D.Phil. (Electrical and Electronic Engineering) / The complexity of multi-objective functions and diverse variables involved with optimization of parallel manipulator or parallel kinematic machine design has inspired the research conducted in this thesis to investigate techniques that are suitable to tackle this problem efficiently. Further the parallel manipulator dimensional synthesis problem is multimodal and has no explicit analytical expressions. This process requires optimization techniques which offer high level of accuracy and robustness. The goal of this work is to present method(s) based on Artificial Intelligence (AI) that may be applied in addressing the challenge stated above. The performance criteria considered include; stiffness, dexterity and workspace. The case studied in this work is a 6 degrees of freedom (DOF) parallel manipulator, particularly because it is considered much more complicated than the lesser DOF mechanisms, owing to the number of independent parameters or inputs needed to specify its configuration (i.e. the higher the DOFs, the more the number of independent variables to be considered). The first contribution in this thesis is a comparative study of several hybrid Multi- Objective Optimization (MOO) AI algorithms, in application of a parallel manipulator dimensional synthesis. Artificial neural networks are utilized to approximate a multiple function for the analytical solution of the 6 DOF parallel manipulator’s performance indices, followed by implementation of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as search algorithms. Further two hybrid techniques are proposed which implement Simulated Annealing and Random Forest in searching for optimum solutions in the Multi-objective Optimization problem. The final contribution in this thesis is ensemble machine learning algorithms for approximation of a multiple objective function for the 6 DOF parallel manipulator analytical solution. The results from the experiments demonstrated not only neural network (NN) but also other machine learning algorithms namely K- Nearest Neighbour (k-NN), M5 Prime (M5’), Zero R (ZR) and Decision Stump (DS) can effectively be implemented for the application of function approximation.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:13311 |
| Date | 12 February 2015 |
| Creators | Modungwa, Dithoto |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Johannesburg |
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