Power Management of a Plug-in Hybrid Electric Vehicle Based on Cycle Energy Estimation

2012 Workshop on Engine and Powertrain Control,Simulation and ModelingThe International Federation of Automatic ControlRueil-Malmaison, France, October 23-25, 2012Plug-in Hybrid Electric Vehicles (PHEV) are being investigated in many research and development programs motivated by the urgent need for more fuel-efficient vehicles that produce fewer harmful emissions. There are many potential advantages of hybridization such as the improvement of transient power demand, the ability of regenerative braking and the opportunities for optimization of the vehicle efficiency. The coordination among the various power sources requires a high level of control in the vehicle. In order to solve the power management problem, the controller proposed in this work is divided into two levels: the upper one calculates the power that must be supplied by the engine at each moment taking into account the estimation of the energy that must be supplied by the powertrain until the end of the journey. The lower one manages the torque/speed set points for all the devices. Besides, the operation modes are changed according to some heuristic rules. Several simulation results are presented, showing that the proposed control strategy can provide good performance with low computational load

Non-linear models for a gypsum kiln. A comparative analysis

INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL. WORLD CONGRESS (15.2002.BARCELONA)This paper presents several non-linear models adjusted in order to capture the dynamics of a gypsum kiln. The behavior of this kind of processes is affected by nonlinear effects caused by the existence of disturbances and the coupling among some variables. The use of second order Volterra and Hammerstein models as appropriate solutions to describe the process dynamics is analyzed. A thorough study of the best model order and structure is performed. Coefficients that best fit real data are also selected. This work aims to obtain a good non-linear model in order to implement a non-linear predictive controller, able to improve the performances of those linear controllers already tested on the plant.Comisión Interministerial de Ciencia y Tecnología (CICYT) 1FD97-083

Application of simple cascade GPC with robust behaviour to a sugar refinery

This paper presents the application of a Generalized Pre dictive Controller (GPC) to sludge density control in a sugar factory. The loop is controlled by a cascade strategy where both the master and the slave controllers are predic tive ones. The control law is extremely simple to compute and the tuning is straightforward since a method to im plement GPC previously developed by the authors which is very simple to implement and tune has been used. The controllers are embedded in the existing control system needing the same computational requirements as pid rou tines. The original GPC algorithm is improved by the use of the socalled T polynomial which increases the stabil ity robustness by ltering the predictions in order to cope with model uncertainties and di erent process dynamics caused by changes in the process operating pointsMinisterio de Ciencia y Tecnología s TAP 96-884 (CICYT)Ministerio de Ciencia y Tecnología TAP 95-370 (CICYT

Application of iterative nonlinear model predictive control to a batch pilot reactor

IFAC WORLD CONGRESS (16) (16.2005.PRAGA, REPÚBLICA CHECA)The aim of this article is to present the Iterative Model Predictive Controller, inmpc, as a good candidate to control chemical batch reactors. The proposed control approach is derived from a model-based predictive control formulation which takes advantage of the repetitive nature of batch processes. The proposed controller combines the good qualities of Model Predictive Control (mpc) with the possibility of learning from past batches, that is the base of Iterative Control. It uses a nonlinear model and a quadratic objective function that is optimized in order to obtain the control law. The controller is tested on a batch pilot reactor, and a comparison with an Iterative Learning Controller (ilc) is made. Under input constraints and for this nonlinear plant, a fast convergence rate is obtained with the proposed controller, showing good operational results. Although the controller is designed for discrete-time systems, it is a necessary condition that the continuous-time model does not present blow-up characteristics. The batch pilot reactor emulates an exothermal chemical reaction by means of electrical heating

Iterative nonlinear model predictive control of a PH reactor. A comparative analysis

