Virtual Sensor for Vehicle Sideslip Angle Based on Extended Kalman Filter

2011 ◽  
Author(s):  
Pan Shenghui ◽  
Lin Chuan ◽  
Li Menghe ◽  
Chen Lezhu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sideslip Angle ◽  
Virtual Sensor

Comparison of Nonlinear Receding-Horizon and Extended Kalman Filter Strategies for Ground Vehicles Longitudinal Slip Estimation

2020 ◽  
Author(s):  
Elias Dias Rossi Lopes ◽  
Gustavo Simão Rodrigues ◽  
Helon Vicente Hultmann Ayala
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Autonomous Vehicles ◽  
Contact Forces ◽  
Control Synthesis ◽  
Sideslip Angle ◽  
Estimation Algorithms ◽  
Longitudinal Slip ◽  
Tire Forces

Friction efforts are present in almost all mechanical applications, due to contact between bodies and there are many important situations, in which they must be properly controlled. Among these, there are tire contact forces, which is focus of many studies in autonomous vehicles and control applications on vehicle systems, since the tire forces and moments are nonlinear and may be modelled as friction efforts. Any control synthesis focused to optimize its performance must be associated to state estimators, since the efforts depend on slip variables, as longitudinal slip and sideslip angle, and it is not possible to accurately measure them. So, in this paper, two state estimation algorithms are evaluated: Extended Kalman Filter (EKF) and Moving Horizon State Estimation (MHSE), which are applied to a quarter-car model for longitudinal dynamics. It is presented that, for both traction and braking phases, the MHSE is more accurate, since it takes explicitly into account the nonlinear model in the estimation process, independently of Jacobian sensitivities to discontinuities as is the case here. So, it is demonstrated that the developed estimator may be successfully associated to controllers with the objective of optimize tire performance in traction and braking control.

Sideslip angle estimation using extended Kalman filter

2008 ◽  
Vol 46 (sup1) ◽  
pp. 353-364 ◽  
Author(s):  
B.-C. Chen ◽  
F.-C. Hsieh
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sideslip Angle ◽  
Angle Estimation

scholarly journals 6D Virtual Sensor for Wrench Estimation in Robotized Interaction Tasks Exploiting Extended Kalman Filter

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 67
Author(s):  
Loris Roveda ◽  
Andrea Bussolan ◽  
Francesco Braghin ◽  
Dario Piga
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Impedance Control ◽  
Human Robot Interaction ◽  
Industrial Robots ◽  
Virtual Sensor ◽  
Robot Controller ◽  
Interaction Control ◽  
Implementation Effort ◽  
High Bandwidth

Industrial robots are commonly used to perform interaction tasks (such as assemblies or polishing), requiring the robot to be in contact with the surrounding environment. Such environments are (partially) unknown to the robot controller. Therefore, there is the need to implement interaction controllers capable of suitably reacting to the established contacts. Although standard force controllers require force/torque measurements to close the loop, most of the industrial manipulators do not have installed force/torque sensor(s). In addition, the integration of external sensors results in additional costs and implementation effort, not affordable in many contexts/applications. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper for the online estimation of the interaction wrench, implementing a 6D virtual sensor. Relying on sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is proposed for the interaction wrench estimation. The described approach has been validated in simulations, taking into account four different scenarios. In addition, experimental validation has been performed employing a Franka EMIKA panda robot. A human–robot interaction scenario and an assembly task have been considered to show the capabilities of the developed EKF, which is able to perform the estimation with high bandwidth, achieving convergence with limited errors.

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