Model predictive control system based on direct yaw moment control for 4WID self-steering agriculture vehicle

Abstract

A model predictive control (MPC) approach based on direct yaw moment control (DYC) was proposed to realize the self-steering drive for a newly autonomous four-wheel independent-drive (4WID) agricultural electric vehicle. The front axle and rear axle of the vehicle chassis could rotate simultaneously around their respective center points and cut the turning radius in half at most through specific mechanical chassis structure design and four-wheel electrical drive. It had great potential to reduce wheel traffic damage to field crops if two rear electrical drive wheels can be controlled to follow wheel tracks of two front wheels during self-steering operation. Therefore, firstly, a two-degree-freedom dynamics model presenting this agricultural electric vehicle was constructed. Then, an MPC controller combined with DYC was applied to arrange torques from four wheels to match desired turning angles, direct yaw moments and travel speeds. The simulation results existed small steady error of steering angles below 0.22% as they were set at 5°, followed with yaw moment under 0.17% and velocity less than 1%. Finally, according to experiment results, the vehicle successfully made a working turning radius of 9.1 m with maximum error of 0.55% when desired steering angles were 5° at the speed of 1 m/s and a minimum turning radius of 1.51 m with maximum error of 6.6% when steering angles were 30° at the speed of 0.5 m/s. It verified that the 4WID agricultural electric vehicle could drive autonomously and steady with small self-steering angle error under the proposed control system and has a feasibility to reduce wheel traffic damage during driving and operation.

Improving Direct Yaw-Moment Control via Neural-Network-Based Non-Singular Fast Terminal Sliding Mode Control for Electric Vehicles

“…Ensuring smooth control and performance reliability poses challenges for DYC systems due to the non-linearity, uncertainties, and interconnected dynamics of vehicles. Consequently, significant research endeavors are underway to address these challenges through various control strategies, including proportional-integral-derivative (PID) control [13,14], linear quadratic regulator (LQR) control [15,16], model predictive control (MPC) [17,18], and resilient control [19,20]. Initially, studies emphasized the implementation of a PID controller [13,14].…”

“…Consequently, these control systems remain vulnerable to disturbances from the external environment or inaccuracies in modeling real-world systems, particularly in segments demonstrating significant non-linearity. Moreover, MPC [17,18] is commonly used for longitudinal tracking in pure electric vehicles. However, recent MPC methods do not typically address the issue of softened constraints.…”

Torque Fault-Tolerant Hierarchical Control of 4WID Electric Vehicles Based on Improved MPC and SMC

“…For example, in [23], Real-time switching control of a multi-control approach can adjust controller parameters to maximize vehicle stability. The reconfigurable controller utilizes insights from motor fault diagnosis to effectively manage the operation of both healthy and faulty motors in a coordinated manner [24]- [27]. The reconfigurable control strategy usually coordinates control variables based on the changing system parameters under a motor fault condition.…”

Path Tracking of Agricultural Vehicles Based on 4WIS–4WID Structure and Fuzzy Control

“…Coordinated regulation of the vehicle's sway and deflection angles is required for smoother WIS steering. Up-to-date control techniques such as fuzzy and model predictive control have been applied to study direct deflection control [11][12][13]. The path-tracking controller introduced in [14] shows that four-channel autoturbation control (ADRC) could achieve performance like anti-side wind, anti-skid, and anti-rollover in path tracking of four-wheel steering (4WS) agricultural vehicles.…”