The steam/water loop is a crucial part of a steam power plant. However, satisfying control performance is difficult to obtain due to the frequent disturbance and load fluctuation. A fractional order model predictive control was studied in this paper to improve the control performance of the steam/water loop. Firstly, the dynamic of the steam/water loop was introduced in large-scale ships. Then, the model predictive control with an extended prediction self adaptive controller framework was designed for the steam/water loop with a distributed scheme. Instead of an integer cost function, a fractional order cost function was applied in the model predictive control optimization step. The superiority of the fractional order model predictive control was validated with reference tracking and load fluctuation experiments.

Supplier development constitutes one of the current tools to enhance supply chain performance. While most literature in this context focuses on the relationship between manufacturers and suppliers, supplier development also provides an opportunity for distinct manufacturers to collaborate in enhancing a joint supplier. This article proposes a model for the optimization of such joint supplier development programs, which incorporates the effects of trust in the manufacturer-to-manufacturer relationship. This article uses a model-predictive formulation to obtain optimal supplier development investment decisions to consider the strong dynamics of the markets. Thereby, the model is designed to be highly customizable to the needs and requirements of different companies. We analyzed the price development related to Mercedes’ A-Class cars and the cost development in the automotive sector over the last ten years in Germany. According to the obtained result, the proposed model shows a sensible b... [more]

An operating space partition method with control performance is proposed, where the heterogeneous multiple model is applied to a nonlinear system. Firstly, the heterogeneous multiple model is obtained from a nonlinear system at the given equilibrium points and transformed into a homogeneous multiple model with auxiliary variables. Secondly, an optimal problem where decision variables are composed of control input and boundary conditions of sub-models is formulated with the hybrid model developed from the homogeneous multiple model. The computational implementation of an optimal operating space partition algorithm is presented according to the Hamilton−Jacobi−Bellman equation and numerical method. Finally, a multiple model predictive controller is designed, and the computational implementation of the multiple model predictive controller is addressed with the auxiliary vectors. Furthermore, a continuous stirred tank reactor (CSTR) is used to confirm the effectiveness of the developed met... [more]

An analytical model predictive control (MPC) tuning method for multivariable first-order plus fractional dead time systems is presented in this paper. First, the decoupling condition of the closed-loop system is derived, based on which the considered multivariable MPC tuning problem is simplified to a pole placement problem. Given such a simplification, an analytical tuning method guaranteeing the closed-loop stability as well as pre-specified time-domain performance is developed. Finally, simulation examples are provided to show the effectiveness of the proposed method.

This paper presents an economic reliability-aware model predictive control (MPC) for the management of drinking water transport networks (DWNs). The proposed controller includes a new goal to increase the system and components reliability based on a finite horizon stochastic optimization problem with joint probabilistic (chance) constraints. The proposed approach is based on a single-layer economic optimization problem with dynamic constraints. The inclusion of components and system reliability in the MPC model using an Linear Parameter Varying (LPV) modeling approach aims to maximize the availability of the system by estimating system reliability. On the other hand, the use of a LPV-MPC control approach allows the controller to consider nonlinearities in the model in a linear like way. Moreover, the resulting MPC optimization problem can be formulated as a Quadratic Programming (QP) problem at each sampling time reducing the computational burden/time compared to solving a nonlinear pr... [more]

In this paper, we propose a triple-mode model predictive control (MPC) algorithm that uses future target information to improve tracking performance. To explicitly take into account the future target information in the MPC optimization, the proposed triple-mode control law encompasses three parts: (i) the future target information feedforward, (ii) the output feedback, and (iii) the extra degrees of freedom for constraint satisfaction. The first two parts of the control law are off-line designed through unconstrained MPC, and the optimal future trajectory horizon is obtained by golden section search based on the integral of squared error (ISE) criterion. The final part is calculated by the on-line MPC algorithm aiming to satisfy constraints. Furthermore, we analyze the feasibility and convergence properties of the proposed algorithm. The method is demonstrated by the simulation of the shell fundamental control problem and also tested on the coordinated control problem in the power plan... [more]

This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. Heating, ventilation, and air conditioning (HVAC) on/off behaviors are simulated by using Mathematical Program with Complementarity Constraints (MPCCs) and solved in near real time with a non-linear solver. Removing HVAC on/off as a discrete variable and replacing it with an MPCC reduces solve time. The results of this work indicate that energy management optimization significa... [more]

