Local WMR navigation with monocular data

This article presents recent WMR (wheeled mobile robot) navigation experiences using local perception knowledge provided by monocular and odometer systems. A local narrow perception horizon is used to plan safety trajectories towards the objective. Therefore, monocular data are proposed as a way to obtain real time local information by building two dimensional occupancy grids through a time integration of the frames. The path planning is accomplished by using attraction potential fields, while the trajectory tracking is performed by using model predictive control techniques. The results are faced to indoor situations by using the lab available platform consisting in a differential driven mobile robot

Controle preditivo robusto baseado em desigualdades matriciais lineares aplicado a um sistema de tanques acoplados

This work deals with an on-line control strategy based on Robust Model Predictive Control (RMPC) technique applied in a real coupled tanks system. This process consists of two coupled tanks and a pump to feed the liquid to the system. The control objective (regulator problem) is to keep the tanks levels in the considered operation point even in the presence of disturbance. The RMPC is a technique that allows explicit incorporation of the plant uncertainty in the problem formulation. The goal is to design, at each time step, a state-feedback control law that minimizes a 'worst-case' infinite horizon objective function, subject to constraint in the control. The existence of a feedback control law satisfying the input constraints is reduced to a convex optimization over linear matrix inequalities (LMIs) problem. It is shown in this work that for the plant uncertainty described by the polytope, the feasible receding horizon state feedback control design is robustly stabilizing. The software implementation of the RMPC is made using Scilab, and its communication with Coupled Tanks Systems is done through the OLE for Process Control (OPC) industrial protocol

Neural Network Based Approach for Developing the Enterprise Strategy

Modern enterprises work in highly dynamic environment. Thus, the developing of company strategy is of crucial importance. It determines the surviving of the enterprise and its evolution. Adapting the desired management goal in accordance with the environment changes is a complex problem. In the present paper, an approach for solving this problem is suggested. It is based on predictive control philosophy. The enterprise is modelled as a cybernetic system and the future plant response is predicted by a neural network model. The predictions are passed to an optimization routine, which attempts to minimize the quadratic performance criterion.

Commande prédictive non linéaire d’un séchoir à lit fluidisé pour granules pharmaceutiques

Avec la disponibilité de capteurs fiables de teneur en eau exploitant la spectroscopie proche infrarouge (NIR pour near-infrared) et les outils chimiométriques, il est maintenant possible d’appliquer des stratégies de commande en ligne sur plusieurs procédés de séchage dans l’industrie pharmaceutique. Dans cet ouvrage, le séchage de granules pharmaceutiques avec un séchoir à lit fluidisé discontinu (FBD pour fluidized bed dryer) de taille pilote est étudié à l’aide d’un capteur d’humidité spectroscopique. Des modifications électriques sont d’abord effectuées sur le séchoir instrumenté afin d’acheminer les signaux mesurés et manipulés à un périphérique d’acquisition. La conception d’une interface homme-machine permet ensuite de contrôler directement le séchoir à l’aide d’un ordinateur portable. Par la suite, un algorithme de commande prédictive (NMPC pour nonlinear model predictive control), basée sur un modèle phénoménologique consolidé du FBD, est exécuté en boucle sur ce même ordinateur. L’objectif est d’atteindre une consigne précise de teneur en eau en fin de séchage tout en contraignant la température des particules ainsi qu’en diminuant le temps de lot. De plus, la consommation énergétique du FBD est explicitement incluse dans la fonction objectif du NMPC. En comparant à une technique d’opération typique en industrie (principalement en boucle ouverte), il est démontré que le temps de séchage et la consommation énergétique peuvent être efficacement gérés sur le procédé pilote tout en limitant plusieurs problèmes d’opération comme le sous-séchage, le surséchage ou le surchauffage des granules.

Closed-loop control of mean arterial blood pressure during surgery with alfentanil: clinical evaluation of a novel model-based predictive controller

BACKGROUND: In contrast to hypnosis, there is no surrogate parameter for analgesia in anesthetized patients. Opioids are titrated to suppress blood pressure response to noxious stimulation. The authors evaluated a novel model predictive controller for closed-loop administration of alfentanil using mean arterial blood pressure and predicted plasma alfentanil concentration (Cp Alf) as input parameters. METHODS: The authors studied 13 healthy patients scheduled to undergo minor lumbar and cervical spine surgery. After induction with propofol, alfentanil, and mivacurium and tracheal intubation, isoflurane was titrated to maintain the Bispectral Index at 55 (+/- 5), and the alfentanil administration was switched from manual to closed-loop control. The controller adjusted the alfentanil infusion rate to maintain the mean arterial blood pressure near the set-point (70 mmHg) while minimizing the Cp Alf toward the set-point plasma alfentanil concentration (Cp Alfref) (100 ng/ml). RESULTS: Two patients were excluded because of loss of arterial pressure signal and protocol violation. The alfentanil infusion was closed-loop controlled for a mean (SD) of 98.9 (1.5)% of presurgery time and 95.5 (4.3)% of surgery time. The mean (SD) end-tidal isoflurane concentrations were 0.78 (0.1) and 0.86 (0.1) vol%, the Cp Alf values were 122 (35) and 181 (58) ng/ml, and the Bispectral Index values were 51 (9) and 52 (4) before surgery and during surgery, respectively. The mean (SD) absolute deviations of mean arterial blood pressure were 7.6 (2.6) and 10.0 (4.2) mmHg (P = 0.262), and the median performance error, median absolute performance error, and wobble were 4.2 (6.2) and 8.8 (9.4)% (P = 0.002), 7.9 (3.8) and 11.8 (6.3)% (P = 0.129), and 14.5 (8.4) and 5.7 (1.2)% (P = 0.002) before surgery and during surgery, respectively. A post hoc simulation showed that the Cp Alfref decreased the predicted Cp Alf compared with mean arterial blood pressure alone. CONCLUSION: The authors' controller has a similar set-point precision as previous hypnotic controllers and provides adequate alfentanil dosing during surgery. It may help to standardize opioid dosing in research and may be a further step toward a multiple input-multiple output controller.

