Multigrid Methods for Elliptic Optimal Control Problems

In this dissertation we study multigrid methods for linear-quadratic elliptic distributed optimal control problems. For optimal control problems constrained by general second order elliptic partial differential equations, we design and analyze a $P_1$ finite element method based on a saddle point formulation. We construct a $W$-cycle algorithm for the discrete problem and show that it is uniformly convergent in the energy norm for convex domains. Moreover, the contraction number decays at the optimal rate of $m^{-1}$, where $m$ is the number of smoothing steps. We also prove that the convergence is robust with respect to a regularization parameter. The robust convergence of $V$-cycle and $W$-cycle algorithms on general domains are demonstrated by numerical results. For optimal control problems constrained by symmetric second order elliptic partial differential equations together with pointwise constraints on the state variable, we design and analyze symmetric positive definite $P_1$ finite element methods based on a reformulation of the optimal control problem as a fourth order variational inequality. We develop a multigrid algorithm for the reduced systems that appear in a primal-dual active set method for the discrete variational inequalities. The performance of the algorithm is demonstrated by numerical results

Designed Interaction Potentials via Inverse Methods for Self-Assembly

We formulate statistical-mechanical inverse methods in order to determine optimized interparticle interactions that spontaneously produce target many-particle configurations. Motivated by advances that give experimentalists greater and greater control over colloidal interaction potentials, we propose and discuss two computational algorithms that search for optimal potentials for self-assembly of a given target configuration. The first optimizes the potential near the ground state and the second near the melting point. We begin by applying these techniques to assembling open structures in two dimensions (square and honeycomb lattices) using only circularly symmetric pair interaction potentials ; we demonstrate that the algorithms do indeed cause self-assembly of the target lattice. Our approach is distinguished from previous work in that we consider (i) lattice sums, (ii) mechanical stability (phonon spectra), and (iii) annealed Monte Carlo simulations. We also devise circularly symmetric potentials that yield chain-like structures as well as systems of clusters.Comment: 28 pages, 23 figure

Trajectory Optimization and Guidance Design by Convex Programming

The field of aerospace guidance and control has recently been evolving from focusing on traditional laws and controllers to numerical algorithms with the aim of achieving onboard applications for autonomous vehicle systems. However, it is very difficult to perform complex guidance and control missions with highly nonlinear dynamic systems and many constraints onboard. In recent years, an emerging trend has occurred in the field of Computational Guidance and Control (CG&C). By taking advantage of convex optimization and highly efficient interior point methods, CG&C allows complicated guidance and control problems to be solved in real time and offers great potential for onboard applications. With the significant increase in computational efficiency, convex-optimization-based CG&C is expected to become a fundamental technology for system autonomy and autonomous operations. In this dissertation, successive convex approaches are proposed to solve optimal control programs associated with aerospace guidance and control, and the emphasis is placed on potential onboard applications. First, both fuel-optimal and time-optimal low-thrust orbit transfer problems are investigated by a successive second-order cone programming method. Then, this convex method is extended and improved to solve hypersonic entry trajectory optimization problems by taking advantage of line-search and trust-region techniques. Finally, the successive convex approach is modified to the design of autonomous entry guidance algorithms. Simulation results indicate that the proposed methodologies are capable of generating accurate solutions for low-thrust orbit transfer problems and hypersonic entry problems with fast computational speed. The proposed methods have great potential for onboard applications

A real time sliding mode control for a wave energy converter based on a wells turbine

Due to the nonlinear dynamics and uncertainties usually present in wave energy conversion systems, the efficiency of these devices can be enhanced employing a robust control algorithms. Wave energy converters are constructed using electric generators of variable velocity, like double feed induction generator (DFIG) since they may improve the system efficiency to generate power when compared to fixed speed generators. The main reason is that this generators with variable speed may adapt the speed of the turbine in order to maintain the optimal flow coefficient values which improves the efficiency of the Wells turbine. However, a suitable speed controller is required in these systems first in order to avoid the stalling phenomenon and second in order to track the optimal turbine reference velocity that optimizes the power generation. In this paper a real time sliding mode control scheme for wave energy conversion systems that incorporate a Wells turbine and a DFIG is proposed. The Lyapunov stability theory is used to analyse the stability of this control scheme under parameter uncertainties and system disturbances. Next, the proposed control scheme is validated first by means of some simulation examples using the Matlab/Simulink software and second using a real-time experimental platform based on a dSPACE DS1103 control board.The authors are very grateful to the UPV/EHU by its support through the projects PPGA18/04 and UFI11/07 and to the Basque Government by its support through the project ELKARTEK KK-2017/00033. The authors also would like to thank the anonymous reviewers who have helped to improve the initial version of this paper

