System Architecture Optimization Using Hidden Genes Genetic Algorithms with Applications in Space Trajectory Optimization

In this dissertation, the concept of hidden genes genetic algorithms is developed. In system architecture optimization problems, the topology of the solution is unknown and hence, the number of design variables is variable. Hidden genes genetic algorithms are genetic algorithm based methods that are developed to handle such problems by hiding some genes in the chromosomes. The genes in the hidden genes genetic algorithms evolve through selection, mutation, and crossover operations. To determine if a gene is hidden or not, binary tags are assigned to them. The value of the tags determine the status of the genes. Different mechanisms are proposed for the evolution of the tags. Some mechanisms utilize stochastic operations while others are based on deterministic operations. All the proposed mechanisms are tested on mathematical and space trajectory optimization problems. Moreover, Markov chain models of the mechanisms are derived and their convergence is investigated analytically. The results show that the proposed concept are capable to search for the optimal solution by autonomously enabling the algorithms to assign the hidden genes

A Hamiltonian Surface-Shaping approach for control system analysis and the design of nonlinear wave energy converters

The dynamic model of Wave Energy Converters (WECs) may have nonlinearities due to several reasons such as a nonuniform buoy shape and/or nonlinear power takeoff units. This paper presents the Hamiltonian Surface-Shaping (HSS) approach as a tool for the analysis and design of nonlinear control of WECs. The Hamiltonian represents the stored energy in the system and can be constructed as a function of the WEC’s system states, its position, and velocity. The Hamiltonian surface is defined by the energy storage, while the system trajectories are constrained to this surface and determined by the power flows of the applied non-conservative forces. The HSS approach presented in this paper can be used as a tool for the design of nonlinear control systems that are guaranteed to be stable. The optimality of the obtained solutions is not addressed in this paper. The case studies presented here cover regular and irregular waves and demonstrate that a nonlinear control system can result in a multiple fold increase in the harvested energy

Optimization of nonlinear wave energy converters

© 2018 Elsevier Ltd This paper presents an optimization approach for the nonlinear control of wave energy converters (WECs). The proposed optimization method also presents the option of optimizing the system nonlinearities, such as those due to the buoy shape, such that the harvested energy is maximized. For the sake of control design, the control force and the system optimizable nonlinear force, each is expressed as a truncated power series function of the system states. The power series coefficients in both the control and system forces are optimized. A hidden genes genetic algorithm is used for optimization. The optimized system\u27s nonlinear force is assumed to drive the design of the WEC. The numerical test cases presented in this paper show that it is possible to attain multiple fold higher harvested energy when using nonlinear control optimization. The advantage of being able to optimize the WEC design simultaneously with the control is the potential of harvesting this multiple fold higher energy without causing large WEC motion and with less dependence on reactive power. While this paper focuses on the optimization part of the problem, the implementation of the obtained control in realtime is discussed at the end of the paper

Evolving hidden genes in genetic algorithms for systems architecture optimization

The concept of hidden genes was recently introduced in genetic algorithms (GAs) to handle systems architecture optimization problems, where the number of design variables is variable. Selecting the hidden genes in a chromosome determines the architecture of the solution. This paper presents two categories of mechanisms for selecting (assigning) the hidden genes in the chromosomes of GAs. These mechanisms dictate how the chromosome evolves in the presence of hidden genes. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not. In the first category of mechanisms, the tags evolve using stochastic operations. Eight different variations in this category are proposed and compared through numerical testing. The second category introduces logical operations for tags evolution. Both categories are tested on the problem of interplanetary trajectory optimization for a space mission to Jupiter, as well as on mathematical optimization problems. Several numerical experiments were designed and conducted to optimize the selection of the hidden genes algorithm parameters. The numerical results presented in this paper demonstrate that the proposed concept of tags and the assignment mechanisms enable the hidden genes genetic algorithms (HGGA) to find better solutions

Convergence analysis of hidden genes genetic algorithms in space trajectory optimization

This study presents a convergence analysis that proves hidden genes genetic algorithms (HGGAs) generate a sequence of solutions with the limit value of the global optima. For an analytical proof, the homogeneous finite Markov models of different mechanisms are derived, and the convergence of the HGGAs with tag evolution mechanisms are investigated. The optimization problem is considered a maximization problem with strictly positive values for the objective function. The stochastic dependency between successive populations is created by applying selection, mutation, and crossover operators to the current population to produce the next population. The GA is a stochastic process in which the state of each population only depends on the state of the immediate predecessor population

A Hamiltonian Surface-Shaping Approach for Control System Analysis and the Design of Nonlinear Wave Energy Converters

The dynamic model of Wave Energy Converters (WECs) may have nonlinearities due to several reasons such as a nonuniform buoy shape and/or nonlinear power takeoff units. This paper presents the Hamiltonian Surface-Shaping (HSS) approach as a tool for the analysis and design of nonlinear control of WECs. The Hamiltonian represents the stored energy in the system and can be constructed as a function of the WEC’s system states, its position, and velocity. The Hamiltonian surface is defined by the energy storage, while the system trajectories are constrained to this surface and determined by the power flows of the applied non-conservative forces. The HSS approach presented in this paper can be used as a tool for the design of nonlinear control systems that are guaranteed to be stable. The optimality of the obtained solutions is not addressed in this paper. The case studies presented here cover regular and irregular waves and demonstrate that a nonlinear control system can result in a multiple fold increase in the harvested energy

Optimal positioning of energy assets in autonomous robotic microgrids for power restoration

As the number of natural disasters and the duration of their aftermath continues to rise, the use of mobile and autonomous energy sources formed into a temporary and adaptive microgrid will improve response time and decrease overall recovery time. The optimal positioning of energy resources in the operating field is critical. There are many options for choosing an optimization technique but many of these assume specific connections between voltage source nodes and loads. This paper presents a brief overview of the genetic algorithm optimization in microgrid resource positioning in an operating field with obstacles. The indexing of the nodes and loads and the formulation for optimization of a general model of a microgrid system are presented. Next, the optimization of microgrids using the genetic algorithm approach is explained, as well as the shortest path algorithm used. The results show the success of optimization applied to a variety of test cases. Further research and expansions of optimization in the test cases are then explained