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.

The systematic design of an advanced and efficient control strategy for controlling critical quality attributes of the tablet compaction operation is necessary to increase the robustness of a continuous pharmaceutical manufacturing process and for real time release. A process model plays a very important role to design, evaluate and tune the control system. However, much less attention has been made to develop a validated control relevant model for tablet compaction process that can be systematically applied for design, evaluation, tuning and thereby implementation of the control system. In this work, a dynamic tablet compaction model capable of predicting linear and nonlinear process responses has been successfully developed and validated. The nonlinear model is based on a series of transfer functions and static polynomial models. The model has been applied for control system design, tuning and evaluation and thereby facilitate the control system implementation into the pilot-plant wi... [more]

We present a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows modeling (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes, when using discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times; (5) feedback on task-yield estimates before the task finishes; (6) task termination during its execution; (7) post-production storage of material in unit; and (8) unit capacity degradation and maintenance. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings. Finally, we demonstrate the effectiveness of this model on a case study from the field of bio-manufacturin... [more]

This paper is about the incremental computation of control sequences for discrete event systems in uncertain environments where uncontrollable events may occur. Timed Petri nets are used for this purpose. The aim is to drive the marking of the net from an initial value to a reference one, in minimal or near-minimal time, by avoiding forbidden markings, deadlocks, and dead branches. The approach is similar to model predictive control with a finite set of control actions. At each step only a small area of the reachability graph is explored: this leads to a reasonable computational complexity. The robustness of the resulting trajectory is also evaluated according to a risk probability. A sufficient condition is provided to compute robust trajectories. The proposed results are applicable to a large class of discrete event systems, in particular in the domains of flexible manufacturing. However, they are also applicable to other domains as communication, computer science, transportation, an... [more]

This paper discusses the problems associated with the implementation of Real Time Optimization/Model Predictive Control (RTO/MPC) systems, taking as reference the hydrogen distribution network of an oil refinery involving eighteen plants. This paper addresses the main problems related to the operation of the network, combining data reconciliation and a RTO system, designed for the optimal generation and redistribution of hydrogen, with a predictive controller for the on-line implementation of the optimal policies. This paper describes the architecture of the implementation, showing how RTO and MPC can be integrated, as well as the benefits obtained in terms of improved information about the process, increased hydrocarbon load to the treatment plants and reduction of the hydrogen required for performing the operations.

Quench hardening is the process of strengthening and hardening ferrous metals and alloys by heating the material to a specific temperature to form austenite (austenitization), followed by rapid cooling (quenching) in water, brine or oil to introduce a hardened phase called martensite. The material is then often tempered to increase toughness, as it may decrease from the quench hardening process. The austenitization process is highly energy-intensive and many of the industrial austenitization furnaces were built and equipped prior to the advent of advanced control strategies and thus use large, sub-optimal amounts of energy. The model computes the energy usage of the furnace and the part temperature profile as a function of time and position within the furnace under temperature feedback control. In this paper, the aforementioned model is used to simulate the furnace for a batch of forty parts under heuristic temperature set points suggested by the operators of the plant. A model predict... [more]