Intelligent technologies for real-time biomedical engineering applications

Intelligent technologies are essential for many biomedical engineering applications in order to cope with a wide variety of patient conditions or user disability. The development of advanced optimisation training algorithms such as adaptive optimal Bayesian neural networks is particularly useful when only limited training data are available. Two specific biomedical engineering applications will be presented. The first application concerns the development of a non-invasive monitor for real-time detection of hypoglycaemic episodes in Type 1 diabetes mellitus patients (T1DM). The second application relates to the development of real-time hands-free wheelchair control systems using head movement to provide mobility independence for severely disabled people. Copyright © 2008, Inderscience Publishers

Trajectory Control and Optimization for Responsive Spacecraft

The concept of responsive space has been gaining interest, and growing to include systems that can be re-tasked to complete multiple missions within their lifetime. The purpose of this study is to develop an algorithm that produces a maneuver trajectory that will cause a spacecraft to arrive at a particular location within its orbit earlier than expected. The time difference, delta t, is used as a metric to quantify the effects of the maneuver. Two separate algorithms are developed. The first algorithm is an optimal control method and is developed through Optimal Control Theory. The second algorithm is a feedback control method and is developed through Lyapunov Theory. It is shown that the two algorithms produce equivalent results for the maneuvers discussed. In-plane maneuver results are analyzed analytically, and an algebraic expression for delta t is derived. Examples are provided of how the analytic expression can be used for mission planning purposes. The feedback control algorithm is then further developed to demonstrate the simplicity of implementing additional capabilities. Finally, a set of simulations is analyzed to show that in order to maximize the amount of delta t achieved, a spacecraft must be allowed as much lead time as possible, and begin thrusting as early as possible

Trajectory Control and Optimization for Responsive Spacecraft

The concept of responsive space has been gaining interest, and growing to include systems that can be re-tasked to complete multiple missions within their lifetime. The purpose of this study is to develop an algorithm that produces a maneuver trajectory that will cause a spacecraft to arrive at a particular location within its orbit earlier than expected. The time difference, delta t, is used as a metric to quantify the effects of the maneuver. Two separate algorithms are developed. The first algorithm is an optimal control method and is developed through Optimal Control Theory. The second algorithm is a feedback control method and is developed through Lyapunov Theory. It is shown that the two algorithms produce equivalent results for the maneuvers discussed. In-plane maneuver results are analyzed analytically, and an algebraic expression for delta t is derived. Examples are provided of how the analytic expression can be used for mission planning purposes. The feedback control algorithm is then further developed to demonstrate the simplicity of implementing additional capabilities. Finally, a set of simulations is analyzed to show that in order to maximize the amount of delta t achieved, a spacecraft must be allowed as much lead time as possible, and begin thrusting as early as possible

Analysis & Synthesis of Distributed Control Systems with Sparse Interconnection Topologies

This dissertation is about control, identification, and analysis of systems with sparse interconnection topologies. We address two main research objectives relating to sparsity in control systems and networks. The first problem is optimal sparse controller synthesis, and the second one is the identification of sparse network. The first part of this dissertation starts with the chapter focusing on developing theoretical frameworks for the synthesis of optimal sparse output feedback controllers under pre-specified structural constraints. This is achieved by establishing a balance between the stability of the controller and the systems quadratic performance. Our approach is mainly based on converting the problem into rank constrained optimizations.We then propose a new approach in the syntheses of sparse controllers by em- ploying the concept of Hp approximations. Considering the trade-off between the controller sparsity and the performance deterioration due to the sparsification pro- cess, we propose solving methodologies in order to obtain robust sparse controllers when the system is subject to parametric uncertainties.Next, we pivot our attention to a less-studied notion of sparsity, namely row sparsity, in our optimal controller design. Combining the concepts from the majorization theory and our proposed rank constrained formulation, we propose an exact reformulation of the optimal state feedback controllers with strict row sparsity constraint, which can be sub-optimally solved by our proposed iterative optimization techniques. The second part of this dissertation focuses on developing a theoretical framework and algorithms to derive linear ordinary differential equation models of gene regulatory networks using literature curated data and micro-array data. We propose several algorithms to derive stable sparse network matrices. A thorough comparison of our algorithms with the existing methods are also presented by applying them to both synthetic and experimental data-sets