High-gain model-following control for trajectory tracking

TL;DR

This paper develops a high-gain model-following control method for nonlinear systems, ensuring practical trajectory tracking with bounded errors and reduced peaking, demonstrated through an automotive cruise control case study.

Contribution

It introduces a computationally efficient high-gain feedback design for model-following control in nonlinear systems, with theoretical guarantees on tracking accuracy and peaking mitigation.

Findings

Establishes ultimate boundedness of tracking error.

Provides bounds for high-gain parameters to achieve desired accuracy.

Demonstrates effectiveness in automotive cruise control case study.

Abstract

We consider trajectory tracking for minimum-phase nonlinear systems in Byrnes-Isidori form using the model-following control (MFC) architecture. The tracking problem is motivated by a hierarchical control concept where a higher-level instance provides the reference trajectory at run-time. We present a computational efficient implementation of the feedback linearisation MFC design, and apply high-gain feedback in the process control loop (PCL) to achieve practical tracking in presence of Lipschitz perturbations. Our main results establish ultimate boundedness of the tracking error and give a constructive bound for the high-gain scaling parameter to achieve arbitrary tracking precision. Further we establish that the peaking phenomenon can be attenuated using MFC. We demonstrate the results via an automotive case study considering advanced engine-based cruise control.