Trajectory Tracking Control of Differential Steering Mobile Robot Based on Fuzzy Logic under Time Constraints

Tan, Yaoyao; Wen, Baoyu; Jiao, Chunting; Su, Xiaojie; Xue, Fangzheng

doi:10.1109/ccdc52312.2021.9601451

Cited by 2 publications

(2 citation statements)

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“…The relationship between lateral error and heading error during vehicle steering can be obtained by combining Equation (12) with Equation ( 14), as shown in Equation (15).…”

Section: Path Tracking Error Modelmentioning

confidence: 99%

“…The primary objective of path tracking control in vehicle systems is to accurately follow a reference path by eliminating tracking bias. Numerous path tracking control strategies have been proposed by researchers, such as Proportional Integral Differential (PID) control [13,14], fuzzy control [15], Sliding Mode Control (SMC) [16], Model Predictive Control (MPC) [17], feedback feedforward control [18,19], and Linear Quadratic Regulator (LQR) control [20]. Among these methods, LQR is widely utilized in vehicle lateral control due to its simplicity in design, ease of implementation, and robustness against uncertainties [21].…”

Section: Introductionmentioning

confidence: 99%

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Research on Path Tracking and Yaw Stability Coordination Control Strategy for Four-Wheel Independent Drive Electric Trucks

Gao,

Zhao,

Zhang

2023

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Achieving accurate path tracking and vehicle stability control for four-wheel independent drive electric trucks under complex driving conditions, such as high speed and low adhesion, remains a major challenge in current research. Poor coordination control may cause the vehicle to deviate from its intended path and become unstable. To address this issue, this article proposes a coordinated control strategy consisting of a three-layer control framework. In the upper layer controller design, establish a linear quadratic regulator (LQR) path tracking controller to ensure precise steering control by eliminating steady-state errors through feedforward control. The middle layer controller utilizes the fractional order sliding mode control (FOSMC) yaw moment controller to calculate the additional yaw moment based on the steering angle of the upper input, utilizing the error of yaw rate and sideslip angle as the state variable. To collectively optimize the control system, establish a coordinated optimization objective function and utilize the hybrid genetic-particle swarm optimization algorithm (GA-PSO) to optimize the weight coefficient of LQR and sliding mode parameters of FOSMC, effectively improving the performance of the controller. In the lower layer torque distribution controller, use the quadratic programming method to achieve real-time optimal torque distribution based on tire utilization, which improves vehicle stability control. Through simulations conducted under four different working conditions, the proposed control scheme demonstrates a 15.54% to 23.17% improvement in tracking performance and a 10.83% to 23.88% optimization in vehicle driving stability compared to other control methods. This scheme provides a theoretical reference for path tracking and stability control in four-wheel independent drive electric trucks.

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“…The relationship between lateral error and heading error during vehicle steering can be obtained by combining Equation (12) with Equation ( 14), as shown in Equation (15).…”

Section: Path Tracking Error Modelmentioning

confidence: 99%