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Related Articles from SNS
Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation
arXiv:2606.05687v1 Announce Type: new Abstract: In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (RL) enables robust whole-body skills through large-scale simulation. However, using MPC inside RL often requires time-consuming problem construction or excessive training overhead, making such frameworks difficult to justify in practice.
A Data-Driven Methodology for Scalable Distributed MPC in Heterogeneous Building Aggregation: From Systematic Feature Selection to Convex Optimization
arXiv:2605.30763v1 Announce Type: new Abstract: Coordinating large-scale, heterogeneous building aggregations for demand response (DR) is impeded by a dual challenge: the computational intractability of centralized Model Predictive Control (MPC) and the inadequacy of conventional feature selection methods, which fail to address the error-compounding nature of multi-step forecasting required by MPC. This paper proposes a comprehensive, data-driven framework that first employs a systematic,...
Right Model, Right Time: Real-Time Cascaded-Fidelity MPC for Bipedal Walking
arXiv:2605.04607v2 Announce Type: replace Abstract: This paper presents a multi-phase whole-body model predictive control (MPC) approach for bipedal walking, combining a detailed whole-body model in the near horizon with a simplified single-rigid-body model in the later prediction steps. This reduces computational complexity while retaining prediction capabilities. The resulting nonlinear optimal control problem is solved entirely within the general-purpose, off-the-shelf nonlinear MPC...
Deterministic Distance Approximation in MPC via Improved Hitting Sets
Announce Type: new Abstract: In this paper, we provide the first deterministic algorithms with sublogarithmic round complexity for spanners and approximate shortest paths in various MPC models. Moreover, we significantly improve upon the state of the art in the deterministic Congested Clique. In particular, we obtain the following four results on undirected graphs: 1.
Situation-Aware Interactive MPC Switching for Autonomous Driving
arXiv:2512.06182v2 Announce Type: replace Abstract: Autonomous driving in interactive traffic scenarios remains challenging because of the mutual influence among vehicles and the inherent uncertainty of surrounding agents. Several model predictive control (MPC) formulations have been proposed to address this challenge, each adopting a different model of inter-agent interaction. While higher-fidelity interaction models enable more intelligent behavior, they incur substantially greater...
Artificial-reference tracking MPC with probabilistically validated performance on industrial embedded systems
arXiv:2511.03603v2 Announce Type: replace Abstract: Industrial embedded systems are typically used to execute simple control algorithms due to their low computational resources. Despite these limitations, the implementation of advanced control techniques such as Model Predictive Control (MPC) has been explored by the control community in recent years, typically considering simple linear formulations or explicit ones to facilitate the online computation of the control input. These...
Impedance MPC for Physical Human-Robot Interaction: Predictive Disturbance Rejection with Joint-Limit Safety
arXiv:2606.08281v1 Announce Type: new Abstract: Physical human-robot interaction (pHRI) demands simultaneous trajectory accuracy and compliant safety under unplanned contact. Classical impedance control incurs a nonzero steady-state position error under sustained human force -- the applied force divided by the task stiffness -- which integral action reduces only within a narrow stable-gain budget. We present a two-layer Impedance MPC that resolves this tension.
Data-Driven Min-Max MPC with Integral Quadratic Constraints
Announce Type: new Abstract: Data-driven control of nonlinear systems with rigorous guarantees is a challenging control problem. Integral quadratic constraints (IQCs) provide a powerful framework for modeling nonlinearities. This paper presents a data-driven min-max model predictive control (MPC) synthesis method for unknown systems subject to (nonlinear) uncertainties using the IQC framework.
RBI MPC may hike inflation forecast, trim growth rate
While the majority of forecasters and market participants expect Reserve Bank of India's Monetary Policy Committee meeting to vote for a status quo on interest rates, the forthcoming MPC statement on June 5 will be observed minutely. With disruptions owing to the West Asia conflict now approaching 100 days, this is no longer a short-term disturbance that the central bank can look through. It will now have to factor in the impact of the crisis into its growth and inflation forecasts.
MPC for nonlinear systems: a comparative review of discretization methods
Electrical Engineering and Systems Science > Systems and Control [Submitted on 4 Jun 2026] Title:MPC for nonlinear systems: a comparative review of discretization methods View PDF HTML (experimental)Abstract:This work provides a comparative review of three different numerical methods generally used to discretize continuous-time non-linear equations appearing in model predictive control problems: direct multiple shooting, direct collocation and successive linearizations. An overview of the...