Grid-connected photovoltaic (PV) systems are now a common part of the modern power network. A recent development in the topology of these systems is the use of transformerless inverters. Although they are compact, cheap, and efficient, transformerless inverters suffer from chronic leakage current. Various researches have been directed toward evolving their performance and diminishing leakage current. This paper introduces the application of a model predictive control (MPC) algorithm to govern and improve the performance of a grid-tied neutral-point-clamped (NPC) 3-φ transformerless inverter powered by a PV panel. The transformerless inverter was linked to the grid via an inductor/capacitor (LC) filter. The filter elements, as well as the internal impedance of the grid, were considered in the system model. The discrete model of the proposed system was determined, and the algorithm of the MPC controller was established. Matlab’s simulations for the proposed system, controlled by the MPC... [more]

Model predictive control (MPC) has been widely studied for regulating frequency in stand-alone microgrids (MGs), owing to the advantages of MPC such as fast response and robustness against the parameter uncertainties. Understanding the impacts of system parameters on the control performance of MPC could be useful for the designing process of the controller to achieve better performance. This study analyzes the impact of system parameters on the control performance of MPC for frequency regulation in a stand-alone MG. The typical stand-alone MG, which consists of a diesel engine generator, an energy storage system (ESS), a wind turbine generator, and a load, is considered in this study. The diesel engine generator is in charge of primary frequency control whereas the ESS is responsible for secondary frequency control. The stand-alone MG is linearized to obtain the dynamic model that is used for designing MPC-based secondary frequency control. The robustness of MPC against the variation o... [more]

To strengthen the integration of the primary and secondary systems, a concept of Cyber Physical Systems (CPS) is introduced to construct a CPS in Power Systems (Power CPS). The most basic work of the Power CPS is to build an integration model which combines both a continuous process and a discrete process. The advanced form of smart grid, the Active Distribution Network (ADN) is a typical example of Power CPS. After designing the Power CPS model architecture and its application in ADN, a Hybrid System based model and control method of Power CPS is proposed in this paper. As an application example, ADN flexible load is modeled and controlled with ADN feeder power control by a control strategy which includes the normal condition and the underpowered condition. In this model and strategy, some factors like load power consumption and load functional demand are considered and optimized. In order to make up some of the deficiencies of centralized control, a distributed control method is pres... [more]

This work addresses the problems of uniquely specifying and robustly achieving user-specified product quality in a complex industrial batch process, which has been demonstrated using a lab-scale uni-axial rotational molding process. In particular, a data-driven modeling and control framework is developed that is able to reject raw material variation and achieve product quality which is specified through constraints on quality variables. To this end, a subspace state-space model of the rotational molding process is first identified from historical data generated in the lab. This dynamic model predicts the evolution of the internal mold temperature for a given set of input move trajectory (heater and compressed air profiles). Further, this dynamic model is augmented with a linear least-squares based quality model, which relates its terminal (states) prediction with key quality variables. For the lab-scale process, the chosen quality variables are sinkhole area, ultrasonic spectra amplitu... [more]

In this paper, an industrial application-oriented wind farm automatic generation control strategy is proposed to stabilize the wind farm power output under power limitation conditions. A wind farm with 20 units that are connected beneath four transmission lines is the selected control object. First, the power-tracking dynamic characteristic of wind turbines is modeled as a first-order inertial model. Based on the wind farm topology, the wind turbines are grouped into four clusters to fully use the clusters’ smoothing effect. A method for frequency-domain aggregation and approximation is used to obtain the clusters’ power-tracking equivalent model. From the reported analysis, a model predictive control strategy is proposed in this paper to optimize the rapidity and stability of the power-tracking performance. In this method, the power set-point for the wind farm is dispatched to the clusters. Then, the active power control is distributed from the cluster to the wind turbines using the c... [more]

