Flexible adaptation of iterative learning control with applications to synthetic bone graft manufacturing

Additive manufacturing processes are powerful tools; they are capable of fabricating structures without expensive structure specific tooling -- therefore structure designs can efficiently change from run-to-run -- and they can integrate multiple distinct materials into a single structure. This work investigates one such additive manufacturing process, micro-Robotic Deposition ($\mu$RD), and its utility in fabricating advanced architecture synthetic bone grafts. These bone grafts, also known as synthetic bone scaffolds, are highly porous three-dimensional structures that provide a matrix to support the natural process of bone remodeling. Ideally, the synthetic scaffold will stimulate complete bone healing in a skeletal defect site and also resorb with time so that only natural tissue remains. The objective of this research is to develop methods to integrate different regions with different porous microstructures into a single scaffold; there is evidence that scaffolds with designed regions of specific microstructures can be used to elicit a strong and directed bone ingrowth response that improves bone ingrowth rate and quality. The key contribution of this work is the development of a control algorithm that precisely places different build materials in specified locations, thereby the fabrication of advanced architecture scaffolds is feasible. Under previous control methods, designs were relegated to be composed of a single material. The control algorithm developed in this work is an adaptation of Iterative Learning Control (ILC), a control method that is typically best suited for mass manufacturing applications. This adaptation reorients the ILC framework such that it is more amenable to additive manufacturing systems, such as $\mu$RD. Control efficacy is demonstrated by the fabrication of advanced architecture scaffolds. Scaffolds with contoured forms, multiple domains with distinct porous microstructures, and hollow cavities are feasible when the developed controller is used in conjunction with a novel manufacturing workflow in which scaffolds are filled within patterned molds that support overhanging features. An additional application demonstrates controller performance on the robot positioning problem; this work has implications for additive manufacturing in general

SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL FOR INTERACTING SERIES PROCESS WITH NONLINEAR DYNAMICS

This thesis discusses the empirical modeling using system identification technique and the implementation of a linear model predictive control with focus on interacting series processes. In general, a structure involving a series of systems occurs often in process plants that include processing sequences such as feed heat exchanger, chemical reactor, product cooling, and product separation. The study is carried out by experimental works using the gaseous pilot plant as the process. The gaseous pilot plant exhibits the typical dynamic of an interacting series process, where the strong interaction between upstream and downstream properties occurs in both ways. The subspace system identification method is used to estimate the linear model parameters. The developed model is designed to be robust against plant nonlinearities. The plant dynamics is first derived from mass and momentum balances of an ideal gas. To provide good estimations, two kinds of input signals are considered, and three methods are taken into account to determine the model order. Two model structures are examined. The model validation is conducted in open-loop and in closed-loop control system. Real-time implementation of a linear model predictive control is also studied. Rapid prototyping of such controller is developed using the available equipments and software tools. The study includes the tuning of the controller in a heuristic way and the strategy to combine two kinds of control algorithm in the control system. A simple set of guidelines for tuning the model predictive controller is proposed. Several important issues in the identification process and real-time implementation of model predictive control algorithm are also discussed. The proposed method has been successfully demonstrated on a pilot plant and a number of key results obtained in the development process are presented

Study and Development of Mechatronic Devices and Machine Learning Schemes for Industrial Applications

