An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control with Ensemble Koopman Modeling

Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation. This paper proposes an online adaptive Koopman operator optimal control (AKOOC) method for MG secondary voltage and frequency control. Unlike typical data-driven methods that are data-hungry and lack guaranteed stability, the proposed AKOOC requires no warm-up training yet with guaranteed bounded-input-bounded-output (BIBO) stability and even asymptotical stability under some mild conditions. The proposed AKOOC is developed based on an ensemble Koopman state space modeling with full basis functions that combines both linear and nonlinear bases without the need of event detection or switching. An iterative learning method is also developed to exploit model parameters, ensuring the effectiveness and the adaptiveness of the designed control. Simulation studies in the 4-bus (with detailed inner-loop control) MG system and the 34-bus MG system showed improved modeling accuracy and control, verifying the effectiveness of the proposed method subject to various changes of operating conditions even with time delay, measurement noise, and missing measurements.Comment: Accepted by IEEE Transactions on Smart Grid for future publicatio

Data-driven Economic NMPC using Reinforcement Learning

Reinforcement Learning (RL) is a powerful tool to perform data-driven optimal control without relying on a model of the system. However, RL struggles to provide hard guarantees on the behavior of the resulting control scheme. In contrast, Nonlinear Model Predictive Control (NMPC) and Economic NMPC (ENMPC) are standard tools for the closed-loop optimal control of complex systems with constraints and limitations, and benefit from a rich theory to assess their closed-loop behavior. Unfortunately, the performance of (E)NMPC hinges on the quality of the model underlying the control scheme. In this paper, we show that an (E)NMPC scheme can be tuned to deliver the optimal policy of the real system even when using a wrong model. This result also holds for real systems having stochastic dynamics. This entails that ENMPC can be used as a new type of function approximator within RL. Furthermore, we investigate our results in the context of ENMPC and formally connect them to the concept of dissipativity, which is central for the ENMPC stability. Finally, we detail how these results can be used to deploy classic RL tools for tuning (E)NMPC schemes. We apply these tools on both a classical linear MPC setting and a standard nonlinear example from the ENMPC literature

Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems

Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system

MIT's interferometer CST testbed

The MIT Space Engineering Research Center (SERC) has developed a controlled structures technology (CST) testbed based on one design for a space-based optical interferometer. The role of the testbed is to provide a versatile platform for experimental investigation and discovery of CST approaches. In particular, it will serve as the focus for experimental verification of CSI methodologies and control strategies at SERC. The testbed program has an emphasis on experimental CST--incorporating a broad suite of actuators and sensors, active struts, system identification, passive damping, active mirror mounts, and precision component characterization. The SERC testbed represents a one-tenth scaled version of an optical interferometer concept based on an inherently rigid tetrahedral configuration with collecting apertures on one face. The testbed consists of six 3.5 meter long truss legs joined at four vertices and is suspended with attachment points at three vertices. Each aluminum leg has a 0.2 m by 0.2 m by 0.25 m triangular cross-section. The structure has a first flexible mode at 31 Hz and has over 50 global modes below 200 Hz. The stiff tetrahedral design differs from similar testbeds (such as the JPL Phase B) in that the structural topology is closed. The tetrahedral design minimizes structural deflections at the vertices (site of optical components for maximum baseline) resulting in reduced stroke requirements for isolation and pointing of optics. Typical total light path length stability goals are on the order of lambda/20, with a wavelength of light, lambda, of roughly 500 nanometers. It is expected that active structural control will be necessary to achieve this goal in the presence of disturbances

Low-complexity learning of Linear Quadratic Regulators from noisy data

This paper considers the Linear Quadratic Regulator problem for linear systems with unknown dynamics, a central problem in data-driven control and reinforcement learning. We propose a method that uses data to directly return a controller without estimating a model of the system. Sufficient conditions are given under which this method returns a stabilizing controller with guaranteed relative error when the data used to design the controller are affected by noise. This method has low complexity as it only requires a finite number of samples of the system response to a sufficiently exciting input, and can be efficiently implemented as a semi-definite program. Further, the method does not require assumptions on the noise statistics, and the relative error nicely scales with the noise magnitude