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.

Orientador: Ricardo Coração de Leão Fontoura de Oliveira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-26T15:54:55Z (GMT). No. of bitstreams: 1 Vieira_HenriquedeSouza_M.pdf: 1027517 bytes, checksum: 9dcae7f42b15c7b6a4659c57bea547f7 (MD5) Previous issue date: 2015 / Resumo: Os problemas de estabilização e controle H? robustos por realimentação de estados para sistemas lineares incertos em domínios politópicos são investigados nesta dissertação. São propostas novas técnicas de síntese baseadas em condições LMIs com busca em escalares, abordando de maneira unificada sistemas contínuos e discretos no tempo. A principal novidade da técnica proposta é o uso efetivo de matrizes de Lyapunov polinomiais de grau arbitrário para certificar a estabilidade robusta do sistema em malha fechada. A segunda vantagem da abordagem proposta é que as melhores condições de síntese por realimentação de estados atualmente disponíveis na literatura podem ser reproduzidas por meio de escolhas particulares dos parâmetros escalares. Para o problema de controle H? também é proposto um procedimento iterativo como alternativa à busca dos escalares. Experimentos numéricos ilustram o potencial e a eficácia da técnica proposta / Abstract: The problems of robust stabilization and H? control by state-feedback for uncertain linear systems in polytopic domains are investigated in this dissertation. New synthesis techniques based on LMI conditions with scalar searches, addressing in a unified way continuous and discrete-time systems, are proposed. The main novelty of the proposed method is the effective use of polynomial Lyapunov matrices of arbitrary degree to certify the robust stability of the closed-loop system. The second advantage of the proposed approach is that the best currently available state-feedback synthesis conditions in the literature can be reproduced by particular choices of the scalars. Regarding the H? control problem, an iterative procedure is also proposed as an alternative to the scalar searches. Numerical experiments illustrate the potential and efficacy of the proposed methods / Mestrado / Automação / Mestre em Engenharia Elétrica

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.

A mathematical model of a small fixed-wing aircraft was developed through application of parameter estimation techniques to simulated flight test data. Multiple controllers were devised based on this model for path following, including a self-scheduled linear parameter-varying (LPV) controller with path curvature as a scheduling parameter. The robustness and performance of these controllers were tested in a rigorous MATLAB simulation environment that included steady winds and gusts, measurement noise, delays, and model uncertainties. The linear controllers designed within were found to be robust to the disturbances and uncertainties in the simulation environment, and had similar or better performance in comparison to a nonlinear control law operating in an inner-outer loop structure. Steps are being taken to implement the resulting controllers on the unmanned aerial vehicle (UAV) testbed in the Nonlinear Systems Laboratory at Virginia Tech. / Master of Science

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Flutter is an aeroelastic instability phenomenon that can result either in serious damage or complete destruction of a gas turbine blade structure due to high cycle fatigue. Although 90% of potential high cycle fatigue occurrences are uncovered during engine development, the remaining 10% stand for one third of the total engine development costs. Field experience has shown that during the last decades as much as 46% of fighter aircrafts were not mission-capable in certain periods due to high cycle fatigue related mishaps. To assure a reliable and safe operation, potential for blade flutter must be eliminated from the turbomachinery stages. However, even the most computationally intensive higher order models of today are not able to predict flutter accurately. Moreover, there are uncertainties in the operational environment, and gas turbine parts degrade over time due to fouling, erosion and corrosion resulting in parametric uncertainties. Therefore, it is essential to design engines that are robust with respect to the possible uncertainties. In this thesis, the robustness of an axial compressor blade design is studied with respect to parametric uncertainties through the Mu analysis. The nominal flutter model is adopted from [9]. This model was derived by matching a two dimensional incompressible flow field across the flexible rotor and the rigid stator. The aerodynamic load on the blade is derived via the control volume analysis. For use in the Mu analysis, first the model originally described by a set of partial differential equations is reduced to ordinary differential equations by the Fourier series based collocation method. After that, the nominal model is obtained by linearizing the achieved non-linear ordinary differential equations. The uncertainties coming from the modeling assumptions and imperfectly known parameters and coefficients are all modeled as parametric uncertainties through the Monte Carlo simulation. As iv compared with other robustness analysis tools, such as Hinf, the Mu analysis is less conservative and can handle both structured and unstructured perturbations. Finally, Genetic Algorithm is used as an optimization tool to find ideal parameters that will ensure best performance in terms of damping out flutter. Simulation results show that the procedure described in this thesis can be effective in studying the flutter stability margin and can be used to guide the gas turbine blade design.

This dissertation addresses the problem of motion planning and control for nonholonomic mobile robots, particularly wheeled and tracked mobile robots, working in extreme environments, for example, desert, forest, and mine. In such environments, the mobile robots are highly subject to external disturbances (e.g., slippery terrain, dusty air, etc.), which essentially introduce uncertainties to the robot systems. The complexity of the motion planning problem is due to taking both nonholonomic and uncertainty constraints into account simultaneously. As a result, none of the conventional nonholonomic motion planning can be directly applied. The control problem is even more challenging since state constraints posed by obstacles in the environments must also be considered along with the nonholonomic and uncertainty constraints. In this research, we systematically develop a new type of motion planning technique that determines an optimal path for a mobile robot in a given environment. This motion planning technique is based on the idea of a maximum allowable uncertainty, which is a number assigned to each free configuration in the environment. The optimal path is a path connecting given initial and goal configurations through a series of configurations respecting the nonholonomic constraint and possessing the highest maximum allowable uncertainty. Both linear and quadratic approximations of the maximum allowable uncertainty, including their corresponding motion planners, have been studied. Additionally, we develop the first real-time robust control algorithm for the mobile robot under uncertainty to follow given paths safely and accurately in cluttered environments. The control algorithm also utilizes the concept of the maximum allowable uncertainty as well as the robust control theory. The simulation results have shown the effectiveness and robustness of the control algorithm in steering the mobile robot along a given path amidst obstacles without collisions even when the level of robot uncertainty is high. / Ph. D.

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A method utilizing H2 control concepts and the numerical method of Polynomial Chaos was developed in order to create a novel robust probabilistically optimal control approach. This method was created for the practical reason that uncertainty in parameters tends to be inherent in system models. As such, the development of new methods utilizing probability density functions (PDFs) was desired. From a more theoretical viewpoint, the utilization of Polynomial Chaos for studying and designing control systems has not been very thoroughly investigated. The current work looks at expanding the H2 and related Linear Quadratic Regulator (LQR) control problems for systems with parametric uncertainty. This allows solving deterministic linear equations that represent probabilistic linear differential equations. The application of common LTI (Linear Time Invariant) tools to these expanded systems are theoretically justified and investigated. Examples demonstrating the utilized optimization process for minimizing the H2 norm and parallels to LQR design are presented. The dissertation begins with a thorough background section that reviews necessary probability theory. Also, the connection between Polynomial Chaos and dynamic systems is explained. Next, an overview of related control methods, as well as an in-depth review of current Polynomial Chaos literature is given. Following, formal analysis, related to the use of Polynomial Chaos, is provided. This lays the ground for the general method of control design using Polynomial Chaos and H2. Then an experimental section is included that demonstrates controller synthesis for a constructed probabilistic system. The experimental results lend support to the method. / Ph. D.