purpose of the Study:

  • To develop a robust virtual sensor for simultaneous estimation of vehicle lateral/longitudinal dynamics and unknown inputs.
  • To address observability and interconnection issues in complex vehicle systems.

Main Methods:

  • Utilized a cascade observer structure with a linear-parameter-varying (LPV) interconnected unknown inputs observer (UIO) framework.
  • Employed Takagi-Sugeno (T-S) fuzzy logic to handle nonlinearities in vehicle speed and tire slip.
  • Applied Lyapunov stability arguments to ensure input-to-state stability (ISS) of estimation errors.

Main Results:

  • The proposed LPV-UIO framework effectively reduces complexity and conservatism.
  • Demonstrated accurate estimation of vehicle states, driver inputs (pedal forces, steering torque), and road attributes (curvature).
  • Validated through extensive simulations (Sherpa simulator) and real-world experiments (Twingo vehicle prototype).

Conclusions:

  • The novel observer design provides a robust and precise virtual sensing solution for vehicle dynamics.
  • The LPV-T-S fuzzy approach effectively manages nonlinearities and improves estimation accuracy.
  • Experimental validation confirms the practical applicability and effectiveness of the proposed method.