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Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems

Margarita A. Guerrero, Braghadeesh Lakshminarayanan, Cristian R. Rojas

TL;DR

This paper tackles data-driven control for nonlinear, regime-varying systems by marrying Data-Enabled Predictive Control (DeePC) with gain scheduling. It builds a bank of local, data-driven predictors indexed by a scheduling variable $\rho$, and online switches between them to preserve local model validity while using standard MPC machinery. On a nonlinear ship-steering benchmark, DeePC-GS delivers improved tracking and reduced computational burden compared with RoKDeePC, Koopman-MPC, and regular DeePC. The approach extends DeePC to practical nonlinear domains and suggests future work on learning region partitions and scheduling maps directly from data.

Abstract

Model predictive control is a well established control technology for trajectory tracking. Its use requires the availability of an accurate model of the plant, but obtaining such a model is often time consuming and costly. Data-Enabled Predictive Control (DeePC) addresses this shortcoming in the linear time-invariant setting, by skipping the model building step and instead relying directly on input-output data. Unfortunately, many real systems are nonlinear and exhibit strong operating-point dependence. Building on classical linear parameter-varying control, we introduce DeePC-GS, a gain-scheduled extension of DeePC for unknown, regime-varying systems. The key idea is to allow DeePC to switch between different local Hankel matrices -- selected online via a measurable scheduling variable -- thereby uniting classical gain scheduling tools with identification-free, data-driven MPC. We test the effectiveness of our DeePC-GS formulation on a nonlinear ship-steering benchmark, demonstrating that it outperforms state-of-the-art data-driven MPC while maintaining tractable computation.

Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems

TL;DR

This paper tackles data-driven control for nonlinear, regime-varying systems by marrying Data-Enabled Predictive Control (DeePC) with gain scheduling. It builds a bank of local, data-driven predictors indexed by a scheduling variable , and online switches between them to preserve local model validity while using standard MPC machinery. On a nonlinear ship-steering benchmark, DeePC-GS delivers improved tracking and reduced computational burden compared with RoKDeePC, Koopman-MPC, and regular DeePC. The approach extends DeePC to practical nonlinear domains and suggests future work on learning region partitions and scheduling maps directly from data.

Abstract

Model predictive control is a well established control technology for trajectory tracking. Its use requires the availability of an accurate model of the plant, but obtaining such a model is often time consuming and costly. Data-Enabled Predictive Control (DeePC) addresses this shortcoming in the linear time-invariant setting, by skipping the model building step and instead relying directly on input-output data. Unfortunately, many real systems are nonlinear and exhibit strong operating-point dependence. Building on classical linear parameter-varying control, we introduce DeePC-GS, a gain-scheduled extension of DeePC for unknown, regime-varying systems. The key idea is to allow DeePC to switch between different local Hankel matrices -- selected online via a measurable scheduling variable -- thereby uniting classical gain scheduling tools with identification-free, data-driven MPC. We test the effectiveness of our DeePC-GS formulation on a nonlinear ship-steering benchmark, demonstrating that it outperforms state-of-the-art data-driven MPC while maintaining tractable computation.

Paper Structure

This paper contains 16 sections, 8 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Preliminaries
  4. DeePC
  5. Gain Scheduling
  6. Proposed Approach
  7. Data Collection
  8. DeePC-GS Optimization
  9. Computational footprint
  10. Region switching (hard vs. soft)
  11. DeePC-GS Algorithm
  12. Experiments
  13. Case Study: Ship Steering Autopilot
  14. Experimental Setting
  15. Experimental Results
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Nominal heading tracking. Top: yaw angle $\psi$ (solid) and reference (dashed). Upper middle: surge speed $U$ (scheduling variable). Lower middle: rudder angle $\delta$. Bottom: propeller thrust $\tau$. Vertical purple dashes indicate Hankel switches; $H_j\!\to\!H_k$ labels denote the switch.
  • Figure 2: Heading tracking with disturbance. Top: yaw angle $\psi$ (solid) and reference (dashed). Middle: surge speed $U$ (scheduling variable). Bottom: propeller thrust $\tau$ (left axis) and rudder angle $\delta$ (right axis). Vertical purple dashes indicate Hankel switches; $H_j\!\to\!H_k$ labels denote the switch.