41 citations · 211 across the 45 of their papers we have counts for
21 papers · 1 filter
A Biologically-Inspired Computational Model of Time Perception
Inês Lourenço, Robert Mattila, Rodrigo Ventura +1
Time perception - how humans and animals perceive the passage of time - forms the basis for important cognitive skills such as decision-making, planning, and communication. In this…
Prediction-Based Leader-Follower Rendezvous Model Predictive Control with Robustness to Communication Losses
Dženan Lapandić, Christos K. Verginis, Dimos V. Dimarogonas +1
In this paper we propose a novel distributed model predictive control (DMPC) based algorithm with a trajectory predictor for a scenario of landing of unmanned aerial vehicles (UAVs…
Robust Trajectory Tracking for Underactuated Quadrotors with Prescribed Performance
Dženan Lapandić, Christos K. Verginis, Dimos V. Dimarogonas +1
We propose a control protocol based on the prescribed performance control (PPC) methodology for a quadrotor unmanned aerial vehicle (UAV). Quadrotor systems belong to the class of…
Learning Models of Model Predictive Controllers using Gradient Data
Rebecka Winqvist, Arun Venkitaraman, Bo Wahlberg
This paper investigates controller identification given data from a Model Predictive Controller (MPC) with constraints. We propose an approach for learning MPC that explicitly uses…
Aperiodic Communication for MPC in Autonomous Cooperative Landing
Dženan Lapandić, Linnea Persson, Dimos V. Dimarogonas +1
This paper investigates the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). Such heterogeneous a…
Cooperative System Identification via Correctional Learning
Inês Lourenço, Robert Mattila, Cristian R. Rojas +1
We consider a cooperative system identification scenario in which an expert agent (teacher) knows a correct, or at least a good, model of the system and aims to assist a learner-ag…
How to Protect Your Privacy? A Framework for Counter-Adversarial Decision Making
Inês Lourenço, Robert Mattila, Cristian R. Rojas +1
We consider a counter-adversarial sequential decision-making problem where an agent computes its private belief (posterior distribution) of the current state of the world, by filte…
Inverse Filtering for Hidden Markov Models with Applications to Counter-Adversarial Autonomous Systems
Robert Mattila, Cristian R. Rojas, Vikram Krishnamurthy +1
Bayesian filtering deals with computing the posterior distribution of the state of a stochastic dynamic system given noisy observations. In this paper, motivated by applications in…
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)
Inês Lourenço, Bo Wahlberg, Rodrigo Ventura
Time perception is the phenomenological experience of time by an individual. In this paper, we study how to replicate neural mechanisms involved in time perception, allowing robots…
What Did Your Adversary Believe? Optimal Filtering and Smoothing in Counter-Adversarial Autonomous Systems
Robert Mattila, Inês Lourenço, Vikram Krishnamurthy +2
We consider fixed-interval smoothing problems for counter-adversarial autonomous systems. An adversary deploys an autonomous filtering and control system that i) measures our curre…
Trajectory Generation using Sharpness Continuous Dubins-like Paths with Applications in Control of Heavy Duty Vehicles
Rui Oliveira, Pedro F. Lima, Marcello Cirillo +2
We present a trajectory generation framework for control of wheeled vehicles under steering actuator constraints. The motivation is smooth autonomous driving of heavy vehicles. The…
Computing monotone policies for Markov decision processes: a nearly-isotonic penalty approach
Robert Mattila, Cristian R. Rojas, Vikram Krishnamurthy +1
This paper discusses algorithms for solving Markov decision processes (MDPs) that have monotone optimal policies. We propose a two-stage alternating convex optimization scheme that…
Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming
Mogens Graf Plessen, Pedro F. Lima, Jonas Martensson +2
This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, obstacle avoidance, and vehicle dimension constraints. Starting from a nonline…
Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models
Robert Mattila, Cristian R. Rojas, Vikram Krishnamurthy +1
We consider estimating the transition probability matrix of a finite-state finite-observation alphabet hidden Markov model with known observation probabilities. The main contributi…
A Markov Decision Process Model to Guide Treatment of Abdominal Aortic Aneurysms
Robert Mattila, Antti Siika, Joy Roy +1
An abdominal aortic aneurysm (AAA) is an enlargement of the abdominal aorta which, if left untreated, can progressively widen and may rupture with fatal consequences. In this paper…
Approximate Regularization Paths for Nuclear Norm Minimization Using Singular Value Bounds -- With Implementation and Extended Appendix
Niclas Blomberg, Cristian R. Rojas, Bo Wahlberg
The widely used nuclear norm heuristic for rank minimization problems introduces a regularization parameter which is difficult to tune. We have recently proposed a method to approx…
Approximate Regularization Path for Nuclear Norm Based H2 Model Reduction
Niclas Blomberg, Cristian R. Rojas, Bo Wahlberg
This paper concerns model reduction of dynamical systems using the nuclear norm of the Hankel matrix to make a trade-off between model fit and model complexity. This results in a c…
Applications Oriented Input Design in Time-Domain Through Cyclic Methods
A. Ebadat, B. Wahlberg, H. Hjalmarsson +3
In this paper we propose a method for applications oriented input design for linear systems under time-domain constraints on the amplitude of input and output signals. The method g…
Application Set Approximation in Optimal Input Design for Model Predictive Control
Afrooz Ebadat, Mariette Annergren, Christian A. Larsson +2
This contribution considers one central aspect of experiment design in system identification. When a control design is based on an estimated model, the achievable performance is re…
On Optimal Input Design for Feed-forward Control
Per Hägg, Bo Wahlberg
This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is…
An ADMM Algorithm for Solving l_1 Regularized MPC
Mariette Annergren, Anders Hansson, Bo Wahlberg
We present an Alternating Direction Method of Multipliers (ADMM) algorithm for solving optimization problems with an l_1 regularized least-squares cost function subject to recursiv…