Predictor-Compensated CLF-QP for Vehicle Tracking with Sensing and Actuation Delays

Delay-aware control for teleoperated vehicle tracking using CLF-QP with SQP.

Overview

This project studies delay-aware vehicle tracking for teleoperation. The controller combines predictor compensation with a control Lyapunov function quadratic program (CLF-QP) solved via SQP to enforce stability and constraints under sensing and actuation delays.

Agenda

  • Background and motivation
  • Problem statement
  • Literature review
  • Algorithm overview
  • Results and discussion
  • Summary and future work

Background and Motivation

Modern control systems experience delays from sensing pipelines, communication networks, computation, and actuator dynamics. In safety-critical settings like autonomous driving, teleoperation, V2X communication, and multi-vehicle coordination, these delays can degrade tracking and compromise stability.

Existing methods have gaps when measurement delays and actuation delays occur simultaneously.

  • Predictor feedback ensures stability but does not directly enforce stability constraints.
  • Delay-aware barrier approaches emphasize invariance but lack explicit performance guarantees and often treat only a single delay channel.

Problem Statement

Objective: Compute an optimal actuation command using CLF-SQP under state and input delays.

System and Optimization

  • Delay-aware system model with sensing and actuation delays
  • Optimization problem with box and rate constraints

Optimization Solver: SQP

Decision Variables

  • Control inputs and slack variables

Constraints and Bounds

  • Amplitude and rate limits
  • Rate limits referenced to previous command
  • CLF inequality constraint
  • No equality constraints

Objective

  • Quadratic cost with tracking and effort penalties

Warm Start

  • Initialize with the previous solution to reduce iterations

Algorithm

  • Predictor estimates the arrival state
  • CLF-QP computes control consistent with Lyapunov decrease
  • SQP solves a small QP each cycle

Simulation Setup

  • Teleoperation tracking scenario with known and unknown delays

Results and Discussion

True Delay Known

  • Stable tracking with CLF-QP
  • Constraint compliance under delay

True Delay Unknown

  • CLF-QP remains resilient to delay estimation bias
  • Predictive control preserves stability trends

Conclusion

  • Objective and guarantees: CLF-QP enforces Lyapunov decrease directly, providing an explicit stability certificate under delay.
  • Constraint handling: CLF-QP respects amplitude and rate limits through box constraints; PID saturates after the fact.
  • Delay handling: Both approaches use a predictor, but CLF-QP solves for commands consistent with the predicted arrival state and Lyapunov decrease.
  • Computation: The SQP-based QP is small and converges reliably at high update rates (e.g., 200 Hz).

Summary of Learning

  • Lyapunov-centric objective yields formal stability decay; slack controls strictness.
  • With PSD/PD weights and affine constraints, the QP remains convex and small.
  • Slack preserves feasibility and tunes the trade-off between stability and constraint satisfaction.

Future Work

  • Joint CLF-CBF optimization to enforce performance and safety simultaneously
  • Robustness to model uncertainty and disturbances
  • Extend from kinematic bicycle to nonlinear dynamics with adapted predictor and constraints

Acknowledgments

Thank you. Questions welcome.