PID Optimization by Genetic Algorithm or any intelligent optimization method is widely being used recently. The main issue is to select a suitable objective function based on error criteria. Original error criteria that is widely being used such as ITAE, ISE, ITSE and IAE is insufficient in enhancing some of the performance parameter. Parameter such as settling time, rise time, percentage of overshoot, and steady state error is included in the objective function. Weightage is added into these parameters based on users’ performance requirement. Based on the results, modified error criteria show improvement in all performance parameter after being modified. All of the error criteria produce 0% overshoot, 29.51%-39.44% shorter rise time, 21.11%-42.98% better settling time, 10% to 53.76% reduction in steady state error. The performance of modified objective function in minimizing the error signal is reduced. It can be concluded that modification of objective function by adding performance parameter into consideration could improve the performance of rise time, settling time, overshoot percentage, and steady state error
This paper describes a real-time vehicle location tracking system which uses the current technology of Global Positioning System (NAVSTAR GPS) to provide continuous position and velocity tracking of a moving vehicle. This system consists of two partsthe hardware GPS tracking unit and the front-end user-friendly mapping software. The hardware-tracking unit will provide a common interface to the computer running the mapping software developed under Microsoft Windows. Eventually, the entire system will be compact and portable enough to be mounted in the vehicle for positionhelocity tracking and hence the display of tracking information on the mapping software showing the Singapore roadmaps.
An artificial neural network (ANN) computing system can be significantly influenced by its implementation type. The software implemented ANN can produce high accuracy output with slow computation time performance compared to hardware implemented ANN which runs at a faster computation time but with low accuracy. Normally, software implementation reduces the proficiency and efficiency of the model. Robot performance plays an important role as it needs fast response to process information that is applied with ANN. As a consequence, the proposed research focuses on comparison between hardware and software implementation multilayer perceptron (MLP) for cart follower in Field Programmable Logic Array (FPGA). Both of the software and hardware models produced the same precision where the output distance at angles-10°, 0° and 10° shows same percentage error. Besides that, both of the models have similar root mean square error (RMSE) which are 0.469, 0.479 and 0.267 at-10°, 0° and 10° respectively. The processing time of MLP model implemented in hardware and software are at 1.91μs and 78.06μs respectively. Thus, it can be concluded that hardware implementation is better than software implementation.
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