Abstract: The inverted pendulum system is a classic control
problem that is used in universities around the world. It is a suitable
process to test prototype controllers due to its high non-linearities and
lack of stability. The inverted pendulum represents a challenging
control problem, which continually moves toward an uncontrolled
state. This paper presents the possibility of balancing an inverted
pendulum system using sliding mode control (SMC). The goal is to
determine which control strategy delivers better performance with
respect to pendulum’s angle and cart's position. Therefore,
proportional-integral-derivative (PID) is used for comparison. Results
have proven SMC control produced better response compared to PID
control in both normal and noisy systems.
Abstract: This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure.