Abstract In this thesis a set of new algorithms is introduced for Iterative Learning Control (ILC) and Repetitive Control (RC). Both areas of study are relatively new in control theory, and the common denominator for them is that they concentrate on controlling systems that include either reference signals or disturbances which are periodic. This provides opportunities for using past information or experience so that the control system learns the control action that results in good performance in terms of reference tracking or disturbance rejection. The first major contribution of the thesis is the algebraic analysis of ILC systems. This analysis shows that in the discrete-time case ILC algorithm design can be considered as designing a multivariable controller for a multivariable static plant and the reference signal that has to be tracked is a multivariable step function. Furthermore, the algebraic analysis reveals that time-varying algorithms should be used instead of time-invariant ones in order to guarantee monotonic convergence of the error in norm. However, from the algebraic analysis it is not clear how to select the free parameters of a given ILC algorithm. Hence in this thesis optimisation methods are used to automate this design phase. Special emphasis is placed on the so called Norm-Optimal Iterative Learning Control (NOILC) that was originally developed in (Amann:1996) as a new result it is shown that a convex modification of the existing predictive algorithm will result in a considerable improvement in convergence speed. Because the NOILC algorithm is computationally quite complex, a new set of Parameter-Optimal ILC algorithms are derived that converge under certain assumptions on the original plant. Three of these new algorithms will result in monotonic convergence to zero tracking error for an arbitrary discrete-time, linear, time-invariant plant. This a very strong property that has been earlier reported for only a small number of ILC algorithms. In the RC case it is shown that an existing RC algorithm that has been widely analysed and used in the research literature is in fact highly unrobust if the algorithm is implemented using sampled-data processing. Consequently, in this thesis a new optimality based discrete-time RC algorithm is derived, which converges to zero tracking error asymptotically for an arbitrary linear, time-invariant discrete-time plant under mild controllability and observability conditions.

Abstract The Generalized Predictive Controller in transfer function representation is proposed for the cascade control task. The recommended cascade GPC (CGPC) applies one predictor and one cost function that results in several advantageous features: The disturbance regulations of the inner and the outer loops can be totally decoupled; The inner disturbance regulation is well damped, the typical overshoot of the traditional cascade control structure is avoided; The robustness properties of the inner and the outer loops can be designed separately; The anti-windup properties of the CGPC are exactly as perfect as in the case of the simple SISO GPC. The typical problem of the saturation in the inner loop, resulting in modeling error for the outer loop, is prevented. The CGPC was applied as the oxygen controller of a pilot fluidized bed boiler. The investigation is based on simulation experiments and on experiments on a pilot scale boiler. In another simulation experiment, the CGPC was applied as the temperature controller of at a steam superheater stage. The results of the experiments well illustrated the power of the proposed cascade control algorithm.

Flight control design for modern fighter aircraft is a challenging task. Aircraft are dynamical systems, which naturally contain a variety of constraints and nonlinearities such as, e.g., maximum permissible load factor, angle of attack and control surface deflections. Taking these limitations into account in the design of control systems is becoming increasingly important as the performance and complexity of the aircraft is constantly increasing. The aeronautical industry has traditionally applied feedforward, anti-windup or similar techniques and different ad hoc engineering solutions to handle constraints on the aircraft. However these approaches often rely on engineering experience and insight rather than a theoretical foundation, and can often require a tremendous amount of time to tune. In this thesis we investigate model predictive control as an alternative design tool to handle the constraints that arises in the flight control design. We derive a simple reference tracking MPC algorithm for linear systems that build on the dual mode formulation with guaranteed stability and low complexity suitable for implementation in real time safety critical systems. To reduce the computational burden of nonlinear model predictive control we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefit of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence. An alternative to deriving MPC algorithms with guaranteed stability properties is to analyze the closed loop stability, post design. Here we focus on deriving a tool based on Mixed Integer Linear Programming for analysis of the closed loop stability and robust stability of linear systems controlled with MPC controllers. To test the performance of model predictive control for a real world example we design and implement a standard MPC controller in the development simulator for the JAS 39 Gripen aircraft at Saab Aeronautics. This part of the thesis focuses on practical and tuning aspects of designing MPC controllers for fighter aircraft. Finally we have compared the MPC design with an alternative approach to maneuver limiting using a command governor.

Networked control systems (NCSs) are known as distributed control systems (DCSs) which are based on traditional feedback control systems but closed via a real-time communication channel. In an NCS, the control and feedback signals are exchanged among the system’s components in the form of information packages through the communication channel. The research of NCSs is important from the application perspective due to the significant advantages over the traditional point-to-point control. However, the insertion of the communication links would also bring challenges and constraints such as the network-induced delays, the missing packets, and the inter symbol interference (ISI) into the system design. In order to tackle these issues and move a step further toward industry applications, two important design problems are investigated in the control areas: Tracking Control (Chapter 2–Chapter 5) and Signal Estimation (Chapter 6–Chapter8). With the fact that more than 90% of control loops in industry are controlled by proportional-integral-derivative (PID) controllers, the first work in this thesis aims to propose the design algorithm on PID controllers for NCSs. Such a design will not require the change or update of the existing industrial hardware, and it will enjoy the advantages of the NCSs. The second motivation is that, due to the network-induced constraints, there is no any existing work on tuning the PID gains for a general NCS with a state-space model. In Chapter 2, the PID tracking control for multi-variable NCSs subject to time-varying delays and packet dropouts is exploited. The H_infty control is employed to attenuate the load disturbance and the measurement noise. In Chapter 3, the probabilistic delay model is used to design the delay-scheduling tracking controllers for NCSs. The tracking control strategy consists of two parts: (1) the feedforward control can enhance the transient response, and (2) the feedback control is the digital PID control. In order to compensate for the delays on both communication links, the predictive control scheme is adopted. To make full use of the delay information, it is better to use the Markov chain to model the network-induced delays and the missing packets. A common assumption on the Markov chain model in the literature is that the probability transition matrix is precisely known. However, the assumption may not hold any more when the delay is time-varying in a large set and the statistics information on the delays is inadequate. In Chapter 4, it is assumed that the transition matrices are with partially unknown elements. An observer-based robust energy-to-peak tracking controller is designed for the NCSs. In Chapter 5, the step tracking control problem for the nonlinear NCSs is in- vestigated. The nonlinear plant is represented by Takagi-Sugeno (T-S) fuzzy linear model. The control strategy is a modified PI control. With an augmentation technique, the tracking controller design problem is converted into an H_infty optimization problem. The controller parameters can be obtained by solving non-iterative linear matrix inequality conditions. The state estimation problem for networked systems is explored in Chapter 6. At the sensor node, the phenomenon of multiple intermittent measurements is considered for a harsh sensing environment. It is assumed that the network-induced delay is time- varying within a bounded interval. To deal with the delayed external input and the non-delayed external input, a weighted H_infty performance is defined. A Lyapunov- based method is employed to deal with the estimator design problem. When the delay is not large, the system with delayed state can be transformed into delay-free systems. By using the probabilistic delay model and the augmentation, the H_infty filter design algorithm is proposed for networked systems in Chapter 7. Considering the phenomenon of ISI, the signals transmitted over the communication link would distort, that is, the output of the communication link is not the same with the input to the communication link. If the phenomenon occurs in the NCSs, it is desired to reconstruct the signal. In Chapter 8, a robust equalizer design algorithm is proposed to reconstruct the input signal, being robust against the measurement noise and the parameter variations. Finally, the conclusions of the dissertation are summarized and future research topics are presented. / Graduate