The conventional modeling method of the marine steam turbine rotational speed control system (MSTRSCS) is based on Newton’s second law, constructing the mechanical equations between the rotational acceleration and the resultant torque. The disadvantages of this are nonlinearity, a complex structure and an infinite point of discontinuity in the rotational acceleration when the rotational speed is close to 0. Taking the kinetic energy of MSTRSCS as the output variable by using the kinetic energy theorem in this paper, we convert the complex nonlinear model of MSTRSCS into a linear one, since kinetic energy and rotational speed are homeomorphic. Model predictive control (MPC) adopts a discrete-time model, whereas the real system is time-continuous. Hence, poor performance is obtained in the real system when the time-discrete control law is applied to the MSTRSCS through the actuator. In case of high requirements for system accuracy and control performance, conventional MPC (CMPC) cannot m... [more]

Currently, the electric power production by wind energy conversion systems (WECSs) has increased significantly. Consequently, wind turbine (WT) generators are requested to fulfill the grid code (GC) requirements stated by network operators. In case of grid faults/voltage dips, a mismatch between the generated active power from the wind generator and the active power delivered to the grid is produced. The conventional approach is using a braking chopper (BC) in the DC-link to dissipate this active power. This paper proposes a fault-ride through (FRT) strategy for variable-speed WECSs based on permanent magnet synchronous generators (PMSGs). The proposed strategy exploits the rotor inertia of the WECS (inertia of the WT and PMSG) to store the surplus active power during the grid faults/voltage dips. Thus, no additional hardware components are requested. Furthermore, a direct model predictive control (DMPC) scheme for the PMSG is proposed in order to enhance the dynamic behavior of the WE... [more]

Plasma stability is one of the obstacles in the path to the successful operation of fusion devices. Numerical control-oriented codes as it is the case of the widely accepted RZIp may be used within Tokamak simulations. The novelty of this article relies in the hierarchical development of a dynamic control loop. It is based on a current profile Model Predictive Control (MPC) algorithm within a multiloop structure, where a MPC is developed at each step so as to improve the Proportional Integral Derivative (PID) global scheme. The inner control loop is composed of a PID-based controller that acts over the Multiple Input Multiple Output (MIMO) system resulting from the RZIp plasma model of the Tokamak à Configuration Variable (TCV). The coefficients of this PID controller are initially tuned using an eigenmode reduction over the passive structure model. The control action corresponding to the state of interest is then optimized in the outer MPC loop. For the sake of comparison, both the tr... [more]

In this paper, we develop and investigate the optimal control of a group of 104 heat pumps and a central Combined Heat and Power unit (CHP). The heat pumps supply space heating and domestic hot water to households. Each house has a buffer for domestic hot water and a floor heating system for space heating. Electricity for the heat pumps is generated by a central CHP unit, which also provides thermal energy to a district heating system. The paper reviews recent smart grid control approaches for central and distributed levels. An online algorithm is described based on the earliest deadline first theory that can be used on the aggregator level to control the CHP and to give signals to the heat pump controllers if they should start or should wait. The central controller requires only a limited amount of privacy-insensitive information from the heat pump controllers about their deadlines, which the heat pump controllers calculate for themselves by model predictions. In this way, a robust he... [more]

In this paper, the optimal operation of a stationary sub-critical 11 kW el organic Rankine cycle (ORC) unit for waste heat recovery (WHR) applications is investigated, both in terms of energy production and safety conditions. Simulation results of a validated dynamic model of the ORC power unit are used to derive a correlation for the evaporating temperature, which maximizes the power generation for a range of operating conditions. This idea is further extended using a perturbation-based extremum seeking (ES) algorithm to identify online the optimal evaporating temperature. Regarding safety conditions, we propose the use of the extended prediction self-adaptive control (EPSAC) approach to constrained model predictive control (MPC). Since it uses input/output models for prediction, it avoids the need for state estimators, making it a suitable tool for industrial applications. The performance of the proposed control strategy is compared to PID-like schemes. Results show that E... [more]

This work presents the implementation of a Model Predictive Control (MPC) scheme used to study the improvement of the thermal quality in aged residential buildings without any rehabilitation. The controller manages the heating system of an experimentally characterized model of a residential dwelling in a social block built during the decade of the 1960s located in the neighborhood of Otxarkoaga (Bilbao, Spain), so as to obtain an optimal energy efficiency performance. Due to the characteristics of the construction in those days, this kind of buildings suffer problems related to the use of awkward building materials and inefficient heating systems. A comparison with traditionally used ON-OFF hysteresis control is presented in order to demonstrate the energetic improvement provided by the MPC scheme. Besides, the variation of different parameters of the MPC is also studied to determine its influence over the energy consumption and comfort conditions.

