Output-Feedback Nonlinear Model Predictive Control with Iterative State- and Control-Dependent Coefficients

TL;DR

This paper extends nonlinear model predictive control (MPC) to systems with pseudo-linear form, using iterative quadratic programming and output feedback to handle complex nonlinear dynamics with constraints.

Contribution

It introduces an iterative quadratic programming approach for output-feedback nonlinear MPC with state- and control-dependent coefficients, enhancing control of complex nonlinear systems.

Findings

Successfully applied to Kapitza pendulum, nonholonomic integrator, and other systems.

Demonstrated improved constraint handling and control accuracy.

Validated effectiveness through numerical simulations.

Abstract

By optimizing the predicted performance over a receding horizon, model predictive control (MPC) provides the ability to enforce state and control constraints. The present paper considers an extension of MPC for nonlinear systems that can be written in pseudo-linear form with state- and control-dependent coefficients. The main innovation is to apply quadratic programming iteratively over the horizon, where the predicted state trajectory is updated based on the updated control sequence. Output-feedback control is facilitated by using the block-observable canonical form for linear, time-varying dynamics. This control technique is illustrated on various numerical examples, including the Kapitza pendulum with slider-crank actuation, the nonholonomic integrator, the electromagnetically controlled oscillator, and the triple integrator with control-magnitude saturation.