82 citations · 486 across the 90 of their papers we have counts for
21 papers · 1 filter
Survey of prognostics methods for condition-based maintenance in engineering systems
Ehsan Taheri, Ilya Kolmanovsky, Oleg Gusikhin
It is not surprising that the idea of efficient maintenance algorithms (originally motivated by strict emission regulations, and now driven by safety issues, logistics and customer…
Rapid Uncertainty Propagation and Chance-Constrained Path Planning for Small Unmanned Aerial Vehicles
Andrew W. Berning, Anouck Girard, Ilya Kolmanovsky +1
With the number of small Unmanned Aircraft Systems (sUAS) in the national airspace projected to increase in the next few years, there is growing interest in a traffic management sy…
A Novel Approach for Optimal Trajectory Design with Multiple Operation Modes of Propulsion System, Part 2
Ehsan Taheri, John L. Junkins, Ilya Kolmanovsky +1
Equipping a spacecraft with multiple solar-powered electric engines (of the same or different types) compounds the task of optimal trajectory design due to presence of both real-va…
A Novel Approach for Optimal Trajectory Design with Multiple Operation Modes of Propulsion System, Part 1
Ehsan Taheri, John L. Junkins, Ilya Kolmanovsky +1
Efficient performance of a number of engineering systems is achieved through different modes of operation - yielding systems described as "hybrid", containing both real-valued and…
Spacecraft Relative Motion Planning Using Chained Chance-Constrained Admissible Sets
Andrew W. Berning, Nan I. Li, Anouck Girard +3
With the increasing interest in proximity and docking operations, there is a growing interest in spacecraft relative motion control. This paper extends a previously proposed constr…
Co-optimization of Speed and Gearshift Control for Battery Electric Vehicles Using Preview Information
Kyoungseok Han, Nan Li, Ilya Kolmanovsky +4
This paper addresses the co-optimization of speed and gearshift control for battery electric vehicles using short-range traffic information. To achieve greater electric motor effic…
Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving
Ali Baheri, Subramanya Nageshrao, H. Eric Tseng +3
In this paper, we present a safe deep reinforcement learning system for automated driving. The proposed framework leverages merits of both rule-based and learning-based approaches…
Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation
Ran Tian, Nan Li, Ilya Kolmanovsky +2
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles. Their planning and control systems need extensive testing, includin…
Model Reference Adaptive Control Allocation for Constrained Systems with Guaranteed Closed Loop Stability
Seyed Shahabaldin Tohidi, Yildiray Yildiz, Ilya Kolmanovsky
This paper proposes an adaptive control allocation approach for uncertain over-actuated systems with actuator saturation. The proposed method does not require uncertainty estimatio…
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control
Sisi Li, Nan Li, Anouck Girard +1
In this paper, we describe an integrated framework for autonomous decision making in a dynamic and interactive environment. We model the interactions between the ego agent and its…
Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning
Ran Tian, Nan Li, Ilya Kolmanovsky +1
It is a long-standing goal of artificial intelligence (AI) to be superior to human beings in decision making. Games are suitable for testing AI capabilities of making good decision…
A Reference Governor for Nonlinear Systems with Disturbance Inputs Based on Logarithmic Norms and Quadratic Programming
Nan Li, Ilya Kolmanovsky, Anouck Girard
This note describes a reference governor design for a continuous-time nonlinear system with an additive disturbance. The design is based on predicting the response of the nonlinear…
Detection-averse optimal and receding-horizon control for Markov decision processes
Nan Li, Ilya Kolmanovsky, Anouck Girard
In this paper, we consider a Markov decision process (MDP), where the ego agent has a nominal objective to pursue while needs to hide its state from detection by an adversary. Afte…
Sensitivity-based Warmstarting for Nonlinear Model Predictive Control with Polyhedral State and Control Constraints
Dominic Liao-McPherson, Marco M. Nicotra, Asen L. Dontchev +2
Model predictive control (MPC) is of increasing interest in applications for constrained control of multivariable systems. However, one of the major obstacles to its broader use is…
A Semismooth Predictor Corrector Method for Suboptimal Model Predictive Control
Dominic Liao-McPherson, Marco Nicotra, Ilya Kolmanovsky
Suboptimal model predictive control is a technique that can reduce the computational cost of model predictive control (MPC) by exploiting its robustness to incomplete optimization.…
Spacecraft Attitude Control with Nonconvex Constraints: An Explicit Reference Governor Approach
Marco Nicotra, Dominic Liao-McPherson, Laurent Burlion +1
This paper introduces a novel attitude controller for spacecraft subject to actuator saturation and multiple exclusion cone constraints. The proposed solution relies on a two-layer…
Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections
Nan Li, Yu Yao, Ilya Kolmanovsky +2
Motivated by the need to develop simulation tools for verification and validation of autonomous driving systems operating in traffic consisting of both autonomous and human-driven…
An analytical safe approximation to joint chance-constrained programming with additive Gaussian noises
Nan Li, Ilya Kolmanovsky, Anouck Girard
We propose a safe approximation to joint chance-constrained programming where the constraint functions are additively dependent on a normally-distributed random vector. The approxi…
Time Distributed Optimization for Model Predictive Control: Stability, Robustness, and Constraint Satisfaction
Dominic Liao-McPherson, Marco Nicotra, Ilya Kolmanovsky
Time distributed optimization is an implementation strategy that can significantly reduce the computational burden of model predictive control by exploiting its robustness to incom…
Quad-rotor Flight Simulation in Realistic Atmospheric Conditions
Behdad Davoudi, Ehsan Taheri, Karthik Duraisamy +2
In trajectory planning and control design for unmanned air vehicles, highly simplified models are typically used to represent the vehicle dynamics and the operating environment. Th…
FBstab: A Stabilized Semismooth Quadratic Programming Algorithm with Applications in Model Predictive Control
Dominic Liao-McPherson, Ilya Kolmanovsky
This paper introduces the proximally stabilized Fischer-Burmeister method (FBstab); a new algorithm for convex quadratic programming that synergistically combines the proximal poin…