Advanced Control Systems Optimization
64 papers on this topic
Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction
Learning-based approaches for controlling safety-critical systems are rapidly growing in popularity; thus, it is important to assure their performance and safety. Hamilton-Jacobi (HJ) reachability...
Time-Aware World Model for Adaptive Prediction and Control
In this work, we introduce the Time-Aware World Model (TAWM), a model-based approach that explicitly incorporates temporal dynamics. By conditioning on the time-step size, Δt, and training over a...
Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization
Optimizing complex and high-dimensional black-box functions is ubiquitous in science and engineering fields. Unfortunately, the online evaluation of these functions is restricted due to time and...
Learning Robust Model Predictive Control for Voltage Control of Islanded Microgrid
This paper proposes a novel control design for voltage tracking of an islanded AC microgrid in the presence of {nonlinear} loads and parametric uncertainties at the primary level of control. The...
Safe Planning in Dynamic Environments using Conformal Prediction
We propose a framework for planning in unknown dynamic environments with probabilistic safety guarantees using conformal prediction. Particularly, we design a model predictive controller (MPC)...
Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control
Diffusion-based models have recently shown strong performance in trajectory planning, as they are capable of capturing diverse, multimodal distributions of complex behaviors. A key limitation of...
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control
Determining the optimal cost function parameters of Model Predictive Control (MPC) to optimize multiple control objectives is a challenging and time-consuming task. Multiobjective Bayesian...
Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions
Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynamical system, and...
Probabilistic Constraint for Safety-Critical Reinforcement Learning
In this paper, we consider the problem of learning safe policies for probabilistic-constrained reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high...
Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation
Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-principles system...
Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
The expansion in automation of increasingly fast applications and low-power edge devices poses a particular challenge for optimization based control algorithms, like model predictive control. Our...
Combined Left and Right Temporal Robustness for Control under STL Specifications
Many modern autonomous systems, particularly multi-agent systems, are time-critical and need to be robust against timing uncertainties. Previous works have studied left and right time robustness...
Data-Driven Min-Max MPC for Linear Systems
Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are desired. In...
Standoff Tracking Using DNN-Based MPC with Implementation on FPGA
This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height. We propose a novel Lyapunov...
Agile Maneuvers in Legged Robots: a Predictive Control Approach
Planning and execution of agile locomotion maneuvers have been a longstanding challenge in legged robotics. It requires to derive motion plans and local feedback policies in real-time to handle...
Revisiting LQR Control from the Perspective of Receding-Horizon Policy Gradient
We revisit in this paper the discrete-time linear quadratic regulator (LQR) problem from the perspective of receding-horizon policy gradient (RHPG), a newly developed model-free learning framework...
Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning
Objective: The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However, designing an effective...
Adaptive Primal-Dual Method for Safe Reinforcement Learning
Primal-dual methods have a natural application in Safe Reinforcement Learning (SRL), posed as a constrained policy optimization problem. In practice however, applying primal-dual methods to SRL is...