Model predictive control of a miniaturized linear single-piston cryocooler

A novel tuning strategy for multivariable model predictive control

This paper presents a general formulation of the model predictive control (MPC) scheme for controlling in real time the response of structures under earthquakes. The MPC scheme is based on an explicit use of a prediction model of the system response to obtain the control actions by minimizing an objective function. Optimization objectives in MPC include minimization of the difference between the predicted and desired response trajectories, and the control effort subjected to certain constraints. In this study, the prediction model is formulated using both feedforward (FF) and feedback (FB) components to increase the effectiveness of the MPC scheme. The FF loop in the prediction model is formulated using two types of input. First, it is designed using the established Kanai–Tajimi‐type model to represent the earthquake input. Second, a real‐time FF loop is introduced using an auto‐regressive (AR) model for earthquake ground motion which is constantly updated using real‐time on‐line observations. The real‐time FF loop certainly promises to add predictive and adaptive features to the control actions to account for any unusual features in the ground motion. The structural response with and without the FF and FB loops are compared. Examples are used to demonstrate the efficacy of the proposed methodology. These examples show that the MPC‐based controller is effective in reducing the structural response under different earthquakes which contain distinct features in their spectral and non‐stationary descriptions. The performance of the MPC scheme is then compared to H2 control strategies for different time horizons. The effectiveness of MPC is shown to be equivalent to the optimal control. This paper lays a foundation for capturing the main strengths of MPC, i.e. computational expediency, real‐time application, intrinsic compensation for time delays, treatment of constraints and potential for future extensions in structural control. Copyright © 2001 John Wiley & Sons, Ltd.

The challenges of contingent island operation of hydropower plants are addressed by proposing an enhanced frequency control with advanced control algorithms. The study is based on a real power plant and considers the relevant power system in its entirety along with the load and transmission lines. Integrated transfer functions between the guide vane opening of a hydro turbine and the frequency of the associated generator were determined by in-situ identification. A model predictive controller, a fractional order PID controller, and a PID controller utilizing integral absolute error and integral time absolute error criteria were designed and developed. The performance of the proposed algorithms with the identified plant model was tested on a NI-cRIO 9049 FPGA platform. Improved frequency control in terms of setpoint change was achieved with the MPC and FOPID controllers. The MPC controller also features betters disturbance rejection compared to conventional PID controllers, which are tuned according to integral error criteria. The considered algorithms have been simulated in different operating scenarios, such as variations of active power load and transmission length. The proposed approach enhances stability and consequently avoids operational failures of hydropower plants in contingent islanding mode.

This article describes how basing on the future behavior of microgrid system, forecasting renewable energy power generation, load and real-time electricity price, a model predictive control (MPC) strategy is proposed in this article to optimize microgrid operations, while meeting the time-varying requirements and operation constraints. Considering the problems of unit commitment, energy storage, economic dispatching, sale-purchase of electricity and load reduction schedule, the authors first model a microgrid system with a large number of constraints and variables to model the power generation technology and physical characteristics. Meanwhile the authors use a mixed logic dynamical framework to guarantee a reasonable behavior for grid interaction and storage and consider the influences of battery life and recession. Then for forecasting uncertainties in the microgrid, a feedback mechanism is introduced in MPC to solve the problem by using a receding horizon control. The objective of minimizing the operation costs is achieved by an MPC strategy for scheduling the behaviors of components in the microgrid. Finally, a comparative analysis has been carried out between the MPC and some traditional control methods. The MPC leads to a significant improvement in operating costs and on the computational burden. The economy and efficiency of the MPC are shown by the simulations.