Predictive motion control of a mirosot mobile robot

This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model

Millora experimental de les lleis de control predictiu aplicades sobre la plataforma PRIM I, per el seguiment de trajectòries

Aquest projecte s’aplica sobre el robot PRIM (Plataforma Robotitzada d’Informació Multimèdia), un robot autònom no humanoide creat el 2004 per Ateneu Informàtic (AI) que permet realitzar trajectòries 2D gràcies a un sistema de tracció format per dues rodes motrius propulsades independentment. La plataforma PRIM és controlada a partir del control predictiu, aquest control es va implementar en un projecte anterior, creat per l’Alexandre Blasco Gutierrez i titulat “Implementació de tècniques MPC (Model Predictiu Control) sobre la plataforma PRIM I”. El que es pretén en aquest projecte és millorar els resultats obtinguts en el passat projecte reformulant la llei de control i analitzar les discrepàncies obtingudes en les metodologies que s’utilitzen per minimitzar la funció de costos a partir de simulacions de trajectòries

Millora experimental de les lleis de control predictiu aplicades sobre la plataforma PRIM I, per el seguiment de trajectòries

Aquest projecte s’aplica sobre el robot PRIM (Plataforma Robotitzada d’Informació Multimèdia), un robot autònom no humanoide creat el 2004 per Ateneu Informàtic (AI) que permet realitzar trajectòries 2D gràcies a un sistema de tracció format per dues rodes motrius propulsades independentment. La plataforma PRIM és controlada a partir del control predictiu, aquest control es va implementar en un projecte anterior, creat per l’Alexandre Blasco Gutierrez i titulat “Implementació de tècniques MPC (Model Predictiu Control) sobre la plataforma PRIM I”. El que es pretén en aquest projecte és millorar els resultats obtinguts en el passat projecte reformulant la llei de control i analitzar les discrepàncies obtingudes en les metodologies que s’utilitzen per minimitzar la funció de costos a partir de simulacions de trajectòries

Predictive motion control of a mirosot mobile robot

This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model

Predictive computed-torque control of a PUMA 560 manipulator robot

This paper describes the integration of constrained predictive control and computed-torque control, and its application on a six degree-of-freedom PUMA 560 manipulator arm. The real-time implementation was based on SIMULINK, with the predictive controller and the computed-torque control law implemented in the C programming language. The constrained predictive controller solved a quadratic programming problem at every sampling interval, which was as short as 10 ms, using a prediction horizon of 150 steps and an 18th order state space model.

Review and future directions in the modelling and control of continuous drum granulation

Many granulation plants operate well below design capacity, suffering from high recycle rates and even periodic instabilities. This behaviour cannot be fully predicted using the present models. The main objective of the paper is to provide an overview of the current status of model development for granulation processes and suggest future directions for research and development. The end-use of the models is focused on the optimal design and control of granulation plants using the improved predictions of process dynamics. The development of novel models involving mechanistically based structural switching methods is proposed in the paper. A number of guidelines are proposed for the selection of control relevant model structures. (C) 2002 Published by Elsevier Science B.V.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.

Distributed MPC for Thermal Comfort and Load Allocation with Energy Auction

This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited shared energy resource using an integrated demand-side management approach that involves a power price auction and an appliance loads allocation scheme. The control objective for each subsystem (house or building) aims to minimize the energy cost while maintaining the indoor temperature inside comfort limits. In a distributed coordinated multi-agent ecosystem, each house or building control agent achieves its objectives while sharing, among them, the available energy through the introduction of particular coupling constraints in their underlying optimization problem. Coordination is maintained by a daily green energy auction bring in a demand-side management approach. Also the implemented distributed MPC algorithm is described and validated with simulation studies.

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches.

Desarrollo y aplicación de estrategias de control avanzado en procesos biológicos de tratamiento de efluentes contaminados

En este proyecto se ha desarrollado estrategias de control avanzadas para plantas de depuración de aguas residuales urbanas que eliminan conjuntamente materia orgánica, nitrógeno y fósforo. Las estrategias se han basado en el estudio multivariable del comportamiento del sistema, que ha producido subsidios para la utilización de lazos de control feedforward, de control predictivo y de un control de costes que automáticamente enviaba las consignas más adecuadas para los controladores de proceso. Para el desarrollo de las estrategias, se ha creado un sistema virtual de simulación (simulador) de plantas de depuradoras, basado en datos de literatura. Para el caso de una planta real, se ha desarrollado un simulador de la planta de Manresa (Catalunya). Sin embargo, el sistema de Manresa se ha utilizado exclusivamente para auxiliar los ingenieros de la planta en la tomada de decisiones de cambio de configuración para que la eliminación de fósforo se dé por la ruta biológica y no por la ruta química. La implementación de los simuladores ha permitido hacer muchas pruebas que en una planta real demandarían mucho tiempo y consumirían muchos recursos energéticos y financieros. Las estrategias de control más elaboradas han podido ahorrar hasta 150.000,00 Euros por año en relación a la operación de la planta sin el control automático. Cuanto a los estudios del modelo de la planta real, se concluyó que la eliminación biológica de fósforo puede sustituir el actual proceso químico de eliminación de fósforo, bajando los costes operacionales (costes del agente precipitante).