RETRACTED: A new vehicle’s steering model and trajectory tracking based on online motion state estimation and adaptive fuzzy control method

Abstract

The roadside stone paver plays an important role in improving the road construction quality and speeding up the road construction progress. Faced with the high requirement for lateral distance precision, this study proposes a motion state estimation and fuzzy control method for trajectory tracking of the unmanned cement paver. The roadside stone paver has three crawler wheels, which are steered by cylinders. Based on the vehicle’s characteristics, the kinematic and dynamic models of the vehicle were established. For trajectory tracking, the values of front and back steering sensors were filtered to calculate the current lateral distance. Through the combination of the state prediction model and cylinder length constraints, the cylinders’ target lengths were calculated to obtain satisfactory results for the next time. Then, an adaptive fuzzy controller was designed to track the cylinders’ target lengths. It could reduce the calculation time effectively and maintain linear characteristics for the valve-controlled nonlinear system. To verify the validity of the motion state estimation and fuzzy control algorithm, a cosimulation was conducted on Simulink and Amesim software. Moreover, based on a new roadside stone paver, different algorithms were used for effect comparison. The simulation and experimental results show that the motion state estimation and fuzzy control algorithm can keep vehicle stability and reduce the error of trajectory tracking.

Enhancement neural control scheme performance using PSO adaptive rate: Experimentation on a transesterification reactor

“…Non-conventional adaptive control methods do not require the mathematical model describing the dynamics of the systems to be controlled like the artificial neural network (Errachdi and Benrejeb (2018). During the past, major advances have been made in adaptive control theory for identifying and controlling highly nonlinear and non-stationary systems in the presence of unmodeled dynamics and disturbances, such as model adaptive fuzzy control (Cui et al, 2020; Xiangyong et al, 2020), adaptive PID control Muliadi and Kusumoputro (2018); Salim et al (2019), adaptive backstepping control (Basaran et al, 2021; Yu et al, 2021), indirect adaptive control (Al Aela et al, 2022; Rodríguez-Molina et al, 2020), and so on.…”