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Paper targets SPEEDAM 2024 (https://www.speedam.org). The paper provides details of a model predictive control designed to operate a four-zone medium-voltage AC/DC electric ship.
Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. Here, this article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.
This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.
We develop an online data-enabled predictive (ODeePC) control method for trajectory tracking of unknown systems, building upon the recently proposed DeePC. Our proposed ODeePC method leverages a primal-dual algorithm with real-time measurement feedback to iteratively compute the corresponding real-time optimal control policy as system conditions change. Specifically, our developed ODeePC: a) records data from the unknown system and updates the underlying primal-dual algorithm dynamically, b) can track changes in the system's operating point and adjust the control inputs, and c) is computationally efficient as it deploys a Fast Fourier Transform-based algorithm enabling the fast computation of the product of a non-square Hankel matrix with a vector. We provide theoretical guarantees regarding the asymptotic behavior of ODeePC and demonstrate its performance through a power system application.
Dabadghao, V., D.A. Allan, M. Li, J.C. Eslick, J. Ma, S.E. Zitney, D. Bhattacharyya, and L.T. Biegler, “Nonlinear Model Predictive Control for Solid-Oxide Electrolysis Cell Systems,” AIChE 2022 Annual Meeting, Phoenix, AZ, November 13-18 (2022).
Abstract Solid oxide cells (SOCs) are a promising dual‐mode technology for the production of hydrogen through high‐temperature water electrolysis, and the generation of power through a fuel cell reaction that consumes hydrogen. Switching between these two modes as the price of electricity fluctuates requires reversible SOC operation and accurate tracking of hydrogen and power production set points. Moreover, a well‐functioning control system is important to avoid cell degradation during mode‐switching operation. In this article, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional‐integral (PI) control strategies, while switching between the modes of hydrogen and power production. While both control methods provide similar performance across various metrics during mode switching, NMPC demonstrates a significant advantage in reducing cell thermal gradients and curvatures (mixed spatial‐temporal partial derivatives), thereby helping to mitigate long‐term degradation.
Generalized Disjunctive Programming (GDP) provides an alternative framework to model optimization problems with both discrete and continuous variables. The key idea behind GDP involves the use of logical disjunctions to represent discrete decisions in the continuous space, and logical propositions to denote algebraic constraints in the discrete space. Compared to traditional mixed-integer programming (MIP), the inherent logic structure in GDP yields tighter relaxations that are exploited by global branch and bound algorithms to improve solution quality. In this paper, we present a general GDP model for optimal control of hybrid systems that exhibit both discrete and continuous dynamics. Specifically, we use GDP to formulate a model predictive control (MPC) model for piecewise-affine systems with implicit switching logic. As an example, the GDP-based MPC approach is used as a supervisory control to improve energy efficiency in residential buildings with binary on/off, relay-based thermostats. A simulation study is used to demonstrate the validity of the proposed approach, and the improved solution quality compared to existing MIPbased control approaches.
Hong, S.M.C., E. Hedrick, K. Hedrick, D. Beahr, D. Bhattacharyya, S.E. Zitney, and B.P. Omell, "Multiple And Nonlinear Model Predictive Control for Rapid Load Following Operation of Supercritical Pulverized Coal Power Plants," AIChE 2022 Annual Meeting, Phoenix, AZ, November 13-18 (2022).
This paper presents an effective resonance suppression control when grid interactive inverter with LCL filter experience weak grid conditions. The resonance suppression mechanism is based on finite-set model predictive control (FSMPC) with adaptive cost function to alter the controller objectives seamlessly. The large parasitic impedance and low short circuit ratio (SCR) of weak grid challenges the operation of gridconnected inverters. Specifically, the LCL filter resonance may get excited, resulting in collapse of the inverter operation. To address this issue, an effective active damping approach is implemented in the proposed control. In the proposed active damping scheme for resonance suppression, the feedback currents are switched based on the operating conditions of inverter. During the stiff grid conditions, grid current serves as feedback to the controller. While in the weak grid conditions, the inverter current is selected as feedback signal. The toggling action between these two feedback currents is determined by comparing moving RMS of grid current with threshold current as constraint in the cost function of the proposed MPC scheme. The threshold current for toggling action is based on the reference active and reactive power setpoints plus an acceptable band of variation. The theoretical analysis is verified by several case studies.
Hedrick, E., K. Reynolds, S. Hong, D. Bhattacharyya, S.E. Zitney, and B. Omell, "Advanced Model Predictive Control for Reducing Equipment Damage in a Supercritical Pulverized Coal Fired Power Plant during Load-Following Operation," AIChE 2021 Annual Meeting, Boston, MA, November 7-11 (2021).