TESIS PARA OPTAR AL GRADO DE MAGÍSTER EN CONTROL DE GESTIÓN / Existe una tendencia histórica a la baja de la ley de cobre de los yacimientos, mientras que la ley de arsénico aumenta, esto aplica para el caso de nuevos proyectos como Chuquicamata Subterráneo y Ministro Hales. Esta misma tendencia, se observa en otros nuevos yacimientos de cobre en Perú, Canadá, Europa y Asia. Uno de los principales retos de la actividad minera hoy, es extraer y procesar los minerales minimizando los impactos en el medioambiente. Por ello, el tratamiento y disposición final de los residuos es uno de los desafíos más relevantes, asociado a mayores exigencias ambientales en Chile y el mundo. Un adecuado manejo de los residuos no solo tiene que ver con minimizar sus impactos ambientales, sino que también con aprovechar de recuperar una gran cantidad de subproductos que hoy se botan en residuos como relaves o escorias. Lo interesante es que, en la medida que se desarrollan procesos más limpios, se crean las condiciones para recuperar económicamente los subproductos. Es decir, la protección del medioambiente finalmente se asocia con el desarrollo y aplicación de tecnologías que hacen viable la recuperación de valor, es decir mejora la sustentabilidad, tanto ambiental como económicamente. En este contexto EcoMetales Limited, que es una empresa filial de CODELCO, busca aprovechar estos cambios que hoy en día existen relacionados a la minería entregando soluciones ambientales. Está empresa es la unidad estratégica de negocios (UEN) en donde se propondrá un sistema de control de gestión. Su proceso productivo se encuentra en la Segunda Región de Antofagasta, en Chuquicamata, que es denominado el Distrito Norte. El propósito del sistema de control de gestión propuesto en este proyecto, busca poder alinear los objetivos entre la organización y las personas que ejecutan la estrategia a través de los distintos procesos. La metodología utilizada, será en base al modelo de Robert S. Kaplan y David P. Norton, desarrollando principalmente tres conceptos que son: la formulación estratégica, el desarrollo de la estrategia y el alineamiento organizacional. En primer lugar, se realiza un análisis estratégico para conocer los factores externos (PESTEL y 5 Fuerzas de Porter) e internos (FODA), de acuerdo a esto se define la propuesta de valor de la UEN: “Somos una empresa de abatimiento de impurezas y recuperación de metales que entregamos a nuestros clientes soluciones ambientales integradas para sus residuos mineros, permitiendo entregar continuidad operacional a procesos cumpliendo estándares de seguridad, salud ocupacional y medioambiente, otorgando rentabilidad a su negocio”, esto de acuerdo a los atributos identificados para la empresa: Continuidad Operacional, Solución Ambiental Integrada y Cumplimiento de Estándares de Seguridad, Salud Ocupacional y Medioambiente. Se elaboró un mapa estratégico de la UEN, aplicado a las diferentes áreas de la empresa. Se incluyeron los recursos principales, procesos internos para cumplir con la propuesta de valor y los resultados financieros, para cumplir con los accionistas de la compañía “Aumentar Rentabilidad del Negocio”. Se destacan dos ejes del mapa estratégico: Reputación y Nuevos Negocios. Esto da paso a la construcción del Cuadro de Mando Integral (CMI) para el monitoreo de la estratégica y planteando iniciativas estratégicas que apalancan los objetivos. Respecto del desdoblamiento estratégico, se seleccionan las 2 Gerencias: Gerencia de Sustentabilidad y Gerencia de Recursos Humanos, donde se proponen tableros de gestión y control en un nivel de mayor detalle. Finalmente se propone un sistema de alineamiento organizacional mediante esquema de incentivos a las gerencias antes mencionadas, buscando relacionar de buena manera la recompensa con el desempeño y esfuerzo.

TESIS PARA OPTAR AL GRADO DE MAGÍSTER EN CONTROL DE GESTIÓN / El presente proyecto de grado tiene como objetivo aplicar un modelo de Sistema de Control de Gestión a la Tesorería General de la República. Si bien en la actualidad existe un Sistema de Planificación y Control de Gestión que articula el quehacer del Servicio de Tesorerías, sustentado por la Planificación Estratégica Institucional 2015-2018, el objetivo es establecer un sistema complementario que potencie al sistema actual institucional, que permita aumentar la satisfacción ciudadana, incrementar los resultados y optimizar el uso de los recursos asignados en la Ley de Presupuesto del Sector Público.

Variable Horizon Model Predictive Control (VH-MPC) is a form of predictive control that includes the horizon length as a decision variable in the constrained optimisation problem solved at each iteration. It has been recently applied to completion problems, where the system state is to be steered to a closed set in finite time. The behaviour of the system once completion has occurred is not considered part of the control problem. This thesis is concerned with three aspects of robustness and optimality in VH-MPC completion problems. In particular, the thesis investigates robustness to well defined but unpredictable changes in system and controller parameters, robustness to bounded disturbances in the presence of certain input parameterisations to reduce computational complexity, and optimal robustness to bounded disturbances using tightened constraints. In the context of linear time invariant systems, new theoretical contributions and algorithms are developed. Firstly, changing dynamics, constraints and control objectives are addressed by introducing the notion of feasible contingencies. A novel algorithm is proposed that introduces extra prediction variables to ensure that anticipated new control objectives are always feasible, under changed system parameters. In addition, a modified constraint tightening formulation is introduced to provide robust completion in the presence of bounded disturbances. Different contingency scenarios are presented and numerical simulations demonstrate the formulation’s efficacy. Next, complexity reduction is considered, using a form of input parameterisation known as move blocking. After introducing a new notation for move blocking, algorithms are presented for designing a move-blocked VH-MPC controller. Constraints are tightened in a novel way for robustness, whilst ensuring that guarantees of recursive feasibility and finite-time completion are preserved. Simulations are used to illustrate the effect of an example blocking scheme on computation time, closed-loop cost, control inputs and state trajectories. Attention is now turned towards mitigating the effect of constraint tightening policies on a VH-MPC controller’s region of attraction. An optimisation problem is formulated to maximise the volume of an inner approximation to the region of attraction, parameterised in terms of the tightening policy. Alternative heuristic approaches are also proposed to deal with high state dimensions. Numerical examples show that the new technique produces substantially improved regions of attraction in comparison to other proposed approaches, and greatly reduces the maximum required prediction horizon length for a given application. Finally, a case study is presented to illustrate the application of the new theory developed in this thesis to a non-trivial example system. A simplified nonlinear surface excavation machine and material model is developed for this purpose. The model is stabilised with an inner-loop controller, following which a VH-MPC controller for autonomous trajectory generation is designed using a discretised, linearised model of the stabilised system. Realistic simulated trajectories are obtained from applying the controller to the stabilised system and incorporating the ideas developed in this thesis. These ideas improve the applicability and computational tractability of VH-MPC, for both traditional applications as well as those that go beyond the realm of vehicle manœuvring.

