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Processor-in-the-Loop Validation of a Gradient Descent-Based Model Predictive Control for Assisted Driving and Obstacles Avoidance Applications

Pierpaolo Dini, Sergio Saponara

2022IEEE Access45 citationsDOIOpen Access PDF

Abstract

For safety-critical applications, the validation process using a model-based approach plays an increasingly important role. In this paper we propose the application of a predictive control algorithm, entirely implemented in low-level code, to the use case of assisted driving of four-wheel vehicles. The aim is to present the workflow for the validation of an advanced control algorithm and its implementation on an Embedded system, representative of the computational capabilities of Automotive ECUs. The proposed validation exploits the SIL (Software-In-the-Loop) and PIL (Processor-In-the-Loop) paradigms to analyse the combination of control parameters and factors related to the choice of the mathematical model describing the vehicle behaviour and the choice of the numerical algorithms selected to approximate the differential equations.

Topics & Concepts

Model predictive controlComputer scienceLoop (graph theory)Gradient descentControl theory (sociology)Control (management)Artificial intelligenceArtificial neural networkMathematicsCombinatoricsReal-time simulation and control systemsAdvanced Control Systems OptimizationVehicle Dynamics and Control Systems
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