In the transition from fossil fuel to electrified heating, a concerning trend is emerging in certain regions of the US. Owners of buildings with gas-based systems leave them in place after adding heat pumps (HPs). Existing control solutions for these hybrid (dual fuel) systems are rudimentary and fall short of realizing the full carbon reduction potential of these systems. Model predictive control (MPC) is often regarded as the benchmark for achieving optimal control in integrated systems. However, in the case of small-medium commercial buildings (SMCBs), the control and communication infrastructure required to facilitate the implementation of such advanced controls is often lacking. This paper presents a field implementation of easy-to-deploy MPC for a dual fuel heating system consisting of HPs and a gas-fired furnace (GF) for SMCBs. The control system is deployed on an open-source middleware platform and utilizes low-cost sensor devices to be used for real SMCBs without major retrofits. Here, we demonstrated this MPC in a real office building with 5 HPs and 1 GF for 2 months. The test results showed that MPC reduced 27% of cost while completely eliminating GF usage by shifting 23% of the thermal load from occupied-peak time to non-occupied-non-peak times.
An increase in wave energy converter (WEC) efficiency requires not only consideration of the nonlinear effects in the WEC dynamics and the power take-off (PTO) mechanisms, but also more integrated treatment of the whole system, i.e., the buoy dynamics, the PTO system, and the control strategy. It results in an optimization formulation that has a nonquadratic and nonstandard cost functional. This article presents the application of real-time nonlinear model predictive controller (NMPC) to two degrees of freedom point absorber type WEC with highly nonlinear PTO characteristics. The nonlinear effects, such as the fluid viscous drag, are also included in the plant dynamics. The controller is implemented on a real-time target machine, and the WEC device is emulated in real-time using the WECSIM toolbox. The results for the successful performance of the design are presented for irregular waves under linear and nonlinear hydrodynamic conditions.
The energy-stored quasi-Z-source inverter (ES-qZSI) has attracted much attention for photovoltaic (PV) power generations, due to its capability to stabilize the PV power fluctuations and other advantages of Z-source inverter. Traditional ES-qZSI suffers from poor operation at night or cloudy days when PV power is extremely low. In order to overcome that, this paper proposes a model predictive control (MPC) strategy for an improved dual-mode ES-qZSI, which is designed to support all-weather operation of PV power generation system. The system predictive models in both day and night operating modes are derived. And the control strategy based on MPC is disclosed to ensure the high-performance operation of the system. Simulation is carried out on PV power fluctuation in daytime and switching between the two modes, to verify the effectiveness of proposed control strategy.
Primary frequency support functionality can be an added advantage for any new grid integrated power electronics resource. With increased penetration of power electronics resources, inertial and primary frequency response capabilities are decreasing. In this research paper, an advanced model-based predictive control (MBPC) algorithm for providing frequency support to a new topology of integrated photovoltaic(PV), batterybased energy storage systems (ESS), and high-voltage direct current (HV dc) systems called multi-port autonomus reconfigurable solar (MARS) plants is based on the synchronverter-based control. The simulation of MARS-HV dc system with proposed control strategy is simulated and validated for MARS connected to low short circuit ratio (SCR) grids in PSCAD/EMTDC simulation environment. The control algorithm proposed showcases better performance in terms of frequency nadir improvement and frequency steady state improvemnt with respect to no frequency control.
Abstract Many biomass intermediates are polyols and selectively oxidizing only a primary or secondary alcohol group is beneficial for the valorization of these intermediates. For example, production of 1,3-dihydroxyacetone, a highly valuable oxidation product of glycerol, requires selective secondary alcohol oxidation. However, selective secondary alcohol oxidation is challenging due to its steric disadvantage. This study demonstrates that NiOOH, which oxidizes alcohols via two dehydrogenation mechanisms, hydrogen atom transfer and hydride transfer, can convert glycerol to 1,3-dihydroxyacetone with high selectivity when the conditions are controlled to promote hydrogen atom transfer, favoring secondary alcohol oxidation. This rational production of 1,3-dihydroxyacetone achieved by selectively enabling one desired dehydrogenation pathway, without requiring alteration of catalyst composition, demonstrates how comprehensive mechanistic understanding can enable predictive control over selectivity.
An electronic device includes a processor, a branch predictor in the processor, and a predictor controller in the processor. The branch predictor includes multiple prediction functional blocks, each prediction functional block configured for generating predictions for control transfer instructions (CTIs) in program code based on respective prediction information, the branch predictor configured to select, from among predictions generated by the prediction functional blocks for each CTI, a selected prediction to be used for that CTI. The predictor controller keeps a record of prediction functional blocks from which the branch predictor previously selected predictions for CTIs. The predictor controller uses information from the record for controlling which prediction functional blocks are used by the branch predictor for generating predictions for CTIs.
To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. Here this work presents how anticipatory control strategies can be implemented via model predictive control (MPC) of a single heat pipe’s temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that nonparametric models such as the ANN and ARX result in more fluctuated control actions compared to the MPC applied to the linear response surface model. However, when the cyber incidents are of a large magnitude, the linear response surface model produces smaller feasible regions than the nonparametric models under identical constraints.