Modeling and model predictive control of the BioPower combined heat and power (CHP) plant

In this paper, a “third generation” benchmark problem that focuses on the control of wind excited response of a tall building, using the Model Predictive Control (MPC) scheme, is presented. A 76 story, 306 m tall concrete office tower proposed for the city of Melbourne, Australia, is being used to demonstrate the effectiveness of MPC. The MPC scheme is based on an explicit use of a prediction model of the system response to obtain the control actions by minimizing an objective function. Optimization objectives in MPC include minimization of the difference between the predicted and desired response trajectories, and the control effort subjected to prescribed constraints. By incorporating input/output hard constraints, the MPC scheme provides an optimal control force that satisfies the prescribed constraints.

A model predictive control for a multi-chiller system in data center considering whole system energy conservation

CONTROL OF NUTRIENT REMOVING ACTIVATED SLUDGE SYSTEM

Model predictive control (MPC) is used in the CO2 removal process in the Subang field to improve its control performance. MPC is used to maintain the CO2 concentration at the sweet gas output by controlling the feed gas pressure (PIC-1101), makeup water flow rate (FIC-1102), and amine flow rate (FIC-1103). The empirical model applied to MPC to represent the process model is the auto-regressive exogenous (ARX) model. The ARX model is compared with the first order plus dead time (FOPDT) model based on the root mean square error (RMSE) between the model and the actual process, then MPC parameters are tuned which include sampling time (T), prediction horizon (P) and control horizon (M) to control for the three variables. Improved control performance is measured based on the integral square error (ISE). The results show that the ARX model is the best model for the CO2 removal process with an RMSE value of 35%-91% smaller than the FOPDT model. The optimal control parameters Prediction Horizon (P), Control Horizon (M) and Sampling Time (T) in the CO2 removal process are 75, 25 and 1 on PIC-1101, 25, 10 and 1 on FIC-1102, and 30, 25 and 1 on FIC-1103. The MPC-ARX (MPC using ARX model) can improve the control performance of 33% in the servo control and 6-56% on the regulatory control. However, not all of them showed an increase in control performance improvement from previous studies even though they had used the best model (ARX). This is due to the MPC parameter setting that is not yet appropriate, so it needs to be retuning.

Energy-efficient operation of the thermal management system in electric vehicles via integrated model predictive control

Using dimensional analysis and numerical optimisation techniques, an optimal method for tuning PID controllers for first order plus time delay systems is presented. Considering integral square error (ISE), integral absolute error (IAE) and integral time absolute error (ITAE) performance criteria, optimal equations for obtaining PID parameters are proposed. Simulation results show that the proposed method has a considerable superiority over conventional techniques. In addition, the closed loop system shows a robust performance in the face of model parameters uncertainty.

Making Non-Centralized a Model Predictive Control Scheme by Using Distributed Smith Dynamics

LIDAR systems are able to provide preview information of wind disturbances at various distances in front of wind turbines. This technology paves the way for new control concepts such as feedforward control and model predictive control. This paper compares a nonlinear model predictive controller and a feedforward controller to a baseline controller. Realistic wind "measurements" are obtained using a detailed simulation of a LIDAR system. A full lifetime comparison shows the advantages of using the wind predictions to reduce wind turbine fatigue loads on the tower and blades as well as to limit the blade pitch rates. The results illustrate that the feedforward controller can be combined with a tower feedback controller to yield similar load reductions as the model predictive controller.

Waste heat from a data centre (DC) is a promising heat source because of the evenly distributed load profile and intensive waste heat generation. Many studies have proven the substantial financial benefits for the district heating (DH) operators by integrating DC waste heat with DH systems. However, there is a scarcity of research focusing on the optimal control of the DH system after integrating DC waste heat to further improve the system’s economic performance. Therefore, this study aimed to further improve the economic performance of a DH system with DC waste heat by utilizing a model predictive control (MPC) scheme. This MPC scheme employed an economic-related objective function and formulated technical operational constraints. The proposed MPC scheme was tested on a campus DH system in Norway by simulation. Compared to a traditional rule-based control approach, the MPC scheme reduced the monthly energy cost by 1.8% while providing more stable chilled water for the DC cooling system.

Robust One-step Model Predictive Control for Discrete Time-delay Systems