Coordinative Optimization Control of Microgrid Based on Model Predictive Control

Discussion on: “Improved MPC Design based on Saturating Control Laws”

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

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

This paper presents a model predictive voltage control method for single-inductor multiple-output (SIMO) DC/DC converters operating in the continuous conduction mode (CCM). The state-space model for a SIMO DC/DC converter system is formulated and a Model Predictive Control (MPC) strategy, which has the capability to track the reference voltage automatically and reduce the impact of cross regulation problem, is proposed to regulate the system. Considering that the switches in the converter only have ON/OFF modes, the quadratic programming algorithm is employed to obtain the MPC's solution. Simulation studies are conducted in MATLAB/Simulink to verify the performance of the proposed method.

This letter presents a novel model predictive control strategy without involving any proportional-integral-differential regulators for practical renewable energy based ac microgrids. The proposed method consists of a model predictive power control (MPPC) scheme and a model predictive voltage control (MPVC) scheme. By controlling the bidirectional buck-boost converters of the battery energy storage systems based on the MPPC algorithm, the fluctuating output from the renewable energy sources can be smoothed, while stable dc-bus voltages can be maintained as the inverters' inputs. Then, the parallel inverters are controlled by using a combination of the MPVC scheme and the droop method to ensure stable ac voltage output and proper power sharing. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation on MATLAB/Simulink and on real-time laboratory platform.

Model predictive control of a miniaturized linear single-piston cryocooler

This paper presents a force control system design by model predictive control scheme. Recently, a lot of research has been carried out on the robots that take the place of human labor. One of the challenges of such robots is deformable object manipulation to working on food processing, surgery, etc. For handling deformable objects by a rigid robot manipulator is required an advanced reaction force control methodology. Such force control methods have to consider suitable force application without excess and deficiency forces for preventing to drop and destroy in contact with unknown objects. To realize this, it is required to directly consider constraints on control input, rate of input, velocity, etc. Model predictive control is known as one of the key techniques in control fields, and it has a potential for stable force control implementation by considering constraints. This paper attempts to design a new force control system by utilizing model predictive velocity control based on an augmented state-space model with a disturbance term. Finally, the performances of the proposed method via numerical simulation are shown.

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

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.

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

An implementable stabilizing model predictive controller applied to a rotary flexible link: An experimental case study

In this paper, the online correction model predictive control (MPC) strategy is presented for partial differential equation (PDE) unknown spatially-distributed systems (SDSs). The low-dimensional MIMO models are obtained using principal component analysis (PCA) method from the high-dimensional spatio-temporal data. Though the linear low-dimensional model is easy for control design, it is a linear approximation for nonlinear SDSs. Thus, the MPC strategy is proposed based on the online correction low-dimensional models, where the state at a previous time is used to correct the output of low-dimensional models and the spatial output is correct by the average deviation of the historical data. The simulations demonstrated show the accuracy and efficiency of the proposed methodologies.

This paper proposes an improved constrained networked model predictive tracking control design for the chamber pressure of a coke furnace under uncertainty and packet losses. Unlike conventional constrained model predictive control (MPC) strategies that have a limitation in the consideration of both set-point tracking and the dynamic process responses, the system state variables and output tracking errors are combined and thus can be regulated simultaneously in the new MPC scheme. Based on such advantages, there are more degrees of freedom for the subsequent controller design and improved system performance can then be obtained. Case studies on the regulation of chamber pressure of a coke furnace under process uncertainties and packet losses are investigated to verify the proposed approach in comparison with typical traditional constrained MPC schemes.