The frequency-domain data of a multivariable system in different operating points is used to design a robust controller with respect to the measurement noise and multimodel uncertainty. The controller is fully parametrized in terms of matrix polynomial functions and can be formulated as a centralized, decentralized or distributed controller. All standard performance specifications like H2, H∞ and loop shaping are considered in a unified framework for continuous-and discrete-time systems. The control problem is formulated as a convex-concave optimization problem and then convexified by linearization of the concave part around an initial controller. The performance criterion converges monotonically to a local optimal solution in an iterative algorithm. The effectiveness of the method is compared with fixed-structure controller design methods based on non-smooth optimization via multiple simulation examples.

Abstract-Heavy-duty vehicles traveling in platoons consume fuel at a reduced rate. In this paper, we attempt to maximize this benefit by introducing local controllers throughout a road network to facilitate platoon formations with minimal information. By knowing a vehicle's position, speed, and destination, the local controller can quickly decide how its speed should be possibly adjusted to platoon with others in the near future. We solve this optimal control and routing problem exactly for small numbers of vehicles, and present a fast heuristic algorithm for realtime use. We then implement such a distributed control system through a large-scale simulation of the German autobahn road network containing thousands of vehicles. The simulation shows fuel savings from 1-9%, with savings exceeding 5% when only a few thousand vehicles participate in the system. We assume no vehicles will travel more than the time required for their shortest paths for the majority of the paper. We conclude the results by analyzing how a relaxation of this assumption can further reduce fuel use.

Abstract-This paper treats the problem of primary and secondary control design in low-inertia power grids with mixed lines and a large amount of inverter-interfaced generation. A dynamic phasor model is developed that represents the electromagnetic and electromechanic dynamics of lines, inverters, synchronous machines and constant power loads. The model offers a straightforward way to combine white-, grey-and blackbox models, and its structure lends itself well to control design. In a next step, a novel method to design fixed-structure robust controllers based on the frequency response of multivariable systems and convex optimization is presented. The method offers an intuitive way to define the control performance specifications, and is able to directly design discrete-time controllers. Finally, the potential of the control design method and the dynamic phasor model is demonstrated in a comprehensive example. In three scenarios it is illustrated how the approach can be used to significantly improve frequency and voltage transient performance in low-inertia power grids. Decentralized as well as distributed architectures for primary and secondary control are studied, and results are validated in simulation.

Understanding hierarchical self‐assembly of biological structures requires real‐time measurement of the self‐assembly process over a broad range of length‐ and timescales. The success of high‐speed atomic force microscopy (HS‐AFM) in imaging small‐scale molecular interactions has fueled attempts to introduce this method as a routine technique for studying biological and artificial self‐assembly processes. Current state‐of‐the‐art HS‐AFM scanners achieve their high imaging speed by trading achievable field of view for bandwidth. This limits their suitability when studying larger biological structures. In ambient conditions, large‐range scanners with lower resonance frequencies offer a solution when combined with first principle model–based schemes. For imaging molecular self‐assembly processes in fluid, however, such traditional control techniques are less suited. In liquid, the time‐varying changes in the behavior of the complex system necessitate frequent update of the compensating controller. Recent developments in data‐driven control theory offer a model‐free, automatable approach to compensate the complex system behavior and its changes. Here, a data‐driven control design method is presented to extend the imaging speed of a conventional AFM tube scanner by one order of magnitude. This enables the recording of the self‐assembly process of DNA tripods into a hexagonal lattice at multiple length scales.

A novel method to design data-driven, fixed-structure controllers with H 2 and H ∞ performance objectives is presented. The control design problem is transformed into a convex optimization problem with linear matrix inequality constraints, which can be solved efficiently with standard solvers. The method is used to design a data-driven controller for an atomic-force microscope. The closed-loop performance of the calculated controller is validated on a real setup.