Model based experimental investigation on Powered Gait Orthosis (PGO)

Research on rehabilitation showed that appropriate and repetitive mechanical movements can help spinal cord injured individuals to restore their functional standing and walking. The objective of this paper was to achieve appropriate and repetitive joint movements and approximately normal gait through the PGO by replicating normal walking, and to minimize the energy consumption for both patients and the device. A model based experimental investigative approach is presented in this dissertation. First, a human model was created in Ideas and human walking was simulated in Adams. The main feature of this model was the foot ground contact model, which had distributed contact points along the foot and varied viscoelasticity. The model was validated by comparison of simulated results of normal walking and measured ones from the literature. It was used to simulate current PGO walking to investigate the real causes of poor function of the current PGO, even though it had joint movements close to normal walking. The direct cause was one leg moving at a time, which resulted in short step length and no clearance after toe off. It can not be solved by simply adding power on both hip joints. In order to find a better answer, a PGO mechanism model was used to investigate different walking mechanisms by locking or releasing some joints. A trade-off between energy consumption, control complexity and standing position was found. Finally a foot release PGO virtual model was created and simulated and only foot release mechanism was developed into a prototype. Both the release mechanism and the design of foot release were validated through the experiment by adding the foot release on the current PGO. This demonstrated an advancement in improving functional aspects of the current PGO even without a whole physical model of foot release PGO for comparison.

Sensorless explicit model predictive control for multi-phase buck converters with variable output voltage

The purpose of this paper is to use the predictive control to take advantage of the future information in order to improve the reference tracking. The control attempts to increase the bandwidth of the conventional regulators by using the future information of the reference, which is supposed to be known in advance. A method for designing a controller is also proposed. A comparison in simulation with a conventional regulator is made controlling a four-phase Buck converter. Advantages and disadvantages are analyzed based on simulation results.

