arXiv subjects

Anouck Girard

Publications and source records attributed to Anouck Girard.

48 records · Page 3Linked to original sources

arXiv2019

A Novel Approach for Optimal Trajectory Design with Multiple Operation Modes of Propulsion System, Part 2

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-valued inputs (power input to each engine in addition to the direction of thrust vector) and discrete variables (number of active engines). Each engine can be switched on/off independently and "optimal" operating power of each engine depends on the available solar power, which depends on the distance from the Sun. Application of the Composite Smooth Control (CSC) framework to a heliocentric fuel-optimal trajectory optimization from the Earth to the comet 67P/Churyumov-Gerasimenko is demonstrated, which presents a new approach to deal with multiple-engine problems. Operation of engine clusters with 4, 6, 10 and even 20 engines of the same type can be optimized. Moreover, engine clusters with different/mixed electric engines are considered with either 2, 3 or 4 different types of engines. Remarkably, the CSC framework allows us 1) to reduce the original multi-point boundary-value problem to a two-point boundary-value problem (TPBVP), and 2) to solve the resulting TPBVPs using a single-shooting solution scheme and with a random initialization of the missing costates. While the approach we present is a continuous neighbor of the discontinuous extremals, we show that the discontinuous necessary conditions are satisfied in the asymptotic limit. We believe this is the first indirect method to accommodate a multi-mode control of this level of complexity with realistic engine performance curves. The results are interesting and promising for dealing with a large family of such challenging multi-mode optimal control problems.

math.OC↗

arXiv2019

Spacecraft Relative Motion Planning Using Chained Chance-Constrained Admissible Sets

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 constrained relative motion approach based on chained positively invariant sets to the case where the spacecraft dynamics are controlled using output feedback on noisy measurements and are subject to stochastic disturbances. It is shown that non-convex polyhedral exclusion zone constraints can be handled. The methodology consists of a virtual net of static equilibria nodes in the Clohessy-Wiltshire-Hill frame. Connectivity between nodes is determined through the use of chance-constrained admissible sets, guaranteeing that constraints are met with a specified probability.

eess.SY↗

arXiv2019

Co-optimization of Speed and Gearshift Control for Battery Electric Vehicles Using Preview Information

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 efficiency, a multi-speed transmission is employed, whose control involves discrete-valued gearshift signals. To overcome the computational difficulties in solving the integrated speed-and-gearshift optimal control problem that involves both continuous and discrete-valued optimization variables, we propose a hierarchical procedure to decompose the integrated hybrid problem into purely continuous and discrete sub-problems, each of which can be efficiently solved. We show, by simulations in various driving scenarios, that the co-optimization of speed and gearshift control using our proposed hierarchical procedure can achieve greater energy efficiency than other typical approaches.

eess.SY↗

arXiv2019

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. We model the interactions between the ego agent and its operating environment as a two-player dynamic game, and integrate cognitive behavioral models, Bayesian inference, and receding-horizon optimal control to define a dynamically-evolving decision strategy for the ego agent. Simulation examples representing autonomous vehicle control in three traffic scenarios where the autonomous ego vehicle interacts with a human-driven vehicle are reported.

cs.AI↗

arXiv2019

A Reference Governor for Nonlinear Systems with Disturbance Inputs Based on Logarithmic Norms and Quadratic Programming

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 system by the response of a linear model with a set-bounded prediction error, where a state-and-input dependent bound on the prediction error is explicitly characterized using logarithmic norms. The online optimization is reduced to a convex quadratic program with linear inequality constraints. Two numerical examples are reported.

eess.SY↗

arXiv2019

Detection-averse optimal and receding-horizon control for Markov decision processes

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. After formulating the problem, we first propose a value iteration (VI) approach to solve it. To overcome the "curse of dimensionality" and thus gain scalability to larger-sized problems, we then propose a receding-horizon optimization (RHO) approach to obtain approximate solutions. We use examples to illustrate and compare the VI and RHO approaches, and to show the potential of our problem formulation for practical applications.