A bidirectional converter able to manage storage is a basic power electronics device, and it is a major component of renewable energy sources, micro grid and also the smart grid concept. In this paper, single-phase bidirectional converter topology is discussed. The state space model has been derived, and a simple model based predictive current controller has been utilized to control the inverter. Control block diagrams have been designed with MATLAB and simulation results are presented and compared with experimental ones, giving credibility to the derived model and the designed control method.

This work presents the design and application of two control techniques—a model predictive control (MPC) and a proportional integral derivative control (PID), both in combination with a multilayer perceptron neural network—to produce hydrogen gas on-demand, in order to use it as an additive in a spark ignition internal combustion engine. For the design of the controllers, a control-oriented model, identified with the Hammerstein technique, was used. For the implementation of both controllers, only 1% of the overall air entering through the throttle valve reacted with hydrogen gas, allowing maintenance of the hydrogen⁻air stoichiometric ratio at 34.3 and the air⁻gasoline ratio at 14.6. Experimental results showed that the average settling time of the MPC controller was 1 s faster than the settling time of the PID controller. Additionally, MPC presented better reference tracking, error rates and standard deviation of 1.03 × 10 − 7 and 1.06 × 10 − 14 , and had a gre... [more]

In this paper, the data-driven predictive control method is applied to the clutch speed tracking control for the inertial phase of the shift process. While the clutch speed difference changes according to the predetermined trajectory, the purpose of improving the shift quality is achieved. The data-driven predictive control is implemented by combining the subspace identification with the model predictive control. Firstly, the predictive factors are constructed from the input and output data of the shift process via subspace identification, and then the factors are applied to a prediction equation. Secondly, an optimization function is deduced by taking the tracking error and the increments of inputs into accounts. Finally, the optimal solutions are solved through quadratic programming algorithm in Matlab software, and the future inputs of the system are obtained. The control algorithm is applied to the upshift process of an automatic transmission, the simulation results show that the a... [more]

This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several example... [more]

Due to insufficient endogenous production of erythropoietin, chronic kidney disease patients with anemia are often treated by the administration of recombinant human erythropoietin (EPO). The target of the treatment is to keep the patient’s hemoglobin level within a normal range. While conventional methods for guiding EPO dosing used by clinicians normally rely on a set of rules based on past experiences or retrospective studies, model predictive control (MPC) based dosage optimization is receiving attention recently. The objective of this paper is to incorporate the hemoglobin response model uncertainty into the dosage optimization decision making. Two methods utilizing Conditional Value at Risk (CVaR) are proposed for hemoglobin control in chronic kidney disease under model uncertainty. The first method includes a set-point tracking controller with the addition of CVaR constraints. The second method involves the use of CVaR directly in the cost function of the optimal control problem... [more]

A novel formulation for combined scheduling and control of multi-product, continuous chemical processes is introduced in which nonlinear model predictive control (NMPC) and noncyclic continuous-time scheduling are efficiently combined. A decomposition into nonlinear programming (NLP) dynamic optimization problems and mixed-integer linear programming (MILP) problems, without iterative alternation, allows for computationally light solution. An iterative method is introduced to determine the number of production slots for a noncyclic schedule during a prediction horizon. A filter method is introduced to reduce the number of MILP problems required. The formulation’s closed-loop performance with both process disturbances and updated market conditions is demonstrated through multiple scenarios on a benchmark continuously stirred tank reactor (CSTR) application with fluctuations in market demand and price for multiple products. Economic performance surpasses cyclic scheduling in all scenarios... [more]

Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated scheduling and control, closed-loop segregated scheduling and control, open-loop scheduling with consideration of process dynamics, and closed-loop integrated scheduling and control responsive to process disturbances and market fluctuations. Progressive economic benefit from dynamic rescheduling and integrating scheduling and control is shown on a continuously stirred tank reactor (CSTR) benchmark application in closed-loop simulations over 24 h. A fixed horizon integrated scheduling and control formulation for multi-product, continuous chemical processes is utilized, in which nonlinear model predictive control (NMPC) and continuous-time scheduling are combined.