A Motorized Adjustable Vertical Platform (MAVeP) is needed by National Space Agency of Malaysia (also known as ANGKASA) at their Satellite Assembly, Integration and Test Centre (AITC), Banting, Selangor. AITC is a clean room used for satellite assessment before it is qualified to be launched into the orbit. Designed as the facility that will provide the similar testing condition as the spacecraft and its payload, AITC is a controlled environment area which having a clean room of class ISO 8. Since it is a confined space for testing, a satellite is carefully transported within the test area as it is very sensitive. Therefore, mecanum wheels are the most suitable wheel for MAVeP mobility mechanism. However, the mecanum wheels come with their common issue like slippage that leads to low accuracy and repeatability of the movement. Each mecanum wheel motor which act as an actuator needs to be properly controlled to avoid overshoot that cause jerk and oscillation that leads to vibration. MAVeP mobility need to transport the satellite with minimum vibration and jerk also achieve positional accuracy and repeatability. This research focuses on the development of dual loop feedback error learning controller with nonlinear autoregressive exogenous neural network (NARX-NN) for MAVeP mobility mechanism. A MAVeP mobility mechanism prototype has been developed and the kinematic model has been derived. Simulations and experiments have been conducted in the linear and diagonal axis. The vibration and jerk issues have been overcame by dual closed loop positioning control system while the slippage problem have been eliminated and improved by using feedback error learning (FEL) control technique which is a dynamic inverse control method that combines simultaneous action of proportional (P) as a feedback controller and NARX as the feedforward controller. NARX learns the inverse dynamic of the MAVeP mobility mechanism in the feedforward controller to improve the response of non-adaptive feedback controller performed by P controller. The experimental result shows that the steady state tracking error is 5%, the maximum overshoot is 0%, the settling time are between 2.9 second to 3.0 second, the RMSE are between 1.83 cm to 2.01 cm and the repeatability are between 98.3% to 100% for all linear movement. Both simulation and experimental results have proven that the proposed controller is successful in controlling the MAVeP mobility mechanism to achieve the desired position accurately and eliminate slippage and jerking.

The daily cost of rail operation is increasing due to the increase in rail network size. Therefore, reducing operation power consumption is important for rail operators to optimize daily operation costs. Operation power consumption in rail operation can be divided into 80% traction power, such as vehicle propulsion system, and 20% non-traction power, such as station electrical consumption. Many research has already been carried out on advanced traction systems, such as regenerative braking storage systems, hybrid batteries, and others, which aim to reduce operation power consumption. However, very few addresses the concern about track condition and maintenance. This research focuses on the interaction between the rail vehicle wheels and the rail track and how the coefficient of friction affects the rail operation power consumption. Train resistance can be expressed in the Davis equation, which is the mathematical model used in rail industries to find train performance, therefore Davis equation is use in the research to determining the operation power consumption of variable track kinetic coefficient of friction. As rail grinding is an essential rail preventive maintenance to improve track surface, indirectly coefficient of friction has shown an improvement. Our findings indicate that the application of the rail grinding method had successfully reduced the track kinetic coefficient of friction from µ = 0.5 to 0.3, therefore reducing 9% in operation power consumption. The reduction of the track kinetic coefficient of friction has led to a decrease in train running resistance, consequently resulting in a reduction in operation power consumption.

Electric cars play a clear and important role in solving issues related to the phenomenon of global warming, because when they operate, they do not emit any emissions that pollute the environment, and the electrical network can also be used to organize its work. However, there are still significant problems with electric cars that need to be fixed. The main challenges are all related to the battery package of the car. The battery package should contain enough energy in order to have a certain driving range and power capability. The first step in creating a decent electric vehicle model is choosing the right parameters and comprehending their properties. In this research, the electric vehicles are modelled. Three vehicle model is simulated with three different drive cycles. The simulation result demonstrates the significance of each segment parameter to the performance and fuel economy of electric vehicles. All works are performed in MATLAB/Simulink environment.