Obiettivo del presente progetto di dottorato è lo studio e sviluppo di sistemi meccatronici e di modelli machine learning per macchine operatrici e celle robotizzate al fine di incrementarne le prestazioni operative e gestionali. Le pressanti esigenze del mercato hanno imposto lavorazioni con livelli di accuratezza sempre più elevati, tempi di risposta e di produzione ridotti e a costi contenuti. In questo contesto nasce il progetto di dottorato, focalizzato su applicazioni di lavorazioni meccaniche (e.g. fresatura), che includono sistemi complessi quali, ad esempio, macchine a 5 assi e, tipicamente, robot industriali, il cui utilizzo varia a seconda dell’impiego. Oltre alle specifiche problematiche delle lavorazioni, si deve anche considerare l’interazione macchina-robot per permettere un’efficiente capacità e gestione dell’intero impianto. La complessità di questo scenario può evidenziare sia specifiche problematiche inerenti alle lavorazioni (e.g. vibrazioni) sia inefficienze più generali che riguardano l’impianto produttivo (e.g. asservimento delle macchine con robot, consumo energetico). Vista la vastità della tematica, il progetto si è suddiviso in due parti, lo studio e sviluppo di due specifici dispositivi meccatronici, basati sull’impiego di attuatori piezoelettrici, che puntano principalmente alla compensazione di vibrazioni indotte dal processo di lavorazione, e l’integrazione di robot per l’asservimento di macchine utensili in celle robotizzate, impiegando modelli di machine learning per definire le traiettorie ed i punti di raggiungibilità del robot, al fine di migliorarne l’accuratezza del posizionamento del pezzo in diverse condizioni. In conclusione, la presente tesi vuole proporre soluzioni meccatroniche e di machine learning per incrementare le prestazioni di macchine e sistemi robotizzati convenzionali. I sistemi studiati possono essere integrati in celle robotizzate, focalizzandosi sia su problematiche specifiche delle lavorazioni in macchine operatrici sia su problematiche a livello di impianto robot-macchina. Le ricerche hanno riguardato un’approfondita valutazione dello stato dell’arte, la definizione dei modelli teorici, la progettazione funzionale e l’identificazione delle criticità del design dei prototipi, la realizzazione delle simulazioni e delle prove sperimentali e l’analisi dei risultati.The aim of this Ph.D. project is the study and development of mechatronic systems and machine learning models for machine tools and robotic applications to improve their performances. The industrial demands have imposed an ever-increasing accuracy and efficiency requirement whilst constraining the cost. In this context, this project focuses on machining processes (e.g. milling) that include complex systems such as 5-axes machine tool and industrial robots, employed for various applications. Beside the issues related to the machining process itself, the interaction between the machining centre and the robot must be considered for the complete industrial plant’s improvement. This scenario´s complexity depicts both specific machining problematics (e.g. vibrations) and more general issues related to the complete plant, such as machine tending with an industrial robot and energy consumption. Regarding the immensity of this area, this project is divided in two parts, the study and development of two mechatronic devices, based on piezoelectric stack actuators, for the active vibration control during the machining process, and the robot machine tending within the robotic cell, employing machine learning schemes for the trajectory definition and robot reachability to improve the corresponding positioning accuracy. In conclusion, this thesis aims to provide a set of solutions, based on mechatronic devices and machine learning schemes, to improve the conventional machining centre and the robotic systems performances. The studied systems can be integrated within a robotic cell, focusing on issues related to the specific machining process and to the interaction between robot-machining centre. This research required a thorough study of the state-of-the-art, the formulation of theoretical models, the functional design development, the identification of the critical aspects in the prototype designs, the simulation and experimental campaigns, and the analysis of the obtained results

The Whirling Blade and the Steaming Cauldron

Ths dissertation applies recent theoretical developments in control to two practical examples. The first example is control of the primary circuit of a pressurized water nuclear reactor. This is an interesting example because the plant is complex and its dynamics vary greatly over the operating range of interest. The second example is a thrust-vectored ducted fan engine, a nonlinear flight control experiment at Caltech. The main part of this dissertation is the application of linear parameter-dependent control techniques to the examples. The synthesis technique is based on the solution of linear matrix inequalities (LMIs) and produces a controller whch acheves specified performance against the worst-case time variation of measurable parameters entering the plant in a linear fractional manner. Thus the plant can have widely varying dynamics over the operating range, a quality possessed by both examples. The controllers designed with these methods perform extremely well and are compared to H∞, gain-scheduled, and nonlinear controllers. Additionally, an in-depth examination of the model of the ducted fan is performed, including system identification. From this work, we proceed to apply various techniques to examine what they can tell us in the context of a practical example. The primary technique is LMI-based model validation. The contribution ths dissertation makes is to show that parameter-dependent control techniques can be applied with great effectiveness to practical applications. Moreover, the trade-off between modelling and controller performance is examined in some detail. Finally, we demonstrate the applicability of recent model validation techruques in practice, and discuss stabilizability issues

