Decentralized Phase Shedding with Low Power Mode for Multiphase Converter

With a multiphase converter, the phase-shedding function dedicated to maximizing the power efficiency, in a manner that is dependent on the load current, is always provided by a centralized controller that induces a Single Point of Failure (SPOF). The objective of this study is to obtain a decentralized control approach to implement this function by removing any SPOF. The method consists of using identical local controllers, each associated with a converter phase, that communicate with each other in a daisy-chain structure. Instead of measuring the global output current to determine the optimal number of active phases required, each local controller measures its own leg current and takes a local decision based on threshold crossing management and inter-controller communications. Functional simulations are carried out on a 5-leg 12 V/1.2 V 60 W multiphase converter supplying a modern microcontroller. They demonstrate that the number of active phases is well adjusted, in a dynamic manner, depending on the load current level. Specific events such as load current inrush or the start-up sequence are analyzed to guarantee optimal transient responses. A maximum power efficiency tracking ability is also demonstrated. Finally, it is shown that this control strategy allows phase shedding to be implemented using as many phases as desired, in a modular manner, thereby avoiding any centralized processing.

Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay

This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents’ movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.

Strategies for an Adaptive Control System to Improve Power Grid Resilience with Smart Buildings

Low-voltage distribution grids face new challenges through the expansion of decentralized,renewable energy generation and the electrification of the heat and mobility sectors. We present amulti-agent system consisting of the energy management systems of smart buildings, a central gridcontroller, and the local controller of a transformer. It can coordinate the provision of ancillary servicesfor the local grid in a centralized way, coordinated by the central controller, and in a decentralizedway, where each building makes independent control decisions based on locally measurable data.The presented system and the different control strategies provide the foundation for a fully adaptivegrid control system we plan to implement in the future, which does not only provide resilienceagainst electricity outages but also against communication failures by appropriate switching ofstrategies. The decentralized strategy, meant to be used during communication failures, could alsobe used exclusively if communication infrastructure is generally unavailable. The strategies areevaluated in a simulated scenario designed to represent the most extreme load conditions that mightoccur in low-voltage grids in the future. In the tested scenario, they can substantially reduce voltagerange deviations, transformer temperatures, and line congestions.

Information Aggregation for Constrained Online Control

This paper considers an online control problem involving two controllers. A central controller chooses an action from a feasible set that is determined by time-varying and coupling constraints, which depend on all past actions and states. The central controller's goal is to minimize the cumulative cost; however, the controller has access to neither the feasible set nor the dynamics directly, which are determined by a remote local controller. Instead, the central controller receives only an aggregate summary of the feasibility information from the local controller, which does not know the system costs. We show that it is possible for an online algorithm using feasibility information to nearly match the dynamic regret of an online algorithm using perfect information whenever the feasible sets satisfy a causal invariance criterion and there is a sufficiently large prediction window size. To do so, we use a form of feasibility aggregation based on entropic maximization in combination with a novel online algorithm, named Penalized Predictive Control (PPC) and demonstrate that aggregated information can be efficiently learned using reinforcement learning algorithms. The effectiveness of our approach for closed-loop coordination between central and local controllers is validated via an electric vehicle charging application in power systems.

DDS-Based Containment Control of Multiple UAV Systems

In this paper, we present a robust containment control design for multi Unmanned Aerial Vehicle Systems (UAVs) based on the Data Distribution Service (DDS) middleware and L 1 adaptive controller. The Data Distribution Service middleware, L 1 adaptive controller and graph theory technique are utilized for the navigation of the UAVs. The L 1 controller is utilized as a local controller for each UAVs and the graph theory approach is utilized to constitute the followers inside their leaders. Finally, the DDS Middleware is used to exchange data between the followers and their leaders. Robust adaptation of the L 1 controller makes the system robust with a high level of performance. Matlab simulation verified the robustness of the L 1 controller. We provide stability proofs using Lyapunov analysis for the UAVs framework.

Manipulating deformable objects by interleaving prediction, planning, and control

We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems, such as deformable objects, achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima owing to obstacles. Our approach consists of three components: (1) a global motion planner to generate gross motion of the deformable object; (2) a local controller for refinement of the configuration of the deformable object; and (3) a novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We provide a detailed proof of probabilistic completeness for our planner, which is valid despite the fact that our system is underactuated and we do not have a steering function. We then demonstrate that our framework is able to successfully perform several manipulation tasks with rope and cloth in simulation, which cannot be performed using either our controller or planner alone. These experiments suggest that our planner can generate paths efficiently, taking under a second on average to find a feasible path in three out of four scenarios. We also show that our framework is effective on a 16-degree-of-freedom physical robot, where reachability and dual-arm constraints make the planning more difficult.