Carlos Bordons

Generalized Predictive Control

This chapter describes one of the most popular predictive control algorithms: Generalized Predictive Control (GPC). The method is developed in detail, showing the general procedure to obtain the control law and its most outstanding characteristics. The original algorithm is extended to include the cases of measurable disturbances and change in the predictor. Close derivations of this controller such as CRHPC and Stable GPC are also treated here, illustrating the way they can be implemented.

Constrained Model Predictive Control

The control problem was formulated in the previous chapters considering all signals to possess an unlimited range. This is not very realistic because in practice all processes are subject to constraints. Actuators have a limited range of action and a limited slew rate, as is the case of control valves limited by a fully closed and fully open position and a maximum slew rate. Constructive or safety reasons, as well as sensor range, cause bounds in process variables, as in the case of levels in tanks, flows in pipes, and pressures in deposits. Furthermore, in practice, the operating points of plants are determined to satisfy economic goals and lie at the intersection of certain constraints. The control system normally operates close to the limits and constraint violations are likely to occur. The control system, especially for longrange predictive control, has to anticipate constraint violations and correct them in an appropriate way. Although input and output constraints are basically treated in the same way, as is shown in this chapter, the implications of the constraints differ. Output constraints are mainly due to safety reasons and must be controlled in advance because output variables are affected by process dynamics. Input (or manipulated) variables can always be kept in bound by the controller by clipping the control action to a value satisfying amplitude and slew rate constraints.

Minimum Fuel Consumption Strategy for PEM Fuel Cells

This paper proposes a proton exchange membrane fuel cell control strategy to produce the power requested by an electrical load, minimizing the fuel consumption and also providing a regulated DC bus voltage to the load. The power system consists of a hybrid fuel cell/capacitor topology, and the control objective is to follow the minimum fuel consumption points for a given load power profile. This is done by controlling the air pump voltage and regulating the fuel cell current through a DC/DC switching converter. Moreover, the design and control parameters of the output DC bus are discussed, and the calculations are adjusted to a Ballard 1.2-kW Nexa power module. Finally, the control results, fuel consumption, and fuel cell protection against oxygen starvation phenomenon are analyzed and experimentally validated, contrasting its performance with the Nexa power module internal control system.

Improving the robustness of dead-time compensating PI controllers

Abstract This paper describes a PI controller with dead-time compensation that presents robust behaviour. The formulation is based on a Smith predictor structure plus the addition of a filter acting on the error between the output and its prediction in order to improve robustness. The controller is very simple, and the filter needs no adjustment, since it is directly related to the plant dead-time. Simulations and experimental results show that this controller can improve the performance of related algorithms.

A generalized predictive controller for a wide class of industrial processes

This paper presents a formulation of generalized predictive control (GPC), easy to implement and tune, that is valid for the majority of industrial processes. The method makes use of the fact that a generalized predictive controller results in a control law that can be described with few parameters. The controller has been developed for a wide class of processes in industry and a set of simple functions relating the controller parameters to the process parameters has been obtained. With this set of functions either a fixed or a self-tuning GPC can be implemented in a straightforward manner. The influence of modeling errors is analyzed, and the results obtained show the stability robustness of the method, especially with respect to uncertainties in time constant and static gain.

Real-Time Implementation of a Constrained MPC for Efficient Airflow Control in a PEM Fuel Cell

Fuel cells represent an area of great industrial interest due to the possibility to generate clean energy for stationary and automotive applications. It is clear that the proper performance of these devices is closely related to the kind of control that is used; therefore, a study of improved control alternatives is fully justified. The air-supply control is widely used to guarantee safety and to achieve a high performance. This paper deals with this control loop, proposing and comparing two control objectives aimed at satisfying the oxygen starvation avoidance criterion and the maximum efficiency criterion, respectively. The control architecture is based on a constrained explicit model predictive control (MPC) law suitable for real-time implementation due to its low computational demands. The proposed controller is implemented and evaluated on a 1.2-kW polymer electrolyte membrane or proton exchange membrane fuel-cell test bench, thus obtaining real data which show that the maximum efficiency criterion does not conflict with the starvation avoidance criterion and allows system performance improvements of up to 3.46%. Moreover, experimental results utilizing the explicit MPC approach also show improved transient responses compared to those of the manufacturers control law.

Nonlinear model prédictive control : An introductory review

Model Predictive Control (MPC) originated in the late seventies and has developed considerably since then. The term Model Predictive Control does not designate a specific control strategy but rather an ample range of control methods which make explicit use of a model of the process to obtain the control signal by minimizing an objective function. The ideas, appearing in greater or lesser degree in the predictive control family, are basically the explicit use of a model to predict the process output at future time instants (horizon), the calculation of a control sequence minimizing an objective function and the use of a receding strategy, so that at each instant the horizon is displaced towards the future, which involves the application of the first control signal of the sequence calculated at each step.

Constrained predictive control strategies for PEM fuel cells

This paper presents some results in advanced control of PEM fuel cells. The work analyzes the use of control strategies that try to fulfill different operational objectives. A preliminary study of PEM fuel requirements leads to three different control criteria: tracking of a desired output voltage, avoidance of starvation and maximization of efficiency. These criteria can be achieved by the design of different control algorithms based on generalized predictive control (GPC), extended with the consideration of measurable disturbances. This controller can also consider two constraints: one physical limit such as compressor voltage, and one operational limit which is introduced in order to avoid starvation: oxygen excess ratio

Design, Planning and Management of a Hydrogen-Based Microgrid

Efficient energy generation and consumption is a key factor to achieve ambitious goals related to air pollution and climate change. Modern electricity networks can include different kind of sources, such as renewable energy sources (RES). Then, hybrid systems are obtained by combining several sources and storage types in the new concept called microgrid (MG). In order to draw the best performance from these hybrid systems, a proper design and operation is essential. The purpose of this paper is to present a detailed report to properly undertake the building and management of a hydrogen MG in a simple and reliable way to continue struggling for more comprehension on the MG operation modes and prevent the reported failures in the literature. The experimental platform developed will provide the valuable knowledge and solid guidelines for future test centers and demonstration plants. The MG, located in Seville, Spain, incorporates an electrolyzer, metal hydride storage, fuel cell, and a battery bank as main components. The developed MG laboratory has been successfully tested. The results indicate reliable operation incorporating the hydrogen and batteries as energy storage.

Adaptive generalized predictive control of a distributed collector field

This brief paper presents an adaptive generalized predictive controller for the distributed collector field of a solar plant. The controller has been implemented using a method which avoids the heavy computation requirement of this type of controller. The method can he applied to processes that can be modeled by the reaction curve method, that is, a wide range of processes in industry. The results obtained in the plant are shown. >