Term of Award
Master of Science in Applied Engineering (M.S.A.E.)
Document Type and Release Option
Thesis (restricted to Georgia Southern)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mechanical Engineering
Committee Member 1
Committee Member 2
Committee Member 3
This work documents a study of intelligent neural network control system design and implementation for engineering applications. In this study, the effectiveness of single multiplicative neuron (SMN) in place of traditional multi-layer perceptron (MLP) is investigated. The objectives were to (i) verify the feasibility of SMN based control systems, (ii) quantitatively compare the performance of SMN and MLP based systems, (iii) determine the amount of computation that could be saved by using SMN instead of MLP in a control system, and (iv) determine the performance of a SMN in an adaptive critic design (ACD) using action dependent heuristic dynamic programming (ADHDP). It was hypothesized that the replacement of a MLP network with a SMN would result in a controller that would achieve the same control quality in a less processor intensive manner, possibly allowing controller implementation on a less costly computer or microcontroller system. Controllers featuring the MLP and the SMN were implemented in LabVIEW for two physical systems and compared based on their ability to accurately control the system response when given complex reference inputs. The SMN based control systems were implemented with both off-line and on-line training using conventional and field programmable gate array (FPGA) based data acquisition hardware. The controllers were also compared based on the number of calculations required to complete the artificial neural network (ANN) related sections of the control loop. The SMN based control systems were found to perform as well as, if not better than their MLP based counterparts, in all cases studied, while significantly reducing required computations. The SMN was finally implemented in an intelligent controller based on ADHDP and found to perform better than conventional controllers like PID with a periodic disturbance.
Turner, Jonathan Gregory, "Intelligent Neural Network Control System Design and FPGA Based Implementation" (2012). Electronic Theses and Dissertations. 778.
Research Data and Supplementary Material