Model-based Self-awareness Patterns for Autonomy

Los sistemas técnicos son cada vez más complejos, incorporan funciones más avanzadas, están más integrados con otros sistemas y trabajan en entornos menos controlados. Todo esto supone unas condiciones más exigentes y con mayor incertidumbre para los sistemas de control, a los que además se demanda un comportamiento más autónomo y fiable. La adaptabilidad de manera autónoma es un reto para tecnologías de control actualmente. El proyecto de investigación ASys propone abordarlo trasladando la responsabilidad de la capacidad de adaptación del sistema de los ingenieros en tiempo de diseño al propio sistema en operación. Esta tesis pretende avanzar en la formulación y materialización técnica de los principios de ASys de cognición y auto-consciencia basadas en modelos y autogestión de los sistemas en tiempo de operación para una autonomía robusta. Para ello el trabajo se ha centrado en la capacidad de auto-conciencia, inspirada en los sistemas biológicos, y se ha explorado la posibilidad de integrarla en la arquitectura de los sistemas de control. Además de la auto-consciencia, se han explorado otros temas relevantes: modelado funcional, modelado de software, tecnología de los patrones, tecnología de componentes, tolerancia a fallos. Se ha analizado el estado de la técnica en los ámbitos pertinentes para las cuestiones de la auto-consciencia y la adaptabilidad en sistemas técnicos: arquitecturas cognitivas, control tolerante a fallos, y arquitecturas software dinámicas y computación autonómica. El marco teórico de ASys existente de sistemas autónomos cognitivos ha sido adaptado para servir de base para este análisis de autoconsciencia y adaptación y para dar sustento conceptual al posterior desarrollo de la solución. La tesis propone una solución general de diseño para la construcción de sistemas autónomos auto-conscientes. La idea central es la integración de un meta-controlador en la arquitectura de control del sistema autónomo, capaz de percibir la estado funcional del sistema de control y, si es necesario, reconfigurarlo en tiempo de operación. Esta solución de metacontrol se ha formalizado en cuatro patrones de diseño: i) el Patrón Metacontrol, que define la integración de un subsistema de metacontrol, responsable de controlar al propio sistema de control a través de la interfaz proporcionada por su plataforma de componentes, ii) el patrón Bucle de Control Epistémico, que define un bucle de control cognitivo basado en el modelos y que se puede aplicar al diseño del metacontrol, iii) el patrón de Reflexión basada en Modelo Profundo propone una solución para construir el modelo ejecutable utilizado por el meta-controlador mediante una transformación de modelo a modelo a partir del modelo de ingeniería del sistema, y, finalmente, iv) el Patrón Metacontrol Funcional, que estructura el meta-controlador en dos bucles, uno para el control de la configuración de los componentes del sistema de control, y otro sobre éste, controlando las funciones que realiza dicha configuración de componentes; de esta manera las consideraciones funcionales y estructurales se desacoplan. La Arquitectura OM y el metamodelo TOMASys son las piezas centrales del marco arquitectónico desarrollado para materializar la solución compuesta de los patrones anteriores. El metamodelo TOMASys ha sido desarrollado para la representación de la estructura y su relación con los requisitos funcionales de cualquier sistema autónomo. La Arquitectura OM es un patrón de referencia para la construcción de una metacontrolador integrando los patrones de diseño propuestos. Este meta-controlador se puede integrar en la arquitectura de cualquier sistema control basado en componentes. El elemento clave de su funcionamiento es un modelo TOMASys del sistema decontrol, que el meta-controlador usa para monitorizarlo y calcular las acciones de reconfiguración necesarias para adaptarlo a las circunstancias en cada momento. Un proceso de ingeniería, complementado con otros recursos, ha sido elaborado para guiar la aplicación del marco arquitectónico OM. Dicho Proceso de Ingeniería OM define la metodología a seguir para construir el subsistema de metacontrol para un sistema autónomo a partir del modelo funcional del mismo. La librería OMJava proporciona una implementación del meta-controlador OM que se puede integrar en el control de cualquier sistema autónomo, independientemente del dominio de la aplicación o de su tecnología de implementación. Para concluir, la solución completa ha sido validada con el desarrollo de un robot móvil autónomo que incorpora un meta-controlador con la Arquitectura OM. Las propiedades de auto-consciencia y adaptación proporcionadas por el meta-controlador han sido validadas en diferentes escenarios de operación del robot, en los que el sistema era capaz de sobreponerse a fallos en el sistema de control mediante reconfiguraciones orquestadas por el metacontrolador. ABSTRACT Technical systems are becoming more complex, they incorporate more advanced functionalities, they are more integrated with other systems and they are deployed in less controlled environments. All this supposes a more demanding and uncertain scenario for control systems, which are also required to be more autonomous and dependable. Autonomous adaptivity is a current challenge for extant control technologies. The ASys research project proposes to address it by moving the responsibility for adaptivity from the engineers at design time to the system at run-time. This thesis has intended to advance in the formulation and technical reification of ASys principles of model-based self-cognition and having systems self-handle at runtime for robust autonomy. For that it has focused on the biologically inspired capability of self-awareness, and explored the possibilities to embed it into the very architecture of control systems. Besides self-awareness, other themes related to the envisioned solution have been explored: functional modeling, software modeling, patterns technology, components technology, fault tolerance. The state of the art in fields relevant for the issues of self-awareness and adaptivity has been analysed: cognitive architectures, fault-tolerant control, and software architectural reflection and autonomic computing. The extant and evolving ASys Theoretical Framework for cognitive autonomous systems has been adapted to provide a basement for this selfhood-centred analysis and to conceptually support the subsequent development of our solution. The thesis proposes a general design solution for building self-aware autonomous systems. Its central idea is the integration of a metacontroller in the control architecture of the autonomous system, capable of perceiving the functional state of the control system and reconfiguring it if necessary at run-time. This metacontrol solution has been formalised into four design patterns: i) the Metacontrol Pattern, which defines the integration of a metacontrol subsystem, controlling the domain control system through an interface provided by its implementation component platform, ii) the Epistemic Control Loop pattern, which defines a modelbased cognitive control loop that can be applied to the design of such a metacontroller, iii) the Deep Model Reflection pattern proposes a solution to produce the online executable model used by the metacontroller by model-to-model transformation from the engineering model, and, finally, iv) the Functional Metacontrol pattern, which proposes to structure the metacontroller in two loops, one for controlling the configuration of components of the controller, and another one on top of the former, controlling the functions being realised by that configuration; this way the functional and structural concerns become decoupled. The OM Architecture and the TOMASys metamodel are the core pieces of the architectural framework developed to reify this patterned solution. The TOMASys metamodel has been developed for representing the structure and its relation to the functional requirements of any autonomous system. The OM architecture is a blueprint for building a metacontroller according to the patterns. This metacontroller can be integrated on top of any component-based control architecture. At the core of its operation lies a TOMASys model of the control system. An engineering process and accompanying assets have been constructed to complete and exploit the architectural framework. The OM Engineering Process defines the process to follow to develop the metacontrol subsystem from the functional model of the controller of the autonomous system. The OMJava library provides a domain and application-independent implementation of an OM Metacontroller than can be used in the implementation phase of OMEP. Finally, the complete solution has been validated in the development of an autonomous mobile robot that incorporates an OM metacontroller. The functional selfawareness and adaptivity properties achieved thanks to the metacontrol system have been validated in different scenarios. In these scenarios the robot was able to overcome failures in the control system thanks to reconfigurations performed by the metacontroller.

