Dual-Level Control Architectures for Over-Actuated Autonomous Vehicle's Stability, Path-Tracking, and Energy Economy
Abstract
Autonomous vehicles equipped with four independent in-wheel motors bestow salutary design flexibility and render the system over-actuated. The strategy percolated for torque allocation dictates the system's performance and marks its energy consumption. In this paper, two complete novel control architectures are developed and contrasted from the viewpoint of vehicle performance and energy consumption. A cascaded control strategy is employed by incorporating two distinct control levels. The high level is differentiated by a centralized approach based on the optimal H∞ control in the framework of the Linear Parameter Varying (LPV) systems, and a decentralized approach based on problem decoupling where a solution is proposed using the Super-Twisting Sliding Mode (STSM) control. Both approaches are supervised by a decision layer to promote the stability objective in critical driving situations. At the low level, stability control based on Direct Yaw Control (DYC) along with speed control are both achieved using an original torque allocation strategy. A comprehensive set of four multi-objective strategies has been devised, centered around a proposed torque allocation configuration. These strategies encompass dynamic online optimization, expertly solved using the highly efficient Sequential Quadratic Programming (SQP) method, as well as a unique offline optimization based on a data-driven implemented algorithm. The proposed architectures are tested and validated in a joint simulation between Simulink/MatLab and SCANeR TM Studio vehicle dynamics simulator. The simulation findings demonstrate substantial improvements in stability, comfort, and energy efficiency at both the high and low levels of the autonomous in-wheel-driven electric vehicle.
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