eess.SY↗

arXiv2019

Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections

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 vehicles, we propose a framework for modeling vehicle interactions at uncontrolled intersections. The proposed interaction modeling approach is based on game theory with multiple concurrent leader-follower pairs, and accounts for common traffic rules. We parameterize the intersection layouts and geometries to model uncontrolled intersections with various configurations, and apply the proposed approach to model the interactive behavior of vehicles at these intersections. Based on simulation results in various traffic scenarios, we show that the model exhibits reasonable behavior expected in traffic, including the capability of reproducing scenarios extracted from real-world traffic data and reasonable performance in resolving traffic conflicts. The model is further validated based on the level-of-service traffic quality rating system and demonstrates manageable computational complexity compared to traditional multi-player game-theoretic models.

eess.SY↗

arXiv2019

An analytical safe approximation to joint chance-constrained programming with additive Gaussian noises

We propose a safe approximation to joint chance-constrained programming where the constraint functions are additively dependent on a normally-distributed random vector. The approximation is analytical, meaning that it requires neither numerical integrations nor sampling-based probability approximations. Under mild assumptions, the approximation is a standard nonlinear program. We compare this new safe approximation to another analytical safe approximation for joint chance-constrained programming based on Boole's inequality through two examples representing the constrained control of linear Gaussian-Markov models. It is shown that our proposed safe approximation has a lower degree of conservatism compared to the one based on Boole's inequality.

math.OC↗

arXiv2018

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. The algorithm is based on a game-theoretic model representing the interactions between the ego vehicle and an opponent vehicle, and adapts to an online estimated driver type of the opponent vehicle. Simulation results are reported.

cs.GT↗

arXiv2017

Attitude Control of a 2U Cubesat by Magnetic and Air Drag Torques

This paper describes the development of a magnetic attitude control subsystem for a 2U cubesat. Due to the presence of gravity gradient torques, the satellite dynamics are open-loop unstable near the desired pointing configuration. Nevertheless the linearized time-varying system is completely controllable, under easily verifiable conditions, and the system's disturbance rejection capabilities can be enhanced by adding air drag panels exemplifying a beneficial interplay between hardware design and control. In the paper, conditions for the complete controllability for the case of a magnetically controlled satellite with passive air drag panels are developed, and simulation case studies with the LQR and MPC control designs applied in combination with a nonlinear time-varying input transformation are presented to demonstrate the ability of the closed-loop system to satisfy mission objectives despite disturbance torques.

math.OC↗

arXiv2016

Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems

Autonomous driving has been the subject of increased interest in recent years both in industry and in academia. Serious efforts are being pursued to address legal, technical and logistical problems and make autonomous cars a viable option for everyday transportation. One significant challenge is the time and effort required for the verification and validation of the decision and control algorithms employed in these vehicles to ensure a safe and comfortable driving experience. Hundreds of thousands of miles of driving tests are required to achieve a well calibrated control system that is capable of operating an autonomous vehicle in an uncertain traffic environment where multiple interactions between vehicles and drivers simultaneously occur. 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. In this paper, we present a game theoretic traffic model that can be used to 1) test and compare various autonomous vehicle decision and control systems and 2) calibrate the parameters of an existing control system. We demonstrate two example case studies, where, in the first case, we test and quantitatively compare two autonomous vehicle control systems in terms of their safety and performance, and, in the second case, we optimize the parameters of an autonomous vehicle control system, utilizing the proposed traffic model and simulation environment.

cs.AI↗

arXiv2016

Reference Governor Strategies for Vehicle Rollover Avoidance

The paper addresses the problem of vehicle rollover avoidance using reference governors applied to modify the driver steering input in vehicles with an active steering system. Several reference governor designs are presented and tested with a detailed nonlinear simulation model. The vehicle dynamics are highly nonlinear for large steering angles, including the conditions where the vehicle approaches a rollover onset, which necessitates reference governor design changes. Simulation results show that reference governor designs are effective in avoiding rollover. The results also demonstrate that the controllers are not overly conservative, adjusting the driver steering input only for very high steering angles.

eess.SY↗