Parametric CAD model based shape optimization using adjoint functions

Adjoint methods have proven to be an efficient way of calculating the gradient of an objective function with respect to a shape parameter for optimisation, with a computational cost nearly independent of the number of the design variables [1]. The approach in this paper links the adjoint surface sensitivities (gradient of objective function with respect to the surface movement) with the parametric design velocities (movement of the surface due to a CAD parameter perturbation) in order to compute the gradient of the objective function with respect to CAD variables.
For a successful implementation of shape optimization strategies in practical industrial cases, the choice of design variables or parameterisation scheme used for the model to be optimized plays a vital role. Where the goal is to base the optimization on a CAD model the choices are to use a NURBS geometry generated from CAD modelling software, where the position of the NURBS control points are the optimisation variables [2] or to use the feature based CAD model with all of the construction history to preserve the design intent [3]. The main advantage of using the feature based model is that the optimized model produced can be directly used for the downstream applications including manufacturing and process planning.
This paper presents an approach for optimization based on the feature based CAD model, which uses CAD parameters defining the features in the model geometry as the design variables. In order to capture the CAD surface movement with respect to the change in design variable, the “Parametric Design Velocity” is calculated, which is defined as the movement of the CAD model boundary in the normal direction due to a change in the parameter value.
The approach presented here for calculating the design velocities represents an advancement in terms of capability and robustness of that described by Robinson et al. [3]. The process can be easily integrated to most industrial optimisation workflows and is immune to the topology and labelling issues highlighted by other CAD based optimisation processes. It considers every continuous (“real value”) parameter type as an optimisation variable, and it can be adapted to work with any CAD modelling software, as long as it has an API which provides access to the values of the parameters which control the model shape and allows the model geometry to be exported. To calculate the movement of the boundary the methodology employs finite differences on the shape of the 3D CAD models before and after the parameter perturbation. The implementation procedure includes calculating the geometrical movement along a normal direction between two discrete representations of the original and perturbed geometry respectively. Parametric design velocities can then be directly linked with adjoint surface sensitivities to extract the gradients to use in a gradient-based optimization algorithm.
The optimisation of a flow optimisation problem is presented, in which the power dissipation of the flow in an automotive air duct is to be reduced by changing the parameters of the CAD geometry created in CATIA V5. The flow sensitivities are computed with the continuous adjoint method for a laminar and turbulent flow [4] and are combined with the parametric design velocities to compute the cost function gradients. A line-search algorithm is then used to update the design variables and proceed further with optimisation process.

