Deep brain stimulation (DBS) is an invasive therapy broadly used to treat the symptoms of neurological and mental diseases. DBS is currently performed by means of surgically implanted multi-contact electrodes delivering electrical stimulation to well-defined targets in the brain of the patient. The therapeutical effect is much dependent on the individual and can be improved by selecting suitable stimulation settings such as amplitude, frequency, and the signal form of the stimuli pulse train. Insufficient stimulation of the target area does not properly alleviate the symptoms of the treated disease, while overstimulation is prone to undesirable side effects.
A major complication on the way of the individualization and optimization of the DBS therapy is the fact that the biological mechanism behind its therapeutical effect is basically unknown. Selecting the stimulation parameters by medical personnel in a trial-and-error procedure takes up to several months and numerous visits to the clinic.
A significant progress in the computer-assisted individualization and optimization of DBS has been recently made. The talk reviews the mathematical models that describe how the electrical pulses emitted by the electrode propagate through the brain tissue and interact with neural populations. Provided measurements of the local potential are available from the electrode, model-based estimation of the electrical properties of the tissue around the electrode can be performed. The problem of target coverage and stimuli spill minimization is cast as a spatial control problem and solved by optimization. The technology for symptoms quantification in neurological diseases is also reviewed. Finally, an outlook on the prospects of of the individualized DBS therapies is offered.
While robots are already doing a wonderful job as factory workhorses, they are now gradually appearing in our daily environments and offering their services as autonomous cars, delivery drones, helpers in search and rescue and much more.
For fast search & rescue or inspection of complex environments, flying robots are probably the most efficient and versatile devices. However, the limited flight time and payload, as well as the restricted computing power of drones renders autonomous operations quite challenging.
This talk will focus on the design and autonomous navigation of flying robots. Innovative designs of flying systems, from novel concepts of omni-directional multi-copters and blimps to solar airplanes for continuous flights are presented. Recent results of visual and laser based navigation (localization, mapping, planning) in GPS denied environments are showcased and discussed. Performance and potential applications are presented.
Passivity theory is one of the cornerstones of control theory, providing a rich framework for analyzing properties of dynamical systems. The compositional properties of passive systems make them especially powerful in the study of multi-agent systems and cooperative control. In this talk we will explore necessary and sufficient conditions for a network of passive dynamical systems to reach an output agreement, i.e., the trajectories of each system will synchronize. The leads to a refinement of classical passivity theory that we term maximal equilibrium passivity. We then show that the steady-state behavior of these systems are in fact solutions to a family of classic network optimization problems, and as a result we draw connections between notions of duality in static optimization to cooperative control. This network optimization perspective also leads to synthesis methods for controllers to guarantee the desired behavior of the network and provides new insights to classical problems such as feedback passivation.
Approximate dynamic programming (ADP) is a powerful approach to construct near-optimal control inputs for general nonlinear dynamical systems and general cost functions. ADP plays a key role in various domains such as optimal control, reinforcement learning, and operational research, and the related literature largely concentrates on optimality. The question of the stability of the controlled system is often ignored, while it is essential in control engineering.
In this talk, we will discuss the interplay between (near-)optimality and stability for systems controlled by ADP and how both can help each other. First, we will start by presenting results that ensure stability properties for such closed-loop systems. Second, once stability is established, we will see that, in return, existing near-optimality bounds found in the ADP literature can be drastically improved. Third, we will show that stability requirements can also be exploited in the optimization algorithm itself, which helps reducing its computational complexity, thus favoring its practical implementation.
Aircraft mobility plays an important role in our life style and societal organisation. While this transportation mean is faced to severe environmental changes and safety constraints, research must provides answers applicable in an industrial context. Since some decades, civil aircraft industry did rely - among others - on dynamical systems and control theory advances to address these issues. Indeed, recent developments in these communities did play a important role in the aircraft efficiency and safety improvement, as well as footprint reduction.
This talk aims at illustrating how three "control-oriented" aspects impact this field and led to great improvements. More specifically, (i) the linear large-scale dynamical model approximation, (ii) the linear control design, and (iii) the uncertain modelling robustness analysis will be presented. The talk will illustrate how these developments benefit to the - constrained - industrial civil aircraft environment and fit to practitioners needs. Numerical, practical and experimental aspects will stand as the cornerstone of the presentation.
Photo credits: Palais des Congrès de Saint-Raphaël