Robust Explicit MPC Based on Approximate Multiparametric Convex Programming

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

In this paper we propose a stochastic model predictive control (MPC) formulation based on scenario generation for linear systems affected by discrete multiplicative disturbances. By separating the problems of (1) stochastic performance, and (2) stochastic stabilization and robust constraints fulfillment of the closed-loop system, we aim at obtaining a less conservative control action with respect to classical robust MPC schemes, still enforcing convergence and feasibility properties for the controlled system. Stochastic performance is addressed for very general classes of stochastic disturbance processes, although discretized in the probability space, by adopting ideas from multi-stage stochastic optimization. Stochastic stability and recursive feasibility are enforced through linear matrix inequality (LMI) problems, which are solved off-line; stochastic performance is optimized by an on-line MPC problem which is formulated as a convex quadratically constrained quadratic program (QCQP) and solved in a receding horizon fashion. The performance achieved by the proposed approach is shown in simulation and compared to the one obtained by standard robust and deterministic MPC schemes.

Two-stage inverters composed of the front DC-DC stage and the rear DC-AC stage are broadly employed for the current tracking. Structures vary according to the different control objectives and application occasions. The concerning structure in this paper is made up of the buck-boost converter stage and the single-phase inverter stage. The simulation results show that the intermediate voltage will affect the total harmonic distortion (THD) values. Moreover, under the same other conditions, a minimum THD can be attained by regulating the intermediate voltage. In this paper, a time-varying observer is introduced for the interface parameters identification based on the robustness of model predictive control (MPC) scheme. Combined with the input voltage, the reference current and grid voltage, the optimal intermediate voltage with lower THD can be determined. Besides, a cascaded proportional-integral (PI) MPC scheme is adopted for the intermediate voltage tracking without steady-state error. Meanwhile, based on the accurate interface parameters obtained by the observer, an adaptive MPC scheme is applied for the output current tracking. The simulation results on MATLAB/Simulink and experimental results on FPGA test bench validate the accuracy of the voltage determination and the effectiveness of the control scheme.

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

In this paper, an integrated tube robust iterative learning model predictive control (Tube‐RILMPC) strategy based on the dynamic partial least squares (DyPLS) identified algorithm is proposed. The problems of large amount of online data calculation and input and output variable dimensions disaster in the original variable space are solved. This integrated Tube‐RILMPC strategy enhances the tracking performance and robustness of two‐dimensional (2D) control system. The output trajectories of system are located in the tube the nominal trajectory, which reduces the modelling error caused by the uncertain model. Based on the worst‐case performance index of ellipsoidal uncertainty and polytopic uncertainty, a robust iterative learning control (ILC) strategy is designed. Finally, the superiority of the proposed control algorithm is verified by comparative simulation.

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.

Levitation control is a core technique of magnetic levitation (Maglev) trains, whose performance has a significant impact on the driving safety and ride comfort during the operation of Maglev trains. With the rapid development of high temperature superconducting (HTS) materials, a novel hybrid levitation system for Maglev trains, composed of HTS and normal conducting electromagnets, is becoming a more promising alternative to the conventional levitation system, due to a lager levitation air gap and lower energy consumption. The controller of the hybrid levitation system is hard to design due to strong nonlinearity, open-loop instability and fast-response requirements. In this study, we propose a robust model predictive control (MPC) strategy for the novel hybrid levitation system in which state constraints are taken into account, including the air gap, and the velocity and acceleration of the hybrid magnet. In the MPC strategy, the prediction model employs a stabilized model through nonlinear and linear state feedback. With regard to the online computational load, the primal-dual interior-point (PDI) algorithm is adopted to solve the optimization problem with high efficiency to deal with constraints in a real-time manner. Finally, simulation results are demonstrated to verify the effectiveness of the proposed MPC-PDI control strategy for the hybrid levitation system, and the significances considering state constraints are revealed. This study provides an engineering realization solution to the novel hybrid levitation system of a Maglev train.

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

This paper studies the optimal control problem for discrete-time switched linear systems with quadratic cost. A model predictive control (MPC) scheme is proposed which results in closed-loop strategies for both switching and control inputs. To reduce the online computation and ensure stability of the MPC scheme, a two-stage pruning algorithm is constructed which is performed offline. The resulting MPC scheme ensures exponential stability, and the cost function is suboptimal. Stability, feasibility, and suboptimality of the MPC scheme are studied. Simulation results are given for the MPC scheme which shows the proposed approach results in reduced computation and acceptable performance.

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

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.

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.

A habituating blood glucose control strategy for the critically ill

In this article, a robust Model Predictive Control (MPC) scheme is designed for the basic position tracking problem of the DC servomechanism control system. The non-linearity and parameters uncertainty are also considered in the design. The control law for MPC is modeled from the servo system dynamics and the stability is analyzed. The proposed design evaluation is performed under disturbance and without disturbance effect using an input reference signal. Moreover, for further validation, the results of MPC based model are compared with the Sliding Mode Controller (SMC) and Proportional Integral Derivative (PID) controller in order to estimate and evaluate the designed system performance. Simulation results show that the designed controller has much better tracking performance, efficiency, robustness and stability.