Georgios Darivianakis

Aerial robotic contact-based inspection: planning and control

The challenge of aerial robotic contact-based inspection is the driving motivation of this paper. The problem is approached on both levels of control and path-planning by introducing algorithms and control laws that ensure optimal inspection through contact and controlled aerial robotic physical interaction. Regarding the flight and physical interaction stabilization, a hybrid model predictive control framework is proposed, based on which a typical quadrotor becomes capable of stable and active interaction, accurate trajectory tracking on environmental surfaces as well as force control. Convex optimization techniques enabled the explicit computation of such a controller which accounts for the dynamics in free-flight as well as during physical interaction, ensures the global stability of the hybrid system and provides optimal responses while respecting the physical limitations of the vehicle. Further augmentation of this scheme, allowed the incorporation of a last-resort obstacle avoidance mechanism at the control level. Relying on such a control law, a contact-based inspection planner was developed which computes the optimal route within a given set of inspection points while avoiding any obstacles or other no-fly zones on the environmental surface. Extensive experimental studies that included complex “aerial-writing” tasks, interaction with non-planar and textured surfaces, execution of multiple inspection operations and obstacle avoidance maneuvers, indicate the efficiency of the proposed methods and the potential capabilities of aerial robotic inspection through contact.

A stochastic optimization approach to cooperative building energy management via an energy hub

Building energy management is an active field of research since the potential in energy savings can be substantial. Nevertheless, the opportunities for large savings within individual buildings can be limited by the flexibility of the installed climate control devices and the individual construction characteristics. The energy hub concept allows one to manage a collection of buildings in a cooperative manner, by providing opportunities for load shifting between buildings and the sharing of expensive but energy efficient equipment housed in the hub, such as heat pumps, boilers, batteries. Typically, control design for the buildings and the energy hub are done separately, underutilizing the potential flexibility provided by the interconnected system. To address these issues, we propose a unified framework for controlling the operation of the energy hub and the buildings it connects to. By modeling all exogenous disturbance parameters as stochastic processes, and by using state-space representation of the building dynamics, we formulate a multistage stochastic optimization problem to minimize the total energy consumption of the system in a cooperative manner. We solve the resulting infinite dimensional optimization problem using a decision rule approximation, and we benchmark its performance on a numerical study, comparing it with established solution techniques.

The Power of Diversity: Data-Driven Robust Predictive Control for Energy-Efficient Buildings and Districts

The cooperative energy management of aggregated buildings has recently received a great deal of interest due to substantial potential energy savings. These gains are mainly obtained in two ways: 1) exploiting the load shifting capabilities of the cooperative buildings and 2) utilizing the expensive but energy-efficient equipment that is commonly shared by the building community (e.g., heat pumps, batteries, and photovoltaics). Several deterministic and stochastic control schemes that strive to realize these savings have been proposed in the literature. A common difficulty with all these methods is integrating knowledge about the disturbances affecting the system. In this context, the underlying disturbance distributions are often poorly characterized based on historical data. In this paper, we address this issue by exploiting the historical data to construct families of distributions, which contain these underlying distributions with high confidence. We then employ tools from data-driven robust optimization to formulate a multistage stochastic optimization problem, which can be approximated by a finite-dimensional linear program. We demonstrate its efficacy in a numerical study, in which it is shown to outperform, in terms of energy cost savings and constraint violations, established solution techniques from the literature. We conclude this paper by showing the significant energy gains that are obtained by cooperatively managing a collection of buildings with heterogeneous characteristics.

Model predictive current control of modular multilevel converters

This paper proposes a model predictive controller for high-power modular multilevel converter operating at low switching frequencies. The controller regulates the load currents along their reference trajectories, controls the circulating currents and controls the sum of the capacitor voltages per branch. Upper limits on the branch currents and capacitor voltages are imposed in the controller formulation. The controller manipulates the voltage references of a carrier-based pulse width modulator. A subsequent balancing controller maintains the capacitor voltages within each branch around their average voltage. Unlike hierarchical schemes based on multiple PI control loops, the proposed controller achieves not only a very good performance at steady-state operation but also very fast current responses during load transients, while maintaining the branch currents and capacitor voltages within their safe operating limits.

