A Human-optimized Model Predictive Control Scheme and Extremum Seeking Parameter Estimator for Slip Control of Electric Race Cars

TL;DR

This paper introduces a real-time slip control system for electric race cars that combines Model Predictive Control with Extremum Seeking to optimize traction and braking without prior tire knowledge.

Contribution

It develops a human-optimized, low-computation MPC-ESC integrated control scheme with an analytical solution and adaptive slip estimation for electric race cars.

Findings

System automatically determines optimal slip values

Demonstrates stability under varying conditions

Outperforms other methods in simulations

Abstract

This paper presents a longitudinal slip control system for a rear-wheel-driven electric endurance race car. The control system integrates Model Predictive Control (MPC) with Extremum Seeking Control (ESC) to optimize the traction and regenerative braking performance of the powertrain. The MPC contains an analytical solution which results in a negligible computation time, whilst providing an optimal solution to a multi-objective optimization problem. The ESC algorithm allows continuous estimation of the optimal slip reference without assuming any prior knowledge of the tire dynamics. Finally, the control parameters are determined using a human-driven preference-based optimization algorithm in order to obtain the desired response. Simulation results and comparisons with other methods demonstrate the system's capability to automatically determine and track the optimal slip values, showing stability and performance under varying conditions.