Technology Category
Intelligent Robots: State EstimationReasoning under Uncertainty: Stochastic OptimizationSearch and Optimization: Mixed Discrete/Continuous Search
Observed Control -- Linearly Scalable Nonlinear Model Predictive Control with Adaptive Horizons
🤖 AI Summary
Model Predictive Control (MPC) suffers from high computational complexity in long horizons and difficulty guaranteeing closed-loop stability in short horizons. To address this, we propose the “Observed Control” framework, which exploits the rigorous duality between state estimation and MPC. It employs the Kalman smoother as a unified optimization backbone to explicitly decouple the reactive and predictive components of the control law. The framework supports online control synthesis for arbitrary horizon lengths and incorporates an adaptive optimization termination criterion to enable early convergence. By integrating extended or unscented Kalman filtering for nonlinear systems, the method ensures closed-loop stability while reducing computational complexity to linear in horizon length. Numerical experiments on nonlinear systems demonstrate its efficiency, scalability, and robustness—achieving real-time performance without sacrificing stability or prediction fidelity.
Technology Category
Application Category
📝 Abstract
This work highlights the duality between state estimation methods and model predictive control. A predictive controller, observed control, is presented that uses this duality to efficiently compute control actions with linear time-horizon length scalability. The proposed algorithms provide exceptional computational efficiency, adaptive time horizon lengths, and early optimization termination criteria. The use of Kalman smoothers as the backend optimization framework provides for a straightforward implementation supported by strong theoretical guarantees. Additionally, a formulation is presented that separates linear model predictive control into purely reactive and anticipatory components, enabling any-time any-horizon observed control while ensuring controller stability for short time horizons. Finally, numerical case studies confirm that nonlinear filter extensions, i.e., the extended Kalman filter and unscented Kalman filter, effectively extend observed control to nonlinear systems and objectives.
Problem
Research questions and friction points this paper is trying to address.
Efficiently compute control actions with linear scalability
Extend observed control to nonlinear systems and objectives
Ensure controller stability for short time horizons
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses Kalman smoothers for optimization framework
Enables linear time-horizon length scalability
Extends to nonlinear systems via filter extensions
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