Design Optimization of an Above-Knee Prosthesis with Energy Regeneration

Above-knee amputees who use a prosthetic leg typically have to compensate for its shortcomings with unnatural hip motions. This compensation eventually leads to adverse health issues such as arthritis. We propose an active prosthesis to improve performance. The motor in our prosthetic knee allows the patient to move his hip normally, thus reducing the possibility of ancillary health issues. To improve the efficiency of the prosthesis, we use the braking phase of the prosthesis to regenerate energy. By storing energy in a supercapacitor during braking, the prosthesis lasts longer between each charge than it would without regenerative braking. We are considering two knee motor designs—a gear drive and a ball screw drive. Both designs appear to have the potential for regeneration. Several parameters characterize the prosthesis design. We use biogeography-based optimization (BBO) to determine these parameters. We are currently optimizing the prosthesis design to achieve accurate tracking of the knee angle. Future optimization criteria will include efficient energy use and generation.https://engagedscholarship.csuohio.edu/u_poster_2013/1009/thumbnail.jp

Analytical and Numerical Comparisons of Biogeography-based Optimization and Genetic Algorithms.

We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems. We also present comparisons between BBO and GA/GUR for combinatorial optimization problems, include the traveling salesman, the graph coloring, and the bin packing problems

Markov Models for Biogeography-based Optimization

Biogeography-based optimization (BBO) is a population-based evolutionary algorithm that is based on the mathematics of biogeography. Biogeography is the science and study of the geographical distribution of biological organisms. In BBO, problem solutions are analogous to islands, and the sharing of features between solutions is analogous to the migration of species. This paper derives Markov models for BBO with selection, migration, and mutation operators. Our models give the theoretically exact limiting probabilities for each possible population distribution for a given problem. We provide simulation results to confirm the Markov models

Design Optimization of an Above-Knee Prosthesis with Energy Regeneration

Above-knee amputees who use a prosthetic leg typically have to compensate for its shortcomings with unnatural hip motions. This compensation eventually leads to adverse health issues such as arthritis. We propose an active prosthesis to improve performance. The motor in our prosthetic knee allows the patient to move his hip normally, thus reducing the possibility of ancillary health issues. To improve the efficiency of the prosthesis, we use the braking phase of the prosthesis to regenerate energy. By storing energy in a supercapacitor during braking, the prosthesis lasts longer between each charge than it would without regenerative braking. We are considering two knee motor designs—a gear drive and a ball screw drive. Both designs appear to have the potential for regeneration. Several parameters characterize the prosthesis design. We use biogeography-based optimization (BBO) to determine these parameters. We are currently optimizing the prosthesis design to achieve accurate tracking of the knee angle. Future optimization criteria will include efficient energy use and generation.https://engagedscholarship.csuohio.edu/u_poster_2013/1009/thumbnail.jp

Analytical and Numerical Comparisons of Biogeography-based Optimization and Genetic Algorithms.

We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems. We also present comparisons between BBO and GA/GUR for combinatorial optimization problems, include the traveling salesman, the graph coloring, and the bin packing problems

Biogeography-based optimization and the solution of the power flow problem

Abstract—Biogeography-based optimization (BBO) is a novel evolutionary algorithm that is based on the mathematics of biogeography. Biogeography is the study of the geographical distribution of biological organisms. In the BBO model, problem solutions are represented as islands, and the sharing of features between solutions is represented as immigration and emigration between the islands. This paper presents an application of the BBO algorithm to the power flow problem for an IEEE 30-bus Test Case system. The BBO solution is compared with the solution of the same problem using a genetic algorithm (GA). The results of Monte Carlo simulations indicate that the BBO algorithm consistently performs better than the GA in determining an optimal solution to the power flow problem

Optimal Design Of A Transfemoral Prosthesis With Energy Storage And Regeneration

We describe the preliminary optimal design of an electromechanical above-knee active prosthesis with energy storage and regeneration. A DC motor-generator applies a positive or negative torque to the knee. The control system regulates the exchange of energy between the motor-generator and a supercapacitor. The central idea of the design is motivated by the mechanics, energy management, and sensor-based control that constitute human movement. We use biogeography-based optimization, which is an evolutionary algorithm, to optimize the system parameters, and we evaluate its performance with Simulink (R) models. We optimize three alternative prosthesis designs. Simulation results indicate that the prosthesis can be optimized to achieve knee angle tracking with an RMS error on the order of 0.2 degrees

La Atalaya : diario de la mañana: Año XVII Número 6297 - 1909 mayo 7

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Optimal Design Of A Transfemoral Prosthesis With Energy Storage And Regeneration

We describe the preliminary optimal design of an electromechanical above-knee active prosthesis with energy storage and regeneration. A DC motor-generator applies a positive or negative torque to the knee. The control system regulates the exchange of energy between the motor-generator and a supercapacitor. The central idea of the design is motivated by the mechanics, energy management, and sensor-based control that constitute human movement. We use biogeography-based optimization, which is an evolutionary algorithm, to optimize the system parameters, and we evaluate its performance with Simulink (R) models. We optimize three alternative prosthesis designs. Simulation results indicate that the prosthesis can be optimized to achieve knee angle tracking with an RMS error on the order of 0.2 degrees