Search Results for author:
Found 35 papers, 2 papers with code
Autonomous Driving With Perception Uncertainties: Deep-Ensemble Based Adaptive Cruise Control
In the scenario of Adaptive Cruise Control (ACC), we employ the Deep Ensemble to estimate distance headway to the lead vehicle from RGB images and enable the downstream controller to account for the estimation uncertainty.
Nonlinear Model Predictive Detumbling of Small Satellites with a Single-axis Magnetorquer
Magnetic torquers have been extensively investigated for momentum management of spacecraft with momentum wheels and for nutation damping of spin satellites, momentum-biased, and dual-spin satellites.
Less Conservative Robust Reference Governors and Their Applications
The applications of reference governors to systems with unmeasured set-bounded disturbances can lead to conservative solutions.
System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models
The Neural Network (NN), as a black-box function approximator, has been considered in many control and robotics applications.
Minimum-Time Trajectory Optimization With Data-Based Models: A Linear Programming Approach
In this paper, we develop a computationally-efficient approach to minimum-time trajectory optimization using input-output data-based models, to produce an end-to-end data-to-control solution to time-optimal planning/control of dynamic systems and hence facilitate their autonomous operation.
Game Projection and Robustness for Game-Theoretic Autonomous Driving
This paper defines the robustness margin of a game solution as the maximum magnitude of cost function deviations that can be accommodated in a game without changing the optimality of the game solution.
Modeling and Control of Diesel Engine Emissions using Multi-layer Neural Networks and Economic Model Predictive Control
This paper presents the results of developing a multi-layer Neural Network (NN) to represent diesel engine emissions and integrating this NN into control design.
Model Predictive Control of Diesel Engine Emissions Based on Neural Network Modeling
The developments described in the paper are based on a high-fidelity model of the engine airpath and torque response in GT-Power, which is extended with a feedforward neural network (FNN)-based model of engine out (feedgas) emissions identified from experimental engine data to enable the controller co-simulation and performance verification.
Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network
Autonomous vehicles need to accomplish their tasks while interacting with human drivers in traffic.
Robust Model Predictive Control for Enhanced Fast Charging on Electric Vehicles through Integrated Power and Thermal Management
This paper explores the synergies between integrated power and thermal management (iPTM) and battery charging in an electric vehicle (EV).
A Comparison between Markov Chain and Koopman Operator Based Data-Driven Modeling of Dynamical Systems
The second part of this paper introduces these models and their corresponding control design methods.
Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors
Understanding the intention of vehicles in the surrounding traffic is crucial for an autonomous vehicle to successfully accomplish its driving tasks in complex traffic scenarios such as highway forced merging.
Robust Adaptive MPC Using Uncertainty Compensation
The performance bounds provided by the L1AC are then used to tighten the state and control constraints of the actual system, and a model predictive controller is designed for the nominal system with the tightened constraints.
Constrained reaction wheel desaturation and attitude control of spacecraft with four reaction wheels
The paper addresses a problem of constrained spacecraft attitude stabilization with simultaneous reaction wheel (RW) desaturation.
Scheduling Dosage of Proton Pump Inhibitors Using Constrained Optimization With Gastric Acid Secretion Model
Dosage schedule of the Proton Pump Inhibitors (PPIs) is critical for gastric acid disorder treatment.
On Complexity Bounds for the Maximal Admissible Set of Linear Time-Invariant Systems
Given a dynamical system with constrained outputs, the maximal admissible set (MAS) is defined as the set of all initial conditions such that the output constraints are satisfied for all time.
Safe Control and Learning Using Generalized Action Governor
This paper introduces the Generalized Action Governor, which is a supervisory scheme for augmenting a nominal closed-loop system with the capability of strictly handling constraints.
Reference Governor for Input-Constrained MPC to Enforce State Constraints at Lower Computational Cost
In this paper, a control scheme is developed based on an input constrained Model Predictive Controller (MPC) and the idea of modifying the reference command to enforce constraints, usual of Reference Governors (RG).
Integrated Adaptive Control and Reference Governors for Constrained Systems with State-Dependent Uncertainties
The proposed framework leverages an L1 adaptive controller (L1AC) that estimates and compensates for the uncertainties, and provides guaranteed transient performance, in terms of uniform bounds on the error between actual states and inputs and those of a nominal (i. e., uncertainty-free) system.
Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach
To address the challenges caused by the complexity in solving a multi-player game and by the requirement of real-time operation, a potential game (PG) based decision-making framework is developed.
Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning
The action governor is an add-on scheme to a nominal control loop that monitors and adjusts the control actions to enforce safety specifications expressed as pointwise-in-time state and control constraints.
Benefits of Feedforward for Model Predictive Airpath Control of Diesel Engines
Two options for the feedforward are considered one based on a look-up table that specifies the feedforward as a function of engine speed and fuel injection rate, and another one based on a (non-rate-based) MPC that generates dynamic feedforward trajectories.
Potential Game-Based Decision-Making for Autonomous Driving
Statistical comparative studies, including 1) finite potential game vs. continuous potential game, and 2) best response dynamics vs. potential function optimization, are conducted to compare the performances of different solution algorithms.
Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios
Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into.
A Reference Governor for linear systems with polynomial constraints
The paper considers the application of reference governors to linear discrete-time systems with constraints given by polynomial inequalities.
Development, Implementation, and Experimental Outdoor Evaluation of Quadcopter Controllers for Computationally Limited Embedded Systems
Quadcopters are increasingly used for applications ranging from hobby to industrial products and services.
Safe Reinforcement Learning Using Robust Action Governor
Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process.
Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance
This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable, and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed.
Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles.
Robotics Systems and Control Systems and Control
Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning
It is a long-standing goal of artificial intelligence (AI) to be superior to human beings in decision making.
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control
In this paper, we describe an integrated framework for autonomous decision making in a dynamic and interactive environment.
Quad-rotor Flight Simulation in Realistic Atmospheric Conditions
A reduced-order version of the atmospheric boundary layer data as well as the popular Dryden model are used to assess the impact of accuracy of the wind field model on the predicted vehicle performance and trajectory.
Fluid Dynamics Atmospheric and Oceanic Physics Applied Physics
FBstab: A proximally stabilized semismooth algorithm for convex quadratic programming
This paper introduces the proximally stabilized Fischer-Burmeister method (FBstab); a new algorithm for convex quadratic programming which synergistically combines the proximal point algorithm with a semismooth Newton-type method.
Optimization and Control 90C20, 49M15, 65K05, 65K10
Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts
In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection.
Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems
Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests.