Temperature regulation of a pilot-scale batch reaction system via explicit model predictive control

In this paper, the temperature of a pilot-scale batch reaction system is modeled towards the design of a controller based on the explicit model predictive control (EMPC) strategy -- Some mathematical models are developed from experimental data to describe the system behavior -- The simplest, yet reliable, model obtained is a (1,1,1)-order ARX polynomial model for which the mentioned EMPC controller has been designed -- The resultant controller has a reduced mathematical complexity and, according to the successful results obtained in simulations, will be used directly on the real control system in a next stage of the entire experimental framework

Optimizing last mile delivery using public transport with multiagent based control

The majority of research work carried out in the field of Operations-Research uses methods and algorithms to optimize the pick-up and delivery problem. Most studies aim to solve the vehicle routing problem, to accommodate optimum delivery orders, vehicles etc. This paper focuses on green logistics approach, where existing Public Transport infrastructure capability of a city is used for the delivery of small and medium sized packaged goods thus, helping improve the situation of urban congestion and greenhouse gas emissions reduction. It carried out a study to investigate the feasibility of the proposed multi-agent based simulation model, for efficiency of cost, time and energy consumption. Multimodal Dijkstra Shortest Path algorithm and Nested Monte Carlo Search have been employed for a two-phase algorithmic approach used for generation of time based cost matrix. The quality of the tour is dependent on the efficiency of the search algorithm implemented for plan generation and route planning. The results reveal a definite advantage of using Public Transportation over existing delivery approaches in terms of energy efficiency.

Brain emotional learning based control of a SDOF structural system with a MR damper

This paper describes the application of a Brain Emotional Learning (BEL) controller to improve the response of a SDOF structural system under an earthquake excitation using a magnetorheological (MR) damper. The main goal is to study the performance of a BEL based semi-active control system to generate the control signal for a MR damper. The proposed approach consists of a two controllers: a primary controller based on a BEL algorithm that determines the desired damping force from the system response and a secondary controller that modifies the input current to the MR damper to generate a reference damping force. A parametric model of the damper is used to predict the damping force based on the piston motion and also the current input. A Simulink model of the structural system is developed to analyze the effectiveness of the semi-active controller. Finally, the numerical results are presented and discussed.

DYNAMIC MODEL BASED STATE ESTIMATION IN A HEAVY DUTY DIESEL AFTERTREATMENT SYSTEM FOR ONBOARD DIAGNOSTICS AND CONTROLS

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance.

Development and Validation of a Model-Based Combustion Controller for Water Injection Management

This manuscript reports the overall development of a Ph.D. research project during the “Mechanics and advanced engineering sciences” course at the Department of Industrial Engineering of the University of Bologna. The project is focused on the development of a combustion control system for an innovative Spark Ignited engine layout. In details, the controller is oriented to manage a prototypal engine equipped with a Port Water Injection system. The water injection technology allows an increment of combustion efficiency due to the knock mitigation effect that permits to keep the combustion phasing closer to the optimal position with respect to the traditional layout. At the beginning of the project, the effects and the possible benefits achievable by water injection have been investigated by a focused experimental campaign. Then the data obtained by combustion analysis have been processed to design a control-oriented combustion model. The model identifies the correlation between Spark Advance, combustion phasing and injected water mass, and two different strategies are presented, both based on an analytic and semi-empirical approach and therefore compatible with a real-time application. The model has been implemented in a combustion controller that manages water injection to reach the best achievable combustion efficiency while keeping knock levels under a pre-established threshold. Three different versions of the algorithm are described in detail. This controller has been designed and pre-calibrated in a software-in-the-loop environment and later an experimental validation has been performed with a rapid control prototyping approach to highlight the performance of the system on real set-up. To further make the strategy implementable on an onboard application, an estimation algorithm of combustion phasing, necessary for the controller, has been developed during the last phase of the PhD Course, based on accelerometric signals.

