A nonlinear tracking model predictive control scheme for dynamic target signals

TL;DR

This paper introduces a nonlinear MPC scheme that combines stabilization and dynamic trajectory planning to effectively track changing target signals while satisfying constraints, with an innovative offline computation for terminal ingredients.

Contribution

It proposes a unified nonlinear MPC framework with online terminal set optimization and a novel offline method for terminal ingredients, improving tracking and computational efficiency.

Findings

Demonstrates superior performance on benchmark examples.

Ensures exponential stability for periodic target signals.

Reduces computational demand through decoupled optimization.

Abstract

We present a nonlinear model predictive control (MPC) scheme for tracking of dynamic target signals. The scheme combines stabilization and dynamic trajectory planning in one layer, thus ensuring constraint satisfaction irrespective of changes in the dynamic target signal. For periodic target signals we ensure exponential stability of the optimal reachable periodic trajectory using suitable terminal ingredients and a convexity condition for the underlying periodic optimal control problem. Furthermore, we introduce an online optimization of the terminal set size to automate the trade-off between fast convergence and operation close to the constraints. In addition, we show how stabilization and dynamic trajectory planning can be formulated as partially decoupled optimization problems, which reduces the computational demand while ensuring recursive feasibility and convergence. The main tool to enable the proposed design is a novel reference generic offline computation that provides suitable terminal ingredients for tracking of dynamic reference trajectories. The practicality of this approach is demonstrated on benchmark examples, which demonstrates superior performance compared to state of the art approaches.