IFAC WORLD CONGRESS (16) (16.2005.PRAGA, REPÚBLICA CHECA)This paper describes the control of a batch pH reactor by a nonlinear predictive controller that improves performance by using data of past batches. The control strategy combines the feedback features of a nonlinear predictive controller with the learning capabilities of run-to-run control. The inclusion of real-time data collected during the on-going batch run in addition to those from the past runs make the control strategy capable not only of eliminating repeated errors but also of responding to new disturbances that occur during the run. The paper uses these ideas to devise an integrated controller that increases the capabilities of Nonlinear Model Predictive Control (NMPC) with batch-wise learning. This controller tries to improve existing strategies by the use of a nonlinear controller devised along the last-run trajectory as well as by the inclusion of filters. A comparison with a similar controller based upon a linear model is performed. Simulation results are presented in order to illustrate performance improvements that can be achieved by the new method over the conventional iterative controllers. Although the controller is designed for discrete-time systems, it can be applied to stable continuous plants after discretization

Iterative Nonlinear Control of a Semibatch Reactor. Stability Analysis

This paper presents the application of Iterative Nonlinear Model Predictive Control, INMPC, to a semibatch chemical reactor. The proposed control approach is derived from a model-based predictive control formulation which takes advantage of the repetitive nature of batch processes. The proposed controller combines the good qualities of Model Predictive Control (MPC) with the possibility of learning from past batches, that is the base of Iterative Control. It uses a nonlinear model and a quadratic objective function that is optimized in order to obtain the control law. A stability proof with unitary control horizon is given for nonlinear plants that are affine in control and have linear output map. The controller shows capabilities to learn the optimal trajectory after a few iterations, giving a better fit than a linear non-iterative MPC controller. The controller has applications in repetitive disturbance rejection, because they do not modify the model for control purposes. In this application, some experiments with a disturbance in inlet water temperature has been performed, getting good results.Ministerio de Ciencia y Tecnología DPI2004-07444-C04-0

Predictive control of a solar air conditioning plant with simultaneous identification

This paper presents the application of a predictive controller with simultaneous identification to a solar air conditioning plant. The time varying nature of the process makes necessary an adjustment of the controller parameters to the varying operational conditions. The main novelty with respect to classic adaptive MPC scheme is to penalize the identification error in the cost function used for control. The behaviour of the controller is illustrated by simulations and experimental results. The integration of identification and control avoids the tedious identification procedure that is necessary before the start-up of any predictive controller. This new adaptive MPC scheme shows its effectiveness in controlling the outlet temperature in the solar thermal plant.Ministerio de Ciencia y Tecnología DPI2004-07444-C04-0

Applications of Model Predictive Controllers in a Sugar Factory

INTERNATIONAL SYMPOSIUM ON ADVANCED CONTROL OF CHEMICAL PROCESSThis paper presents two applications of Model Predictive Control in a sugar factory: temperature control in the diffusion process and density control in the wastewater treatment plant. The implementation is done using a Generalized Predictive Controller (GPC) designed for a wide class of industrial process, with the same computational requirements as a PID routine and embedded in the existing control system. The processes have in common the existence of long and uncertain dead times, therefore the original GPC algorithm is improved by the use of the T polynomial, which increases the stability robustness by filtering the predictions.Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 96-0884Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 98-0541Comisión Interministerial de Ciencia y Tecnología (CICYT) 1FD97-083

Implementation of GPC for integrating··processes with low computational. Requirenients

This paper presents a straightforward method for im­plementing generalized predictive self-tuning controllers with low computational requirements. 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 processes having an integral effect. 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 selftuning GPC can be implemented in a straight­forward manner. An application to the control of a DC motor is given

Inferential sensor for the olive oil industry

This paper shows an inferential sensor that has been developed to be used in the olive oil industry. This sensor has been designed to measure two variables that appear in the elaboration of olive oil in a mill which are very difficult to be measured on line by a physical sensor. The knowledge of these variables on line is crucial for the optimal operation of the process, since they provide the state of the plant, allowing the development of a control strategy that can improve the quality and yield of the product. This sensor measures variables that in other case should come form laboratory analysis with large processing delays or from very expensive and difficult to use on line analysers. The sensor has been devised based upon artificial Neural Networks (NN) and has been implemented as a routine running on a Programmable Logic Controller (PLC) and successfully tested on a real plant.Ministerio de Ciencia y Tecnología DPI2001-2380-C02-0