A System Level Approach to Discrete-Time Nonlinear Systems
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Abstract (arXiv)
We will show that there is a universal connection between the achievable closed-loop dynamics and the corresponding feedback controller that produces it. This connection shows promise to lead to new methods for robust nonlinear control in discrete-time. We will show that, given a causal nonlinear discrete-time system and controller, the resulting closed-loop is a solution to a nonlinear operator equation. Conversely, any causal solution to the nonlinear operator equation is a closed-loop that can be achieved by some causal controller. Moreover, solutions can be substituted into a simple dynamic controller structure, which we will refer to as a system level controller, to obtain an implementation of the unique corresponding feedback controller. System level controllers could be an attractive approach for robust nonlinear control, as we will show that even when they are parametrized with approximate solutions to the operator equation, they can still produce robustly stable closed loops. We will provide theoretical results that state how grade of approximation and robust stability of the closed loop are related. Additionally, we will explore some first applications of our results. Using the cart-pole system as an illustrative example, we will derive how to design robust discrete-time trajectory tracking controllers for continuous-time nonlinear systems. Secondly, we will introduce a particular class of system level controller that shows to be particularly useful for linear systems with actuator saturation and state constraints. The special structure of the controller allows for simple stability and performance analysis of the closed-loop in presence of disturbances. A particular application to large-scale systems with actuator saturation and safety constraints is presented in our companion paper.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2022Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systemscited by 10
- 2024Learning to Boost the Performance of Stable Nonlinear Systemscited by 7
- 2025React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-RENcited by 1
- 2026Stability-Preserving Online Adaptation of Neural Closed-loop Mapscited by 0
- 2026Stability-Preserving Online Adaptation of Neural Closed-loop Maps23 Mar 2026
- 2025React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN2 Jun 2025
- 2024Learning to Boost the Performance of Stable Nonlinear Systems1 May 2024
- 2022Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems22 Mar 2022