Please read the Resume in the section 06resume of this document / Dissertation (MSc (Agric))--University of Pretoria, 2006. / Microbiology and Plant Pathology / unrestricted

Stochastic model predictive control (MPC) is a fascinating field for research and of increasing practical importance since optimal control techniques have been intensively investigated in modern control system design. With the development of computer technologies and communication networks, networked control systems (NCSs) or cyber-physical systems (CPSs) have become an interest of research due to the comprehensive integration of physical systems, such as sensors, actuators and plants, with intricate cyber components, possessing information communication and computation. In CPSs, advantages of low installation cost, high reliability, flexible modularity, improved efficiency, and greater autonomy can be obtained by the tight coordination of physical and cyber components. Several sectors, including robotics, transportation, health care, smart buildings, and smart grid, have witnessed the successful application of CPSs design. The integration of extensive cyber capability and physical plants with ubiquitous uncertainties also introduces concerns over communication efficiency, robustness and stability of the CPSs. Thus, to achieve satisfactory performance metrics of efficiency, robustness and stability, a detailed investigation into control synthesis of CPSs under the stochastic model predictive control framework is of importance. The stochastic model predictive control synthesis plays a vital role in CPSs design since the multivariable stochastic system subject to probabilistic constraints can be controlled in an optimized way. On the other hand, aperiodically sampled, or event-based, model predictive control has also been applied to CPSs extensively to improve communication efficiency. In this thesis, the control synthesis and analysis of aperiodically sampled stochastic model predictive control for CPSs is considered. Chapter 1 provides an introductory literature review of the current development of stochastic MPC, distributed stochastic MPC and event-based MPC. Chapter 2 presents a stochastic self-triggered model predictive control scheme for linear systems with additive uncertainty and with the states and inputs being subject to chance constraints. In the proposed control scheme, the succeeding sampling time instant and current control inputs are computed online by solving a formulated optimization problem. Chapter 3 discusses a stochastic self-triggered model predictive control algorithm with an adaptive prediction horizon. The communication cost is explicitly considered by adding a damping factor in the cost function. Sufficient conditions are provided to guarantee closed-loop chance constraints satisfactions. Furthermore, the recursive feasibility of the algorithm is analyzed, and the closed-loop system is shown to be stable. Chapter 4 proposes a distributed self-triggered stochastic MPC control scheme for CPSs under coupled chance constraints and additive disturbances. Based on the assumptions on stochastic disturbances, both local and coupled probabilistic constraints are transformed into the deterministic form using the tube-based method, and improved terminal constraints are constructed to guarantee the recursive feasibility of the control scheme. Theoretical analysis has shown that the overall closed-loop CPSs are quadratically stable. Numerical examples illustrate the efficacy of the proposed control method in terms of data transmission reductions. Chapter 5 concludes the thesis and suggests some promising directions for future research. / Graduate / 2022-01-15

The agricultural industry is facing a major technological change with autonomousvehicles in focus. This follows the global trend, where the interest lies in increas-ing production, while reducing costs with the help of automation. Consideringthe vast amount of different agricultural machines on the market today, the pro-cess of automating these machines is long and needs to start on one machine.This thesis covers the process of developing an automatic control system for aseedbed tine harrow. The seedbed tine harrow cultivates the soil at a certain depth in preparationfor planting. The different functions on the harrow are today manually controlledfrom the cab of the tractor, which means that the farmer must constantly moni-tor the process. The proposed control system uses radar sensors to measure andhydraulic systems to control the harrowing depth and the crossboards. The de-velopment of the control system consists of modeling the harrow, creating a sim-ulation environment, choosing a filtering strategy, and testing different controlalgorithms. The resulting control algorithm, implemented and tested on the harrow, con-sisted of a Kalman filter with separate PD-controllers for each function, the har-rowing depth, and the angle of the crossboards. The crossboard controllers usean additional feedforward control from measured disturbance. The thesis alsoexplores a set of experimental control algorithms, for instance, cascade control.These are not possible to implement on this generation of the harrow but showpromising potential from simulation.