New prediction methods for CO2 evaporation inside tubes: Part II - An updated general flow boiling heat transfer model based on flow patterns

Corresponding to the updated flow pattern map presented in Part I of this study, an updated general flow pattern based flow boiling heat transfer model was developed for CO2 using the Cheng-Ribatski-Wojtan-Thome [L. Cheng, G. Ribatski, L. Wojtan, J.R. Thome, New flow boiling heat transfer model and flow pattern map for carbon dioxide evaporating inside horizontal tubes, Int. J. Heat Mass Transfer 49 (2006) 4082-4094; L. Cheng, G. Ribatski, L. Wojtan, J.R. Thome, Erratum to: ""New flow boiling heat transfer model and flow pattern map for carbon dioxide evaporating inside tubes"" [Heat Mass Transfer 49 (21-22) (2006) 4082-4094], Int. J. Heat Mass Transfer 50 (2007) 391] flow boiling heat transfer model as the starting basis. The flow boiling heat transfer correlation in the dryout region was updated. In addition, a new mist flow heat transfer correlation for CO2 was developed based on the CO2 data and a heat transfer method for bubbly flow was proposed for completeness sake. The updated general flow boiling heat transfer model for CO2 covers all flow regimes and is applicable to a wider range of conditions for horizontal tubes: tube diameters from 0.6 to 10 mm, mass velocities from 50 to 1500 kg/m(2) s, heat fluxes from 1.8 to 46 kW/m(2) and saturation temperatures from -28 to 25 degrees C (reduced pressures from 0.21 to 0.87). The updated general flow boiling heat transfer model was compared to a new experimental database which contains 1124 data points (790 more than that in the previous model [Cheng et al., 2006, 2007]) in this study. Good agreement between the predicted and experimental data was found in general with 71.4% of the entire database and 83.2% of the database without the dryout and mist flow data predicted within +/-30%. However, the predictions for the dryout and mist flow regions were less satisfactory due to the limited number of data points, the higher inaccuracy in such data, scatter in some data sets ranging up to 40%, significant discrepancies from one experimental study to another and the difficulties associated with predicting the inception and completion of dryout around the perimeter of the horizontal tubes. (C) 2007 Elsevier Ltd. All rights reserved.

Implementation of a Control Loop Experiment in a Network-Based Control System With LonWorks Technology and IP Networks

Considering the increasing popularity of network-based control systems and the huge adoption of IP networks (such as the Internet), this paper studies the influence of network quality of service (QoS) parameters over quality of control parameters. An example of a control loop is implemented using two LonWorks networks (CEA-709.1) interconnected by an emulated IP network, in which important QoS parameters such as delay and delay jitter can be completely controlled. Mathematical definitions are provided according to the literature, and the results of the network-based control loop experiment are presented and discussed.

Robust integration of real time optimization with linear model predictive control

Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.

Real time optimization (RTO) with model predictive control (MPC)

This paper studies a simplified methodology to integrate the real time optimization (RTO) of a continuous system into the model predictive controller in the one layer strategy. The gradient of the economic objective function is included in the cost function of the controller. Optimal conditions of the process at steady state are searched through the use of a rigorous non-linear process model, while the trajectory to be followed is predicted with the use of a linear dynamic model, obtained through a plant step test. The main advantage of the proposed strategy is that the resulting control/optimization problem can still be solved with a quadratic programming routine at each sampling step. Simulation results show that the approach proposed may be comparable to the strategy that solves the full economic optimization problem inside the MPC controller where the resulting control problem becomes a non-linear programming problem with a much higher computer load. (C) 2010 Elsevier Ltd. All rights reserved.

Robust model predictive control with zone control

Model predictive control (MPC) is usually implemented as a control strategy where the system outputs are controlled within specified zones, instead of fixed set points. One strategy to implement the zone control is by means of the selection of different weights for the output error in the control cost function. A disadvantage of this approach is that closed-loop stability cannot be guaranteed, as a different linear controller may be activated at each time step. A way to implement a stable zone control is by means of the use of an infinite horizon cost in which the set point is an additional variable of the control problem. In this case, the set point is restricted to remain inside the output zone and an appropriate output slack variable is included in the optimisation problem to assure the recursive feasibility of the control optimisation problem. Following this approach, a robust MPC is developed for the case of multi-model uncertainty of open-loop stable systems. The controller is devoted to maintain the outputs within their corresponding feasible zone, while reaching the desired optimal input target. Simulation of a process of the oil re. ning industry illustrates the performance of the proposed strategy.