Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (p. 126-129).

En esta Tesis se tratan varios aspectos relacionados con la linealización de sistemas no lineales. Las dificultades inherentes al empleo de técnicas de análisis, modelado y control de sistemas no lineales, son debidas en gran parte a lo poco sistemáticas que son. Además, respecto a los métodos de linealización basados en técnicas de control cabe decir que los que usan realimentación de estado, en general, son difíciles de realizar en la práctica ya que el diseño de observadores de estado no lineales es problemático. Asimismo, también lo es el diseño y realización de controladores no lineales. El método de linealización propuesto en esta Tesis consiste en determinar el tipo de realimentación a aplicar a un sistema no lineal, de modo que, según un cierto criterio de medida, el efecto de las no linealidades se reduzca frente al de las linealidades. El sistema no lineal se descompondrá en dos bloques, uno lineal y otro no lineal. El bloque lineal será el que caracterizará el funcionamiento deseado, es decir el funcionamiento linealizado, y es por esto que se considera un sistema modelo. Este sistema modelo se le denominará modelo de referencia, y será la guía de cual es el funcionamiento deseado. Una de las alternativas para descomponer el sistema no lineal en los dos bloques comentados es haciendo uso del desarrollo en serie de Volterra del mismo, de modo que el primer término de la expansión, término lineal, se corresponderá con el modelo de referencia a seguir.Haciendo uso del modelo matemático del sistema no lineal y del modelo de referencia se obtendrá la caracterización de un sistema error, que modela las diferencias de funcionamiento entre el sistema no lineal y el lineal deseado. De este modo, el objetivo consiste en conseguir que la salida del sistema error sea nula, o en su defecto que sea lo menor posible. Esta reducción del sistema error se plantea como un problema de atenuación de perturbaciones vía realimentación, de acuerdo a un criterio óptimo medido con la norma H-infinita: se buscará la minimización de la norma H-infinita de la parte lineal del sistema error, pero vigilando al mismo tiempo la estabilidad del sistema global en lazo cerrado. Para ello el criterio de estabilidad empleado es el de la pequeña ganancia. / In this Thesis some aspects related to the linearization of nonlinear systems are considered. When working with nonlinear systems it is difficult the analysis, modelling and control of such kind of systems. Moreover the linearization methods based on control theory using state feedback are difficult to use, in a practical point of view, due to the difficult to design nonlinear state observers.The linearization method proposed in this Thesis consist in determining what kind of linear feedback must be applied to a nonlinear system in order to reduce the error, according to a certain norm, between nonlinear and linear terms. The nonlinear systems will be decomposed in two blocks, one linear and the other nonlinear. The linear part corresponds to the desired behaviour: it can be considered as a model. This model system is called reference model. One option, proposed in this work, for doing this decomposition is by using a Volterra series decomposition, being the first Volterra kernel the desired linear part.By using the mathematical model of the nonlinear system and the reference model it can be obtained an error model, able for describing the error between nonlinear and model (linear) systems. So, the goal to achieve is the reduction of this error. It consists in a problem of optimization (minimization) of the H-infinity norm by using output feedback. Moreover, the overall stability of the closed loop system must be accomplished and tested by means of the small gain stability theory.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

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.

This work is focused on robust control theory especially on methods H and analysis (structured singular value). The first part of the thesis contains theoreticle background to uncertainty modeling, to robust controller designs and to permanent magnet synchronous machine modeling. The second part presents concrette robust controller design which is tested in simulations and validated on a real motor. The influence of parameter changes on stability of closed loop system is discussed and description of obtained results is given in conclusions.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 152-158).

This thesis tries to research the term “robust controllers”. Its aim is to compare the robustness of discrete PID controllers (Discrete Equivalent Continuous Controller, Discrete Impulse Area Invariant, Takahashi, Feed-Forward), adaptive discrete PID controllers (Discrete Impulse Area Invariant, Takahashi, Feed-Forward), optimal controllers (quadratic optimal), and adaptive optimal controllers (quadratic optimal) on chosen transfer functions. Its aim is also to check the influence of A/D and D/A converters. The aims to obtain are demarked at the beginning of the text and also there is an explanation of the term “robustness.” Later on there is a description and an approximation to each of the chosen kinds of controllers and the identification methods used in the thesis (for adaptive controllers the method of recursive least-squares was used). The Kharitonov's Theorem are made on the chosen transfer function. Next there is a description of the methods with which the robustness of the controllers will be tested. The first method is the integral criteria, particular ITAE criterion and quadratic criterion. The second one is the analysis of the generalised circle criterion. Furthermore there are various displays of the results obtained and their corresponding comments. The results obtained are graphically displayed and by means of these schemes the particular types of controllers are compared. All of the simulations and results obtained were acquired through the use of the program MATLAB- Simulink. In the end of the thesis there is an overall evaluation.

