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Register a new userA distributed framework for linear adaptive MPC
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Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain $ell_2$ stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations.
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An important issue in todays electricity markets is the management of flexibilities offered by new practices, such as smart home appliances or electric vehicles. By inducing changes in the behavior of residential electric utilities, demand response (DR) seeks to adjust the demand of power to the supply for increased grid stability and better integration of renewable energies. A key role in DR is played by emergent independent entities called load aggregators (LAs). We develop a new decentralized algorithm to solve a convex relaxation of the classical Alternative Current Optimal Power Flow (ACOPF) problem, which relies on local information only. Each computational step can be performed in an entirely privacy-preserving manner, and system-wide coordination is achieved via node-specific distribution locational marginal prices (DLMPs). We demonstrate the efficiency of our approach on a 15-bus radial distribution network.
Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for the next generation smart manufacturing. It can lead to improving economic performance but at the same time increases the computational complexity significantly. Model approximation has been a standard approach for reducing computational complexity in process control. In this work, we perform a study on three types of representative model approximation methods applied to EMPC, including model reduction based on available first-principle models (e.g., proper orthogonal decomposition), system identification based on input-output data (e.g., subspace identification) that results in an explicitly expressed mathematical model, and neural networks based on input-output data. A representative algorithm from each model approximation method is considered. Two processes that are very different in dynamic nature and complexity were selected as benchmark processes for computational complexity and economic performance comparison, namely an alkylation process and a wastewater treatment plant (WWTP). The strengths and drawbacks of each method are summarized according to the simulation results, with future research direction regarding control oriented model approximation proposed at the end.
An autonomous adaptive MPC architecture is presented for control of heating, ventilation and air condition (HVAC) systems to maintain indoor temperature while reducing energy use. Although equipment use and occupant changes with time, existing MPC methods are not capable of automatically relearning models and computing control decisions reliably for extended periods without intervention from a human expert. We seek to address this weakness. Two major features are embedded in the proposed architecture to enable autonomy: (i) a system identification algorithm from our prior work that periodically re-learns building dynamics and unmeasured internal heat loads from data without requiring re-tuning by experts. The estimated model is guaranteed to be stable and has desirable physical properties irrespective of the data; (ii) an MPC planner with a convex approximation of the original nonconvex problem. The planner uses a descent and convergent method, with the underlying optimization problem being feasible and convex. A year long simulation with a realistic plant shows that both of the features of the proposed architecture - periodic model and disturbance update and convexification of the planning problem - are essential to get the performance improvement over a commonly used baseline controller. Without these features, though MPC can outperform the baseline controller in certain situations, the benefits may not be substantial enough to warrant the investment in MPC.
Decentralized conflict resolution for autonomous vehicles is needed in many places where a centralized method is not feasible, e.g., parking lots, rural roads, merge lanes, etc. However, existing methods generally do not fully utilize optimization in decentralized conflict resolution. We propose a decentralized conflict resolution method for autonomous vehicles based on a novel extension to the Alternating Directions Method of Multipliers (ADMM), called Online Adaptive ADMM (OA-ADMM), and on Model Predictive Control (MPC). OA-ADMM is tailored to online systems, where fast and adaptive real-time optimization is crucial, and allows the use of safety information about the physical system to improve safety in real-time control. We prove convergence in the static case and give requirements for online convergence. Combining OA-ADMM and MPC allows for robust decentralized motion planning and control that seamlessly integrates decentralized conflict resolution. The effectiveness of our proposed method is shown through simulations in CARLA, an open-source vehicle simulator, resulting in a reduction of 47.93% in mean added delay compared with the next best method.
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.
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