Fault tolerant longitudinal aircraft control using non-linear integral sliding mode

Copyright © 2014 Institution of Engineering and Technology (IET)This study proposes a novel non-linear fault tolerant scheme for longitudinal control of an aircraft system, comprising an integral sliding mode control allocation scheme and a backstepping structure. In fault free conditions, the closed loop system is governed by the backstepping controller and the integral sliding mode control allocation scheme only influences the performance if faults/failures occur in the primary control surfaces. In this situation, the allocation scheme redistributes the control signals to the secondary control surfaces and the scheme is able to tolerate total failures in the primary actuator. A backstepping scheme taken from the existing literature is designed for flight path angle tracking (based on the non-linear equations of motion) and this is used as the underlying baseline controller in nominal conditions. The efficacy of the scheme is demonstrated using a high-fidelity aircraft benchmark model. Excellent results are obtained in the presence of plant/model uncertainty in both fault free and faulty conditions

Multivariable control of a nonlinear process : output prioritisation by error redistribution

Bibliography: leaves 149-154

High Performance Control of a Transmission Based Servo Actuator System

High performance actuation is a key factor in the industrial robot area. The transmission based servo actuator system (TBA) is a new type of robot actuator with a brushless DC servo motor and a three speed discrete variable transmission (DVT). The proposed TBA design can match the performance of a typical hydraulic actuator with compact size and weight. The TBA is a typical hybrid dynamic system consisting of three continuous dynamic systems and a discrete state controller. This dissertation addresses the fundamental problems associated with the TBA system control from a hybrid system point of view. A detailed dynamic model of the TBA is developed. Due to the complexity of the TBA system, an exact model is unwieldy for control design and analysis purposes. In this research, the TBA system is simplified into a hybrid system with three second order linear time invariant systems, on which all the controls are developed.Dynamic stability of the TBA is critical for its function as a servoactuator. For a hybrid system, the stability problem has much broader range of issues than a purely continuous system. In general, the plant stability and the subsystem stability are independent. For example, a hybrid system with stable subsystems can be unstable for certain switch sequences; on the other hand, a hybrid system with unstable subsystems can be stabilized by proper switch signals. In this dissertation, a sufficient condition is established for stability of the TBA system. It is proven that the hybrid system is stable under asynchronous switching if there exists a common Lyapunov function for all subsystems. It is proven that the TBA subsystems can have a common Lypunov function by designing appropriate feedback controller. The feedback controller to stabilize the TBA can be transformed into a PID equivalent controller because the subsystems are second order linear time invariant systems (LTI). The PID controller was then implemented and high performance in terms of position error and transient suppression has been achieved. The discrete state controller should be stable, which means that its output should be consistent if the hybrid system is subjected to disturbances. A common phenomenon is that the state changes back and forth very frequently near the switch boundary, which is referred to as transition instability. This research proposes a switch strategy consisting of two boundaries to achieve the transition stability, and it is proved that the proposed switch strategy is transition stable. An optimal controller is designed and difficulties associated with implementation are generated. Based on the proposed control methods, a multithread real time control software has been developed to achieve a deterministic control loop sampling. The control software is developed in C/C++ under Real Time Application Interface (RTAI), which provides a real time programming environment in a normal Linux operating system. With the proposed controller and a prototype TBA test system, TBA stability and control performance was demonstrated and evaluated. The following results were observed: Steady state error of 0.005 degrees at the emulated robust manipulator shoulder pitch joint Control loop sampling period of 1 millisecond with negligible delay Transient disturbances associated with the gear shifting of ~20% in most cases. The methods and applications used in this dissertation can be extended to a large range of hybrid dynamic systems in terms of control system design, analysis and implementation. This research contributes to the literature and research knowledge base in the following ways: Exploration and solution of the control problems of TBA’s in the hybrid system control context. Expansion of the fundamental understanding of the practical control issues of TBA’s. Analysis, design, and implementation of a real time TBA control system, and identification of the most suitable control strategy for the TBA. The development of analysis and control methods that can be extended to a much broader range of hybrid dynamic systems