The presence of uncertainty in model predictive control (MPC) has been accounted for using two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC and SMPC formulations consider closed-loop optimal control problems whose exact solution, via dynamic programming, is intractable for most systems. Much effort then has been devoted to find good compromises between the degree of optimality and computational tractability. This thesis expands on this effort and presents robust and stochastic MPC strategies with reduced online computational requirements where the conservativeness incurred is made as small as conveniently possible. Two RMPC strategies are proposed for linear systems under additive uncertainty. They are based on a recently proposed approach which uses a triangular prediction structure and a non-linear control policy. One strategy considers a transference of part of the computation of the control policy to an offline stage. The other strategy considers a modification of the prediction structure so that it has a striped structure and the disturbance compensation extends throughout an infinite horizon. An RMPC strategy for linear systems with additive and multiplicative uncertainty is also presented. It considers polytopic dynamics that are designed so as to maximize the volume of an invariant ellipsoid, and are used in a dual-mode prediction scheme where constraint satisfaction is ensured by an approach based on a variation of Farkas' Lemma. Finally, two SMPC strategies for linear systems with additive uncertainty are presented, which use an affine-in-the-disturbances control policy with a striped structure. One strategy considers an offline sequential design of the gains of the control policy, while these are variables in the online optimization in the other. Control theoretic properties, such as recursive feasibility and stability, are studied for all the proposed strategies. Numerical comparisons show that the proposed algorithms can provide a convenient compromise in terms of computational demands and control authority.

A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.

Negative-imaginary systems are broadly speaking stable and square (equal number of inputs and outputs) systems whose Nyquist plot lies underneath (never touches for strictly negative-imaginary systems) the real axis when the frequency varies in the open interval 0 to ∞. This class of systems appear quite often in engineering applications, for example, in lightly damped flexible structures with collocated position sensors and force actuators, multi-link robots, DC machines, active filters, etc. In this thesis, robustness analysis and controller synthesis methods for uncertain negative-imaginary systems are explored. Two new reformulation techniques are proposed that facilitate both the robustness analysis and controller synthesis for uncertain negative-imaginary systems. These reformulations are based on the transformation from negative-imaginary systems to a bounded-real framework via the positive-real property. In the presence of strictly negative-imaginary uncertainty, the robust stabilization problem is posed in an equivalent H∞ control framework; similarly, a negative-imaginary robust performance analysis problem is cast into an equivalent μ-framework. The latter framework also allows robust stability analysis when the perturbations are a mixture of bounded-real and negative-imaginary uncertainties. The proposed two techniques pave the way for existing H∞ control and μ theory to be applied to robustness analysis and controller synthesis for negative-imaginary systems. In addition, a static state-feedback synthesis method is proposed to achieve robust stability of a system in the presence of strictly negative-imaginary uncertainties. The method is developed in the LMI framework, which can be solved efficiently using convex optimization techniques. The controller synthesis method is based on the negative-imaginary stability theorem: a positive feedback interconnection of two negative-imaginary systems is internally stable if and only if the DC loop gain is contractive and at least one of the systems in the interconnected loop is strictly negative-imaginary. Also, in order to handle non-strict negative-imaginary uncertainties, a strongly strictly negative-imaginary lemma is proposed that helps to ensure the strictly negative-imaginary property of the nominal closed-loop system for robustness. To this end, a state-space characterization for strictly negative-imaginary property is given for non-minimal systems where the conditions are convex and hence numerically attractive. The results in this thesis hence facilitate both the robustness analysis and controller synthesis for negative-imaginary systems that quite often arise in practical scenarios. In addition, they can be applied to quantify the worse-case performance for this class of systems. Therefore, the proposed results have important implications in controller synthesis for uncertain negative-imaginary systems that achieve not only robust stabilization but also robust performance.