Optimal nonlinear control with AI techniques

Optimal control can model and address practical problems appearing in a wide variety of fields, such as automatic control, artificial intelligence, operations research, economics, medicine, etc. In these problems, a nonlinear dynamical system must be controlled so as to optimize a cumulative performance index over time. While optimal solutions have been theoretically characterized starting in the 1950s, computational methods to find them are still a challenging, open area of research.

In this research direction, we focus on the development of methods originating from artificial intelligence and their usage in automatic control. In particular, we are investigating optimistic planning and reinforcement learning techniques, developing fundamental lines like complexity analysis on the one hand, and on the other hand adapting the techniques to solve to open problems in nonlinear control, such as cooperative multiagent control or networked control systems. On the application side, we are investigating the application of these methods to the control of mobile robotic assistants.

Our open and ongoing projects in this area are listed below, together with a selection of completed projects where relevant.

PHC Brancusi grant: Artificial-Intelligence-Based Optimization for the Stable and Optimal Control of Networked Systems (AICONS)

The optimal operation of communication, energy, transport, and other networks is of paramount importance in today's society, and will certainly become more important in the future. Operating these networks optimally requires the effective control of their component systems. Our project AICONS therefore focuses on the control of general networked systems. We consider both the coordinated behavior of multiple systems having a local view of the network, as well as the networked control of individual systems where new challenges arise from the limitations of the network.

AUF-RO grant: AI methods for the networked control of assistive UAVs (NETASSIST)

This project develops methods for the networked control and sensing for a team of unmanned, assistive aerial vehicles that follows a group of vulnerable persons. On the control side, we consider multiagent and consensus techniques, while on the vision side the focus is egomotion estimation of the UAVs and cooperative tracking of persons with filtering techniques. NETASSIST is an international cooperation project involving the Technical University of Cluj-Napoca in Romania, the University of Szeged in Hungary, and the University of Lorraine at Nancy, France.

Robot, bring me... my glasses: Mixed air and ground assistive teams

Robots that assist elderly or disabled persons, or even anyone in their day-to-day tasks, can lead to a huge improvement in quality of life. At ROCON we are pursuing domestic mobile manipulators, as well as UAVs for monitoring the persons. Our next goal is to integrate these two platforms into an overall framework that will both monitor the persons and assist them on the ground.

Optimistic planning with constant control over multiple time steps (OSP)

Optimistic Planning for Deterministic Systems (OPD) is an algorithm designed to deal with very general control problems, usually at high computational costs. However, there exist specific classes of “simpler” problems to which OPD can be adapted, reaching good performance with less computation than the original OPD. This project focuses on one such type of adaptation: namely, to extend the OPD planning algorithm to the class of control problems that prefers long ranges of repeated actions.

Young Teams grant: Reinforcement learning and planning for large-scale systems

Many controlled systems, such as robots in open environments, traffic and energy networks, etc. are large-scale: they have many continuous variables. Such systems may also be nonlinear, stochastic, and impossible to model accurately. Optimistic planning (OP) is a recent paradigm for general nonlinear and stochastic control, which works when a model is available; reinforcement learning (RL) additionally works model-free, by learning from data. However, existing OP and RL methods cannot handle the number of continuous variables required in large-scale systems.

Nonlinear control for commercial drones in autonomous railway maintenance

Drones are getting widespread and low-cost platforms already offer good flight and video recording experience. This project intends to use such drones in the context of railway maintenance by developing applications for autonomous navigation in railway environment.

Optimistic planning for nonlinear control applications

Optimistic planning is a recent approach to the predictive control of nonlinear systems, which optimistically explores the space of action sequences from the current state. Due to generality in the dynamics and objective functions that it can address, it has a wide range of potential applications to problems in nonlinear control.

Intelligent Sumo Robots

The Sumo Robot contest is an annual event at our faculty. Each year more and more students are interested and join in. In this project, we will start from existing sumo robot hardware (preferably the student's robot!), with the goal of developing better software, so that it could beat more expensive robots by simply being a "smarter" fighter. This will be achieved with a combination of machine learning (e.g. about the oponnent) and optimal trajectory control.

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