A5001424518- Vehicle Dynamics and Control Systems
- Autonomous Vehicle Technology and Safety
- Real-time simulation and control systems
- Hydraulic and Pneumatic Systems
- Advanced Control Systems Optimization
- Automotive and Human Injury Biomechanics
- Mechanical Engineering and Vibrations Research
- Aerospace Engineering and Applications
- Aerospace Engineering and Control Systems
- Robotic Path Planning Algorithms
- Vehicle Noise and Vibration Control
- Infrastructure Maintenance and Monitoring
- Electric Vehicles and Infrastructure
- Electric and Hybrid Vehicle Technologies
- Vehicle emissions and performance
- Soil Mechanics and Vehicle Dynamics
Delft University of Technology
2022-2025
Polytechnic University of Turin
2019
This paper proposes a novel vehicle sideslip angle estimator, which uses the physical knowledge from an Unscented Kalman Filter (UKF) based on non-linear single-track model to enhance estimation accuracy of Convolutional Neural Network (CNN). The model-based and data-driven approaches interact mutually, both use standard inertial measurement unit tyre forces measured by load sensing technology. CNN benefits UKF capacity leverage laws physics. Concurrently, outputs as pseudo-measurement...
This paper presents a novel methodology to auto-tune an Unscented Kalman Filter (UKF). It involves using Two-Stage Bayesian Optimisation (TSBO), based on t-Student Process optimise the process noise parameters of UKF for vehicle sideslip angle estimation. Our method minimises performance metrics, given by average sum states' and measurement' estimation error various manoeuvres covering wide range behaviour. The predefined cost function is minimised through TSBO which aims find location in...
This paper presents an original approach to vehicle obstacle avoidance. It involves the development of a nonlinear Model Predictive Contouring Control, which uses torque vectoring stabilise and drive in evasive manoeuvres at limit handling. The proposed algorithm combines motion planning, path tracking stability objectives, prioritising collision avoidance emergencies. controller's prediction model is double-track based on extended Fiala tyre capture coupled longitudinal lateral dynamics....
This paper presents a novel approach to automated drifting with standard passenger vehicle, which involves Nonlinear Model Predictive Control stabilise and maintain the vehicle at high sideslip angle conditions. The proposed controller architecture is split into three components. first part consists of offline computed equilibrium maps, provide points for each state given desired radius path. second predictive minimising errors between actual states. third path-following controller, reduces...
This paper presents a novel Learning-based Model Predictive Contouring Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The uses Student-t Process (STP) to minimise model mismatches and uncertainties online. proposed STP captures between prediction measured lateral tyre forces yaw rate. correspond posterior means provided improve its accuracy. Simultaneously, covariances are propagated vehicle velocity rate along horizon. covariance directly depends on variance...
This paper proposes a novel vehicle sideslip angle estimator, which uses the physical knowledge from an Unscented Kalman Filter (UKF) based on non-linear single-track model to enhance estimation accuracy of Convolutional Neural Network (CNN). The model-based and data-driven approaches interact mutually, both use standard inertial measurement unit tyre forces measured by load sensing technology. CNN benefits UKF capacity leverage laws physics. Concurrently, outputs as pseudo-measurement...
This paper proposes a non-linear Model Predictive Contouring Control (MPCC) for obstacle avoidance in automated vehicles driven at the limit of handling. The proposed controller integrates motion planning, path tracking and vehicle stability objectives, prioritising emergencies. controller's prediction model is single-track with Fiala tyre to capture vehicle's behaviour. MPCC computes optimal steering angle brake torques minimise error safe situations maximise vehicle-to-obstacle distance...