Nnamdi Okaeme

Hybrid Bacterial Foraging Optimization Strategy for Automated Experimental Control Design in Electrical Drives

This paper explores the automated experimental control design for variable speed drives using a novel heuristic optimization algorithm. A hybrid approach, which combines desirable characteristics of two of the most widely used biologically-inspired heuristic algorithms, the genetic algorithms (GAs) and the bacterial foraging (BF) algorithms, is studied and developed in this paper. Both the structures and parameters of digital speed controllers are optimized experimentally and directly on the drive while it is subject to different types of mechanical load; the dynamics of these load profiles are generated using a programmable load emulator. The proposed hybrid bacterial foraging (HBF) algorithm is evaluated, for the purpose of control optimization for electric drives, against GA and BF, and their performances are compared and contrasted.

Robust Control Design through Experimental Load Identification for Variable Speed Drives

A robust method for speed control design in variable speed drives based on experimental plant model identification is presented in this paper. Genetic algorithms are employed for both experimentally identifying the nominal mechanical system parameters and to optimize suitable controllers on an accurate simulation of the experimental prototype using the estimated plant parameters and with a robustness constraint based on the same identified mechanical system model. Structure and parameters of robust speed controllers have been designed for a permanent magnet DC variable speed drive subject to variable mechanical loads. Experimental tests are presented to validate the proposed design procedure. The method provides a fully automated commissioning procedure for servo and high performance drives and offers significant improvements to current industrial drive systems.

Automated Online Design of Robust Speed Digital Controllers For Variable Speed Drives

This paper addresses the online robust control design for variable speed drives under varying load conditions, using genetic algorithms (GA). Both the structures and parameters of suitable digital speed controllers are optimized online experimentally on the drive while being subject to varying mechanical loads, testing out a population of solutions and, by means of genetic operators, evolving to the optimum solution. The dynamic of different load profiles is generated using a programmable load emulator. Experimental results of the online optimization of the controller are presented and computer simulations are shown for the purpose of comparison

Continuous, non-linear, optimal speed control of a Distributed Generation Power Pack using Artificial Neural Networks

Distributed Generation Power Packs with a combustion engine prime mover are still widely used to supply electric power in a variety of applications. These applications range from backup power supply systems to providing power in places where grid connection is either technically impractical or financially uneconomic. Due to the ever increasing cost of diesel fuel and the environmental issues associated with its use, the optimisation of these AC generators and the reduction of fuel consumption is vital. This paper presents how Artificial Neural Networks can be utilised in order to obtain a continuous function which relates variable load demand to optimal speed demand. The Artificial Neural Network toolbox within MATLAB is used to create, train and test the Artificial Neural Networks. This paper also shows the results of an experimental system used in order to emulate the Distributed Generation Power Pack. Overall it is shown that is possible to operate a variable speed system under optimal, non-linear, speed control using Artificial Neural Networks.