A Data-Driven Stochastic Optimization Approach to the Seasonal Storage Energy Management

Several studies in the literature have shown the potential energy savings emerging from the cooperative management of the aggregated building energy demands. Sophisticated predictive control schemes have recently been developed that achieve these gains by exploiting the energy generation, conversion, and storage equipment shared by the building community. A common difficulty with all these methods is integrating knowledge about the long term evolution of the disturbances affecting the system dynamics (e.g., ambient temperature and solar radiation). In this context, the seasonal storage capabilities of the system are difficult to be optimally managed. This letter addresses this issue by exploiting available historical data to: (i) construct bounds that confine with high probability the optimal charging trajectory of the seasonal storage and (ii) generate a piece-wise affine approximation of the value function of the energy stored in the seasonal storage at each time step. Using these bounds and value functions, we formulate a multistage stochastic optimization problem to minimize the total energy consumption of the system. In a numerical study based on a realistic system configuration, the proposed method is shown to operate the system close to global optimality.

Humans in the Loop: A Stochastic Predictive Approach to Building Energy Management in the Presence of Unpredictable Users * *This project is supported by the ETH Zurich Foundation, the Swiss Competence Centers for Energy Research under the project FEEB&D and NanoTera.ch under the project HeatReserves.

Abstract Efficient building energy management has attracted a great deal of academic interest with significant potential energy savings to be envisaged. Social scientists strive to achieve these savings by employing behavior-based approaches, while engineers investigate control strategies for the efficient operation of the building devices. This work can be seen as a first step towards bridging these two approaches by proposing a control scheme that encapsulates building occupant behavior into the energy management system. In particular, the occupants willingness to tolerate comfort bound violations is modeled as a random measurable uncertainty and incorporated into the building energy management system through disturbance feedback control policies. The respective optimal control problem is formulated as a mixed-integer stochastic optimization problem, and a computationally tractable approximation of it is derived by restricting the disturbance feedback control policies to admit an affine structure. An extensive numerical study verifies that the proposed approach can significantly reduce the energy consumption of the buildings.

Humans in the Loop: A Stochastic Predictive Approach to Building Energy Management in the Presence of Unpredictable Users

Abstract Efficient building energy management has attracted a great deal of academic interest with significant potential energy savings to be envisaged. Social scientists strive to achieve these savings by employing behavior-based approaches, while engineers investigate control strategies for the efficient operation of the building devices. This work can be seen as a first step towards bridging these two approaches by proposing a control scheme that encapsulates building occupant behavior into the energy management system. In particular, the occupants willingness to tolerate comfort bound violations is modeled as a random measurable uncertainty and incorporated into the building energy management system through disturbance feedback control policies. The respective optimal control problem is formulated as a mixed-integer stochastic optimization problem, and a computationally tractable approximation of it is derived by restricting the disturbance feedback control policies to admit an affine structure. An extensive numerical study verifies that the proposed approach can significantly reduce the energy consumption of the buildings.

Model predictive control of a STATCOM based on a modular multilevel converter in delta configuration

This paper proposes a model predictive control (MPC) scheme for the single delta bridge cell (SDBC) modular multilevel converter (MMC) when operated as a static synchronous compensator (Statcom). The controller achieves reactive power compensation and current harmonic elimination while maintaining the branch currents and capacitor voltages within their safe operating limits. The MPC scheme manipulates the setpoints of a subsequent pulse width modulator (PWM). The controller is conceptually simple with an easy to devise objective function, a linearized converter model based on first principles, and constraints on the main physical quantities. The underlying optimization problem is a quadratic program (QP), which can be solved efficiently using off-the-shelf solvers. The developed control framework achieves a good performance at steady-state operation and very fast current response during load transients.