Advanced Control Systems Optimization
64 papers on this topic
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...
Safe Controller Synthesis with Tunable Input-to-State Safe Control Barrier Functions
To bring complex systems into real world environments in a safe manner, they will have to be robust to uncertainties - both in the environment and the system. This paper investigates the safety of...
Inverse Optimal Safety Filters
CBF-QP safety filters are pointwise minimizers of the control effort at a given state vector, i.e., myopically optimal at each time instant. But are they optimal over the entire infinite time...
Adaptive Conformal Prediction for Motion Planning among Dynamic Agents
This paper proposes an algorithm for motion planning among dynamic agents using adaptive conformal prediction. We consider a deterministic control system and use trajectory predictors to predict...
Learning to Play Trajectory Games Against Opponents with Unknown Objectives
Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such...
Optimal Control of Connected Automated Vehicles with Event/Self-Triggered Control Barrier Functions
We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints. It has been shown that such problems can be...
Verification of Neural-Network Control Systems by Integrating Taylor Models and Zonotopes
We study the verification problem for closed-loop dynamical systems with neural-network controllers (NNCS). This problem is commonly reduced to computing the set of reachable states. When...
Combining model-predictive control and predictive reinforcement learning for stable quadrupedal robot locomotion
Stable gait generation is a crucial problem for legged robot locomotion as this impacts other critical performance factors such as, e.g. mobility over an uneven terrain and power consumption. Gait...
Predictive Control with Learning-Based Terminal Costs Using Approximate Value Iteration
Stability under model predictive control (MPC) schemes is frequently ensured by terminal ingredients. Employing a (control) Lyapunov function as the terminal cost constitutes a common choice....
Numerical Methods for Convex Multistage Stochastic Optimization
Optimization problems involving sequential decisions in a stochastic environment were studied in Stochastic Programming (SP), Stochastic Optimal Control (SOC) and Markov Decision Processes (MDP)....
Interval Markov Decision Processes with Continuous Action-Spaces
Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on...
ComTraQ-MPC: Meta-Trained DQN-MPC Integration for Trajectory Tracking with Limited Active Localization Updates
Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates -- the process by which the agent obtains its true...
Wasserstein Distributionally Robust Control of Partially Observable Linear Stochastic Systems
Distributionally robust control (DRC) aims to effectively manage distributional ambiguity in stochastic systems. While most existing works address inaccurate distributional information in fully...
Dimensionless Policies based on the Buckingham $\pi$ Theorem: Is This a Good Way to Generalize Numerical Results?
The answer to the question posed in the title is yes if the context (the list of variables defining the motion control problem) is dimensionally similar. This article explores the use of the...
Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks
Safety is a fundamental requirement of many robotic systems. Control barrier function (CBF)-based approaches have been proposed to guarantee the safety of robotic systems. However, the...
A Data-driven Method for Safety-critical Control: Designing Control Barrier Functions from State Constraints
This paper addresses the challenge of integrating explicit hard constraints into the control barrier function (CBF) framework for ensuring safety in autonomous systems, including robots. We...
Safe Whole-Body Task Space Control for Humanoid Robots
Complex robotic systems require whole-body controllers to deal with contact interactions, handle closed kinematic chains, and track task-space control objectives. However, for many applications,...
Time-Varying Convex Optimization: Time-Structured Algorithms and Applications
Optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms grounded on batch...